Weather explains the decline and rise of insect biomass over 34 years.

Insects have a pivotal role in ecosystem function, thus the decline of more than 75% in insect biomass in protected areas over recent decades in Central Europe1 and elsewhere2,3 has alarmed the public, pushed decision-makers4 and stimulated research on insect population trends. However, the drivers of this decline are still not well understood. Here, we reanalysed 27 years of insect biomass data from Hallmann et al.1, using sample-specific information on weather conditions during sampling and weather anomalies during the insect life cycle. This model explained variation in temporal decline in insect biomass, including an observed increase in biomass in recent years, solely on the basis of these weather variables. Our finding that terrestrial insect biomass is largely driven by complex weather conditions challenges previous assumptions that climate change is more critical in the tropics5,6 or that negative consequences in the temperate zone might only occur in the future7. Despite the recent observed increase in biomass, new combinations of unfavourable multi-annual weather conditions might be expected to further threaten insect populations under continuing climate change. Our findings also highlight the need for more climate change research on physiological mechanisms affected by annual weather conditions and anomalies.

Anthropogenic fingerprints in daily precipitation revealed by deep learning.

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1-4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations6. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.

Forced changes in the Pacific Walker circulation over the past millennium.

The Pacific Walker circulation (PWC) has an outsized influence on weather and climate worldwide. Yet the PWC response to external forcings is unclear1,2, with empirical data and model simulations often disagreeing on the magnitude and sign of these responses3. Most climate models predict that the PWC will ultimately weaken in response to global warming4. However, the PWC strengthened from 1992 to 2011, suggesting a significant role for anthropogenic and/or volcanic aerosol forcing5, or internal variability. Here we use a new annually resolved, multi-method, palaeoproxy-derived PWC reconstruction ensemble (1200-2000) to show that the 1992-2011 PWC strengthening is anomalous but not unprecedented in the context of the past 800 years. The 1992-2011 PWC strengthening was unlikely to have been a consequence of volcanic forcing and may therefore have resulted from anthropogenic aerosol forcing or natural variability. We find no significant industrial-era (1850-2000) PWC trend, contrasting the PWC weakening simulated by most climate models3. However, an industrial-era shift to lower-frequency variability suggests a subtle anthropogenic influence. The reconstruction also suggests that volcanic eruptions trigger El Niño-like PWC weakening, similar to the response simulated by climate models.

Increased heat risk in wet climate induced by urban humid heat.

Cities are generally warmer than their adjacent rural land, a phenomenon known as the urban heat island (UHI). Often accompanying the UHI effect is another phenomenon called the urban dry island (UDI), whereby the humidity of urban land is lower than that of the surrounding rural land1-3. The UHI exacerbates heat stress on urban residents4,5, whereas the UDI may instead provide relief because the human body can cope with hot conditions better at lower humidity through perspiration6,7. The relative balance between the UHI and the UDI-as measured by changes in the wet-bulb temperature (Tw)-is a key yet largely unknown determinant of human heat stress in urban climates. Here we show that Tw is reduced in cities in dry and moderately wet climates, where the UDI more than offsets the UHI, but increased in wet climates (summer precipitation of more than 570 millimetres). Our results arise from analysis of urban and rural weather station data across the world and calculations with an urban climate model. In wet climates, the urban daytime Tw is 0.17 ± 0.14 degrees Celsius (mean ± 1 standard deviation) higher than rural Tw in the summer, primarily because of a weaker dynamic mixing in urban air. This Tw increment is small, but because of the high background Tw in wet climates, it is enough to cause two to six extra dangerous heat-stress days per summer for urban residents under current climate conditions. The risk of extreme humid heat is projected to increase in the future, and these urban effects may further amplify the risk.

Warming weakens the night-time barrier to global fire.

Night-time provides a critical window for slowing or extinguishing fires owing to the lower temperature and the lower vapour pressure deficit (VPD). However, fire danger is most often assessed based on daytime conditions1,2, capturing what promotes fire spread rather than what impedes fire. Although it is well appreciated that changing daytime weather conditions are exacerbating fire, potential changes in night-time conditions-and their associated role as fire reducers-are less understood. Here we show that night-time fire intensity has increased, which is linked to hotter and drier nights. Our findings are based on global satellite observations of daytime and night-time fire detections and corresponding hourly climate data, from which we determine landcover-specific thresholds of VPD (VPDt), below which fire detections are very rare (less than 95 per cent modelled chance). Globally, daily minimum VPD increased by 25 per cent from 1979 to 2020. Across burnable lands, the annual number of flammable night-time hours-when VPD exceeds VPDt-increased by 110 hours, allowing five additional nights when flammability never ceases. Across nearly one-fifth of burnable lands, flammable nights increased by at least one week across this period. Globally, night fires have become 7.2 per cent more intense from 2003 to 2020, measured via a satellite record. These results reinforce the lack of night-time relief that wildfire suppression teams have experienced in recent years. We expect that continued night-time warming owing to anthropogenic climate change will promote more intense, longer-lasting and larger fires.

Thinking clearly about social aspects of infectious disease transmission.

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

Concurrent Asian monsoon strengthening and early modern human dispersal to East Asia during the last interglacial.

The relationship between initial Homo sapiens dispersal from Africa to East Asia and the orbitally paced evolution of the Asian summer monsoon (ASM)-currently the largest monsoon system-remains underexplored due to lack of coordinated synthesis of both Asian paleoanthropological and paleoclimatic data. Here, we investigate orbital-scale ASM dynamics during the last 280 thousand years (kyr) and their likely influences on early H. sapiens dispersal to East Asia, through a unique integration of i) new centennial-resolution ASM records from the Chinese Loess Plateau, ii) model-based East Asian hydroclimatic reconstructions, iii) paleoanthropological data compilations, and iv) global H. sapiens habitat suitability simulations. Our combined proxy- and model-based reconstructions suggest that ASM precipitation responded to a combination of Northern Hemisphere ice volume, greenhouse gas, and regional summer insolation forcing, with cooccurring primary orbital cycles of ~100-kyr, 41-kyr, and ~20-kyr. Between ~125 and 70 kyr ago, summer monsoon rains and temperatures increased in vast areas across Asia. This episode coincides with the earliest H. sapiens fossil occurrence at multiple localities in East Asia. Following the transcontinental increase in simulated habitat suitability, we suggest that ASM strengthening together with Southeast African climate deterioration may have promoted the initial H. sapiens dispersal from their African homeland to remote East Asia during the last interglacial.

Positive effects of public breeding on US rice yields under future climate scenarios.

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. rice-growing region. This region spans the states of Arkansas, Louisiana, Texas, Mississippi, and Missouri and accounts for 85% of total U.S. rice production. By digitizing and combining four decades of county-level variety acreage data (1970 to 2015) with varietal information from genotyping-by-sequencing data, we estimate annual historical county-level allele frequencies. These allele frequencies are used together with county-level weather and yield data to develop ten machine learning models for yield prediction. A two-layer meta-learner ensemble model that combines all ten methods is externally evaluated against observations from historical Uniform Regional Rice Nursery trials (1980 to 2018) conducted in the same states. Finally, the ensemble model is used with forecasted weather from the Coupled Model Intercomparison Project across the 110 rice-growing counties to predict production in the coming decades for Composite Variety Groups assembled based on year of release, breeding program, and several breeding trends. Results indicate positive effects over time of public breeding on rice resilience to future climates, and potential reasons are discussed.

Weather deviations linked to undocumented migration and return between Mexico and the United States.

As the world's climate continues to change, human populations are exposed to increasingly severe and extreme weather conditions that can promote migration. Here, we examine how extreme weather influences the likelihood of undocumented migration and return between Mexico and the United States. We used data from 48,313 individuals observed between 1992 and 2018 in 84 Mexican agricultural communities. While controlling for regional and temporal confounding factors, we related individual decisions to migrate to the United States without documents and subsequently return to Mexico with lagged weather deviations from the historical norm during the corn-growing season (May to August). Undocumented migration was most likely from areas experiencing extreme drought, and migrants were less likely to return to their communities of origin when extreme weather persisted. These findings establish the role of weather shocks in undocumented Mexican migration to, and eventual settlement in, the United States. The findings also suggest that extreme weather conditions, which are likely to increase with climate change, promote clandestine mobility across borders and, thus, expose migrants to risks associated with crossing dangerous terrain and relying upon smugglers.

Space weather disrupts nocturnal bird migration.

Space weather, including solar storms, can impact Earth by disturbing the geomagnetic field. Despite the known dependence of birds and other animals on geomagnetic cues for successful seasonal migrations, the potential effects of space weather on organisms that use Earth's magnetic field for navigation have received little study. We tested whether space weather geomagnetic disturbances are associated with disruptions to bird migration at a macroecological scale. We leveraged long-term radar data to characterize the nightly migration dynamics of the nocturnally migrating North American avifauna over 22 y. We then used concurrent magnetometer data to develop a local magnetic disturbance index associated with each radar station (ΔBmax), facilitating spatiotemporally explicit analyses of the relationship between migration and geomagnetic disturbance. After controlling for effects of atmospheric weather and spatiotemporal patterns, we found a 9 to 17% decrease in migration intensity in both spring and fall during severe space weather events. During fall migration, we also found evidence for decreases in effort flying against the wind, which may represent a depression of active navigation such that birds drift more with the wind during geomagnetic disturbances. Effort flying against the wind in the fall was most reduced under both overcast conditions and high geomagnetic disturbance, suggesting that a combination of obscured celestial cues and magnetic disturbance may disrupt navigation. Collectively, our results provide evidence for community-wide avifaunal responses to geomagnetic disturbances driven by space weather during nocturnal migration.

A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.

Twofold expansion of the Indo-Pacific warm pool warps the MJO life cycle.

The Madden-Julian Oscillation (MJO) is the most dominant mode of subseasonal variability in the tropics, characterized by an eastward-moving band of rain clouds. The MJO modulates the El Niño Southern Oscillation1, tropical cyclones2,3 and the monsoons4-10, and contributes to severe weather events over Asia, Australia, Africa, Europe and the Americas. MJO events travel a distance of 12,000-20,000 km across the tropical oceans, covering a region that has been warming during the twentieth and early twenty-first centuries in response to increased anthropogenic emissions of greenhouse gases11, and is projected to warm further. However, the impact of this warming on the MJO life cycle is largely unknown. Here we show that rapid warming over the tropical oceans during 1981-2018 has warped the MJO life cycle, with its residence time decreasing over the Indian Ocean by 3-4 days, and increasing over the Indo-Pacific Maritime Continent by 5-6 days. We find that these changes in the MJO life cycle are associated with a twofold expansion of the Indo-Pacific warm pool, the largest expanse of the warmest ocean temperatures on Earth. The warm pool has been expanding on average by 2.3 × 105 km2 (the size of Washington State) per year during 1900-2018 and at an accelerated average rate of 4 × 105 km2 (the size of California) per year during 1981-2018. The changes in the Indo-Pacific warm pool and the MJO are related to increased rainfall over southeast Asia, northern Australia, Southwest Africa and the Amazon, and drying over the west coast of the United States and Ecuador.

Deep learning and process understanding for data-driven Earth system science.

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

Notable impact of wildfires in the western United States on weather hazards in the central United States.

Increased wildfire events constitute a significant threat to life and property in the United States. Wildfire impact on severe storms and weather hazards is another pathway that threatens society, and our understanding of which is very limited. Here, we use unique modeling developments to explore the effects of wildfires in the western US (mainly California and Oregon) on precipitation and hail in the central US. We find that the western US wildfires notably increase the occurrences of heavy precipitation rates by 38% and significant severe hail (≥2 in.) by 34% in the central United States. Both heat and aerosols from wildfires play an important role. By enhancing surface high pressure and increasing westerly and southwesterly winds, wildfires in the western United States produce (1) stronger moisture and aerosol transport to the central United States and (2) larger wind shear and storm-relative helicity in the central United States. Both the meteorological environment more conducive to severe convective storms and increased aerosols contribute to the enhancements of heavy precipitation rates and large hail. Moreover, the local wildfires in the central US also enhance the severity of storms, but their impact is notably smaller than the impact of remote wildfires in California and Oregon because of the lessened severity of the local wildfires. As wildfires are projected to be more frequent and severe in a warmer climate, the influence of wildfires on severe weather in downwind regions may become increasingly important.

Trends of extreme US weather events in the changing climate.

Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from "noisy" weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in [Formula: see text] geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVT model with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y high-temperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months.

Light and flow regimes regulate the metabolism of rivers.

Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial counterparts, most river ecosystems respire far more carbon than they fix and have less pronounced and consistent seasonality in their metabolic rates. We find that variation in annual solar energy inputs and stability of flows are the primary drivers of GPP and ER across rivers. A classification schema based on these drivers advances river science and informs management.

The Antarctic ozone hole and the pattern effect on climate sensitivity.

Since about 1980, the tropical Pacific has been anomalously cold, while the broader tropics have warmed. This has caused anomalous weather in midlatitudes as well as a reduction in the apparent sensitivity of the climate associated with enhanced low-cloud abundance over the cooler waters of the eastern tropical Pacific. Recent modeling work has shown that cooler temperatures over the Southern Ocean around Antarctica can lead to cooler temperatures over the eastern tropical Pacific. Here we suggest that surface wind anomalies associated with the Antarctic ozone hole can cause cooler temperatures over the Southern Ocean that extend into the tropics. We use the short-term variability of the Southern Annular Mode of zonal wind variability to show an association between surface zonal wind variations over the Southern Ocean, cooling over the Southern Ocean, and cooling in the eastern tropical Pacific. This suggests that the cooling of the eastern tropical Pacific may be associated with the onset of the Antarctic ozone hole.

Seasonality, density dependence, and spatial population synchrony.

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.