Exploring oak processionary caterpillar induced lepidopterism (part 2): ex vivo bio-assays unmask the role of TRPV1

As human skin comes into contact with the tiny hairs or setae of the oak processionary caterpillar, Thaumetopoea processionea, a silent yet intense chemical confrontation occurs. The result is a mix of issues: skin rashes and an intense itching that typically lasts days and weeks after the contact. This discomfort poses a significant health threat not only to humans but also to animals. In Western Europe, the alarming increase in outbreaks extends beyond areas near infested trees due to the dispersion of the setae. Predictions indicate a sustained rise in outbreaks, fueled by global changes favoring the caterpillar's survival and distribution. Currently, the absence of an efficient treatment persists due to significant gaps in our comprehension of the pathophysiology associated with this envenomation. Here, we explored the interaction between the venom extract derived from the setae of T. processionea and voltage- and ligand-gated ion channels and receptors. By conducting electrophysiological analyses, we discovered ex vivo evidence highlighting the significant role of TPTX1-Tp1, a peptide toxin from T. processionea, in modulating TRPV1. TPTX1-Tp1 is a secapin-like peptide and demonstrates a unique ability to modulate TRPV1 channels in the presence of capsaicin, leading to cell depolarization, itch and inflammatory responses. This discovery opens new avenues for developing a topical medication, suggesting the incorporation of a TRPV1 blocker as a potential solution for the local effects caused by T. processionea.

Anthropogenic influence of temperature changes across East Asia using CMIP6 simulations

The present study explores the impact of anthropogenic forcings (ANT) on surface air temperatures (SATs) across East Asia (EA) over a long period (1850–2014) using the new Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. Based on CMIP6 multi-model ensemble simulations, the historical simulations (twentieth century) and future (twenty-first century) SAT projections were investigated. Our calculations show that during 1850–2014, the combination of ANT and natural (NAT) (‘ALL = ANT + NAT’) forcings increased the EA’s SAT by 0.031 °C/decade, while a high increase of 0.08 °C/decade due to greenhouse gas (GHG) emissions. The ANT forcing rapidly increased after 1969. As a result, SAT change was enhanced at a rate of 0.268 °C/decade and 0.255 °C/decade due to GHG and ALL forcings, respectively. Human-induced GHG emissions were the dominant factors driving SAT warming and will also contribute to substantial future warming trends. Additionally, the optimal fingerprinting method was used to signify the influence of ANT forcing on climate change in EA. In a two-signal analysis, the ANT forcing was distinctly detected and detached from NAT forcing. In three-signal analyses, GHG forcing was dominant and separated from AER and NAT forcings. The future projections from 2015 to 2100 were examined based on CMIP6 socioeconomic pathway emission scenarios.

Differing responses of precipitation in Northern Hemisphere mid-latitudes to increased black carbon aerosols and carbon dioxide

As the most important contributors to global warming in recent decades, anthropogenic carbon dioxide (CO₂) and black carbon (BC) play significant roles in driving the global/regional hydrological cycle. Most of previous studies on the climate effects of CO₂ and BC focused on tropics and monsoon regions. The influences and their differences of CO₂ and BC on the precipitation in Northern Hemisphere mid-latitudes (NHML) have not been paid enough attention. Here we investigate the NHML precipitation responses to a tenfold increase in BC and a doubling of CO₂ by analyzing the multi-model simulation results from the Precipitation Driver Response Model Intercomparison Project (PDRMIP). Our results show that the NHML precipitation changes induced by BC and CO₂ distinctly differ in trends and seasons. The increased BC will reduce the NHML precipitation, especially in summer, whereas the doubled CO₂ will enhance the regional precipitation, mainly in winter. The differences between the BC and CO₂ induced NHML precipitation changes are most distinct in Central Asia and central North America. Further analyses reveal the underlying mechanisms of the distinct responses of precipitation: the decrease in NHML precipitation induced by BC aerosols mainly results from the dynamic effect by reducing the temperature gradient, thereby weakening the zonal wind, while the increased precipitation by CO₂ is caused by the increase in atmospheric water vapor through the thermodynamic effect. The results of these simulations are helpful for understanding the mechanism of anthropogenic precipitation changes in mid-latitudes.

Robust detection of forced warming in the presence of potentially large climate variability

Climate warming is unequivocal and exceeds internal climate variability. However, estimates of the magnitude of decadal-scale variability from models and observations are uncertain, limiting determination of the fraction of warming attributable to external forcing. Here, we use statistical learning to extract a fingerprint of climate change that is robust to different model representations and magnitudes of internal variability. We find a best estimate forced warming trend of 0.8°C over the past 40 years, slightly larger than observed. It is extremely likely that at least 85% is attributable to external forcing based on the median variability across climate models. Detection remains robust even when evaluated against models with high variability and if decadal-scale variability were doubled. This work addresses a long-standing limitation in attributing warming to external forcing and opens up opportunities even in the case of large model differences in decadal-scale variability, model structural uncertainty, and limited observational records.

Human influence on sub-regional surface air temperature change over India

Human activities have been implicated in the observed increase in Global Mean Surface Temperature. Over regional scales where climatic changes determine societal impacts and drive adaptation related decisions, detection and attribution (D&A) of climate change can be challenging due to the greater contribution of internal variability, greater uncertainty in regionally important forcings, greater errors in climate models, and larger observational uncertainty in many regions of the world. We examine the causes of annual and seasonal surface air temperature (TAS) changes over sub-regions (based on a demarcation of homogeneous temperature zones) of India using two observational datasets together with results from a multimodel archive of forced and unforced simulations. Our D&A analysis examines sensitivity of the results to a variety of optimal fingerprint methods and temporal-averaging choices. We can robustly attribute TAS changes over India between 1956-2005 to anthropogenic forcing mostly by greenhouse gases and partially offset by other anthropogenic forcings including aerosols and land use land cover change.

Historical and Projected Surface Temperature over India during the 20th and 21st century

Surface Temperature (ST) over India has increased by ~0.055 K/decade during 1860-2005 and follows the global warming trend. Here, the natural and external forcings (e.g., natural and anthropogenic) responsible for ST variability are studied from Coupled Model Inter-comparison phase 5 (CMIP5) models during the 20th century and projections during the 21st century along with seasonal variability. Greenhouse Gases (GHG) and Land Use (LU) are the major factors that gave rise to warming during the 20th century. Anthropogenic Aerosols (AA) have slowed down the warming rate. The CMIP5 projection over India shows a sharp increase in ST under Representative Concentration Pathways (RCP) 8.5 where it reaches a maximum of 5 K by the end of the 21st century. Under RCP2.6 emission scenarios, ST increases up to the year 2050 and decreases afterwards. The seasonal variability of ST during the 21st century shows significant increase during summer. Analysis of rare heat and cold events for 2080-2099 relative to a base period of 1986-2006 under RCP8.5 scenarios reveals that both are likely to increase substantially. However, by controlling the regional AA and LU change in India, a reduction in further warming over India region might be achieved.

Detecting sulphate aerosol geoengineering with different methods

Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. We investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr⁻¹ of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.

Migrations and dynamics of the intertropical convergence zone

Rainfall on Earth is most intense in the intertropical convergence zone (ITCZ), a narrow belt of clouds centred on average around six degrees north of the Equator. The mean position of the ITCZ north of the Equator arises primarily because the Atlantic Ocean transports energy northward across the Equator, rendering the Northern Hemisphere warmer than the Southern Hemisphere. On seasonal and longer timescales, the ITCZ migrates, typically towards a warming hemisphere but with exceptions, such as during El Niño events. An emerging framework links the ITCZ to the atmospheric energy balance and may account for ITCZ variations on timescales from years to geological epochs.

Identifying human influences on atmospheric temperature

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

Systems approaches in global change and biogeochemistry research

Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.

Climate change and heat-related mortality in six cities Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change

Previous assessments of the impacts of climate change on heat-related mortality use the "delta method" to create temperature projection time series that are applied to temperature-mortality models to estimate future mortality impacts. The delta method means that climate model bias in the modelled present does not influence the temperature projection time series and impacts. However, the delta method assumes that climate change will result only in a change in the mean temperature but there is evidence that there will also be changes in the variability of temperature with climate change. The aim of this paper is to demonstrate the importance of considering changes in temperature variability with climate change in impacts assessments of future heat-related mortality. We investigate future heat-related mortality impacts in six cities (Boston, Budapest, Dallas, Lisbon, London and Sydney) by applying temperature projections from the UK Meteorological Office HadCM3 climate model to the temperature-mortality models constructed and validated in Part 1. We investigate the impacts for four cases based on various combinations of mean and variability changes in temperature with climate change. The results demonstrate that higher mortality is attributed to increases in the mean and variability of temperature with climate change rather than with the change in mean temperature alone. This has implications for interpreting existing impacts estimates that have used the delta method. We present a novel method for the creation of temperature projection time series that includes changes in the mean and variability of temperature with climate change and is not influenced by climate model bias in the modelled present. The method should be useful for future impacts assessments. Few studies consider the implications that the limitations of the climate model may have on the heat-related mortality impacts. Here, we demonstrate the importance of considering this by conducting an evaluation of the daily and extreme temperatures from HadCM3, which demonstrates that the estimates of future heat-related mortality for Dallas and Lisbon may be overestimated due to positive climate model bias. Likewise, estimates for Boston and London may be underestimated due to negative climate model bias. Finally, we briefly consider uncertainties in the impacts associated with greenhouse gas emissions and acclimatisation. The uncertainties in the mortality impacts due to different emissions scenarios of greenhouse gases in the future varied considerably by location. Allowing for acclimatisation to an extra 2 degrees C in mean temperatures reduced future heat-related mortality by approximately half that of no acclimatisation in each city.

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales. The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.

Detection of a direct carbon dioxide effect in continental river runoff records

Continental runoff has increased through the twentieth century despite more intensive human water consumption. Possible reasons for the increase include: climate change and variability, deforestation, solar dimming, and direct atmospheric carbon dioxide (CO2) effects on plant transpiration. All of these mechanisms have the potential to affect precipitation and/or evaporation and thereby modify runoff. Here we use a mechanistic land-surface model and optimal fingerprinting statistical techniques to attribute observational runoff changes into contributions due to these factors. The model successfully captures the climate-driven inter-annual runoff variability, but twentieth-century climate alone is insufficient to explain the runoff trends. Instead we find that the trends are consistent with a suppression of plant transpiration due to CO2-induced stomatal closure. This result will affect projections of freshwater availability, and also represents the detection of a direct CO2 effect on the functioning of the terrestrial biosphere.

Amplification of surface temperature trends and variability in the tropical atmosphere

The month-to-month variability of tropical temperatures is larger in the troposphere than at Earth's surface. This amplification behavior is similar in a range of observations and climate model simulations and is consistent with basic theory. On multidecadal time scales, tropospheric amplification of surface warming is a robust feature of model simulations, but it occurs in only one observational data set. Other observations show weak, or even negative, amplification. These results suggest either that different physical mechanisms control amplification processes on monthly and decadal time scales, and models fail to capture such behavior; or (more plausibly) that residual errors in several observational data sets used here affect their representation of long-term trends.

Human contribution to the European heatwave of 2003

The summer of 2003 was probably the hottest in Europe since at latest ad 1500, and unusually large numbers of heat-related deaths were reported in France, Germany and Italy. It is an ill-posed question whether the 2003 heatwave was caused, in a simple deterministic sense, by a modification of the external influences on climate--for example, increasing concentrations of greenhouse gases in the atmosphere--because almost any such weather event might have occurred by chance in an unmodified climate. However, it is possible to estimate by how much human activities may have increased the risk of the occurrence of such a heatwave. Here we use this conceptual framework to estimate the contribution of human-induced increases in atmospheric concentrations of greenhouse gases and other pollutants to the risk of the occurrence of unusually high mean summer temperatures throughout a large region of continental Europe. Using a threshold for mean summer temperature that was exceeded in 2003, but in no other year since the start of the instrumental record in 1851, we estimate it is very likely (confidence level >90%) that human influence has at least doubled the risk of a heatwave exceeding this threshold magnitude.