Identifying keystone connectivity spots under climate change: Implications to conservation and management of riparian systems.

Climate change has intensified the effects of habitat fragmentation in many ecosystems, particularly exacerbated in riparian habitats. Therefore, there is an urgent need to identify keystone connectivity spots to ensure long-term conservation and sustainable management of riparian systems as they play a crucial role for landscape connectivity. This paper aims to identify critical areas for connectivity under two contrasting climate change scenarios (RCP 4.5 and RCP 8.5 models) for the years 2030, 2050 and 2100 and to group these critical areas by similar connectivity in keystone spots for sustainable management. A set of analyses comprising climate analysis, drainage network analysis, configuration of potential riparian habitats, riparian habitat connectivity, data clustering, and statistical analysis within a Spanish river basin (NW Spain) were applied. The node and link connectivity would be reduced under the two climate change scenarios (≈2.5 % and 4.4 % reduction, respectively), intensifying riparian habitat fragmentation. Furthermore, 51 different clusters (critical areas) were obtained and classified in five classes (keystone spots) with similar connectivity across the different scenarios of climate change. Each keystone spot obtained by hierarchical classification was associated with one or more climate scenarios. One of these keystone spots was especially susceptible to the worst climate change scenario. Key riparian connectivity spots will be crucial for the management and restoration of highly threatened riparian systems and to ensure long-term biodiversity conservation.

Short-term resilience to climate-induced temperature increases for equatorial sea turtle populations.

Projection models are being increasingly used to manage threatened taxa by estimating their responses to climate change. Sea turtles are particularly susceptible to climate change as they have temperature-dependent sex determination and increased sand temperatures on nesting beaches could result in the 'feminisation' of hatchling sex ratios for some populations. This study modelled likely long-term trends in sand temperatures and hatchling sex ratios at an equatorial nesting site for endangered green turtles (Chelonia mydas) and critically endangered hawksbill turtles (Eretmochelys imbricata). A total of 1078 days of sand temperature data were collected from 28 logger deployments at nest depth between 2018 and 2022 in Papua New Guinea (PNG). Long-term trends in sand temperature were generated from a model using air temperature as an environmental proxy. The influence of rainfall and seasonal variation on sand temperature was also investigated. Between 1960 and 2019, we estimated that sand temperature increased by ~0.6°C and the average hatchling sex ratio was relatively balanced (46.2% female, SD = 10.7). No trends were observed in historical rainfall anomalies and projections indicated no further changes to rainfall until 2100. Therefore, the sex ratio models were unlikely to be influenced by changing rainfall patterns. A relatively balanced sex ratio such as this is starkly different to the extremely female-skewed hatchling sex ratio (>99% female) reported for another Coral Sea nesting site, Raine Island (~850 km West). This PNG nesting site is likely rare in the global context, as it is less threatened by climate-induced feminisation. Although there is no current need for 'cooling' interventions, the mean projected sex ratios for 2020-2100 were estimated 76%-87% female, so future interventions may be required to increase male production. Our use of long-term sand temperature and rainfall trends has advanced our understanding of climate change impacts on sea turtles.

A possible linkage of Eurasian heat wave and East Asian heavy rainfall in Relation to the Rapid Arctic warming.

In June-July 2020 two remarkable weather events occurred in northern Eurasia. One is a severe heat wave that produced a record-breaking temperature of 38 °C at Verkhoyansk, eastern Siberia on 20 June. The other one is heavy rainfall events observed in East Asia (southern China and southwestern Japan) in early July, causing severe floods that brought about considerable damage to infrastructure and the economy, as well as the loss of human lives. Despite the accumulated evidence of stronger and more extreme heat waves and heavy rainfall as a result of global warming, little is known about the linkage between these phenomena. Given that the Arctic is warming twice as fast as the global mean, Arctic warming might be enhancing the increase of heavy rainfall events in East Asia. Here, we investigated the relationship between the Siberian heat wave and the East Asian heavy rainfall that occurred summer in 2020. An empirical orthogonal function (EOF) analysis applied to atmospheric reanalysis data of 1958-2020 period captures dominant summer circulation patterns reflecting atmospheric internal variability and externally forced anomalies. On the basis of these EOF patterns, operational forecasts of summer 2020 using the global model from the Japan Meteorological Agency (JMA) and a global climate model experiment based on 2-K warming future projection are utilized to examine roles of the internal variability and external forcing, respectively. Consistent results between them reveal that development of the blocking high over eastern Siberia has certain impacts on rainfall anomalies over East Asia. By a statistical technique applied to the ensemble forecast data, prediction of the East Asian precipitation is improved by 10-20% of its amplitude. Our research demonstrates possibility that East Asian rainfall is being enhanced by high-latitude atmospheric circulations due to the Arctic warming even in the current climate in which the tropical warming is not yet severe. Suggestions are given that continued Arctic warming and a future increase of tropical warming will lead to increases of the frequency and severity of heavy rainfall events in East Asia.

Disquisitions Relating to Principles of Thermodynamic Equilibrium in Climate Modelling.

We revisit the fundamental principles of thermodynamic equilibrium in relation to heat transfer processes within the Earth's atmosphere. A knowledge of equilibrium states at ambient temperatures (T) and pressures (p) and deviations for these p-T states due to various transport 'forces' and flux events give rise to gradients (dT/dz) and (dp/dz) of height z throughout the atmosphere. Fluctuations about these troposphere averages determine weather and climates. Concentric and time-span average values (z, Δt)) and its gradients known as the lapse rate = d < T(z) >/dz have hitherto been assumed in climate models to be determined by a closed, reversible, and adiabatic expansion process against the constant gravitational force of acceleration (g). Thermodynamics tells us nothing about the process mechanisms, but adiabatic-expansion hypothesis is deemed in climate computer models to be convection rather than conduction or radiation. This prevailing climate modelling hypothesis violates the 2nd law of thermodynamics. This idealized hypothetical process cannot be the causal explanation of the experimentally observed mean lapse rate (approx.−6.5 K/km) in the troposphere. Rather, the troposphere lapse rate is primarily determined by the radiation heat-transfer processes between black-body or IR emissivity and IR and sunlight absorption. When the effect of transducer gases (H2O and CO2) is added to the Earth's emission radiation balance in a 1D-2level primitive model, a linear lapse rate is obtained. This rigorous result for a perturbing cooling effect of transducer ('greenhouse') gases on an otherwise sunlight-transducer gas-free troposphere has profound implications. One corollary is the conclusion that increasing the concentration of an existing weak transducer, i.e., CO2, could only have a net cooling effect, if any, on the concentric average (z = 0) at sea level and lower troposphere (z < 1 km). A more plausible explanation of global warming is the enthalpy emission 'footprint' of all fuels, including nuclear.

Managing the effects of extreme sub-daily rainfall and flash floods-a practitioner's perspective.

Extreme sub-daily rainfall affects flooding in the UK and urban pollution management. Water utilities in the UK need to understand the characteristics of this rainfall, and how it may change in the future in order to plan for and manage these impacts. There is also significant interest from infrastructure owners and urban authorities exposed to flood risk from short-period, intense rainfall events. This paper describes how UK flood risk guidance incorporates allowances for climate change and how recent research using convection-permitting climate models is helping to inform this guidance. The guidance documents are used by engineers and scientists in the modelling of sewer networks, smaller river catchments and urban drainage areas and provide values to 'uplift' rainfall event data used as model inputs to reflect climate change model projections. With an increasing focus on continuous simulation modelling using time series rainfall, research into adjusting time series data to reflect future rainfall characteristics in convection-permitting climate models is discussed. Other knowledge gaps for practitioners discussed are the potential changing shape (profile) of future rainfall events and future changes in antecedent wetness conditions. The author explains the challenge of developing simple and effective guidance for practitioners from the complex scientific output. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Machine learning for weather and climate are worlds apart.

Modern weather and climate models share a common heritage and often even components; however, they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour, there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem, which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. To emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make climate a domain which is rich in interesting machine learning challenges. Here, I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI.

In September 2019, a workshop was held to highlight the growing area of applying machine learning techniques to improve weather and climate prediction. In this introductory piece, we outline the motivations, opportunities and challenges ahead in this exciting avenue of research. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Challenges and outlook for convection-permitting climate modelling.

Climate projections at very high resolution (kilometre-scale grid spacing) are becoming affordable. These 'convection-permitting' models (CPMs), commonly used for weather forecasting, better represent land-surface characteristics and small-scale processes in the atmosphere such as convection. They provide a step change in our understanding of future changes at local scales and for extreme weather events. For short-duration precipitation extremes, this includes capturing local storm feedbacks, which may modify future increases. Despite the major advance CPMs offer, there are still key challenges and outstanding science issues. Heavy rainfall tends to be too intense; there are challenges in representing land-surface processes; sub-kilometre scale processes still need to be parametrized, with existing parametrization schemes often requiring development for use in CPMs; CPMs rely on the quality of lateral boundary forcing and typically do not include ocean-coupling; large CPM ensembles that comprehensively sample future uncertainties are costly. Significant progress is expected over the next few years: scale-aware schemes may improve the representation of unresolved convective updrafts; work is underway to improve the modelling of complex land-surface fluxes; CPM ensemble experiments are underway and methods to synthesize this information with larger coarser-resolution model ensembles will lead to local-scale predictions with more comprehensive uncertainty context for user application. Large-domain (continental or tropics-wide) CPM climate simulations, potentially with additional earth-system processes such as ocean and wave coupling and terrestrial hydrology, are an exciting prospect, allowing not just improved representation of local processes but also of remote teleconnections. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

The Ski Climate Index (SCI): fuzzification and a regional climate modeling application for Turkey.

Climatology has increasingly become an important discipline for understanding tourism and recreation, especially in the era of contemporary climate change. Climate indices, in this respect, have been useful tools to yield the climatic attractiveness of tourism destinations as well as in understanding their altering suitability to various tourism types along with the changing climates. In this study, a major gap for a comprehensive climate index tailored for ski tourism is aimed to be fulfilled. For this purpose, initially the Ski Climate Index (SCI) is specified, based on fuzzy logic and as informed by literature and through extensive co-creation with the ski tourism industry experts, and applied to an emerging destination, Turkey, based on regional climate modeling projections. The index is designed as a combination of snow reliability and aesthetics and comfort facets, the latter of which includes sunshine, wind, and thermal comfort conditions. Results show that the Eastern Anatolia region is climatically the most suitable area for future development, taking account of the overriding effects of natural and technical snow reliability. Future research suggestions include the incorporation of more components into the index as well as technical recommendations to improve its application and validation.

The physics of numerical analysis: a climate modelling case study.

The case is made for a much closer synergy between climate science, numerical analysis and computer science. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.

The effect of state dependence in a delay differential equation model for the El Niño Southern Oscillation.

Delay differential equations (DDEs) have been used successfully in the past to model climate systems at a conceptual level. An important aspect of these models is the existence of feedback loops that feature a delay time, usually associated with the time required to transport energy through the atmosphere and/or oceans across the globe. So far, such delays are generally assumed to be constant. Recent studies have demonstrated that even simple DDEs with non-constant delay times, which change depending on the state of the system, can produce surprisingly rich dynamical behaviour. Here, we present arguments for the state dependence of the delay in a DDE model for the El Niño Southern Oscillation phenomenon in the climate system. We then conduct a bifurcation analysis by means of continuation software to investigate the effect of state dependence in the delay on the observed dynamics of the system. More specifically, we show that the underlying delay-induced structure of resonance regions may change considerably in the presence of state dependence. This article is part of the theme issue 'Nonlinear dynamics of delay systems'.

The use of driving endonuclease genes to suppress mosquito vectors of malaria in temporally variable environments.

BACKGROUND: The use of gene drive systems to manipulate populations of malaria vectors is currently being investigated as a method of malaria control. One potential system uses driving endonuclease genes (DEGs) to spread genes that impose a genetic load. Previously, models have shown that the introduction of DEG-bearing mosquitoes could suppress or even extinguish vector populations in spatially-heterogeneous environments which were constant over time. In this study, a stochastic spatially-explicit model of mosquito ecology is combined with a rainfall model which enables the generation of a variety of daily precipitation patterns. The model is then used to investigate how releases of a DEG that cause a bias in population sex ratios towards males are affected by seasonal or random rainfall patterns. The parameters of the rainfall model are then fitted using data from Bamako, Mali, and Mbita, Kenya, to evaluate release strategies in similar climatic conditions. RESULTS: In landscapes with abundant resources and large mosquito populations the spread of a DEG is reliable, irrespective of variability in rainfall. This study thus focuses mainly on landscapes with low density mosquito populations where the spread of a DEG may be sensitive to variation in rainfall. It is found that an introduced DEG will spread into its target population more reliably in wet conditions, yet an established DEG will have more impact in dry conditions. In strongly seasonal environments, it is thus preferable to release DEGs at the onset of a wet season to maximize their spread before the following dry season. If the variability in rainfall has a substantial random component, there is a net increase in the probability that a DEG release will lead to population extinction, due to the increased impact of a DEG which manages to establish in these conditions. For Bamako, where annual rainfall patterns are characterized by a long dry season, it is optimal to release a DEG at the start of the wet season, where the population is growing fastest. By contrast release timing is of lower importance for the less seasonal Mbita. CONCLUSION: This analysis suggests that DEG based methods of malaria vector control can be effective in a wide range of climates. In environments with substantial temporal variation in rainfall, careful timing of releases which accounts for the temporal variation in population density can substantially improve the probability of mosquito suppression or extinction.

Strong thermal acclimation of photosynthesis in tropical and temperate wet-forest tree species: the importance of altered Rubisco content.

Understanding of the extent of acclimation of light-saturated net photosynthesis (An ) to temperature (T), and associated underlying mechanisms, remains limited. This is a key knowledge gap given the importance of thermal acclimation for plant functioning, both under current and future higher temperatures, limiting the accuracy and realism of Earth system model (ESM) predictions. Given this, we analysed and modelled T-dependent changes in photosynthetic capacity in 10 wet-forest tree species: six from temperate forests and four from tropical forests. Temperate and tropical species were each acclimated to three daytime growth temperatures (Tgrowth ): temperate - 15, 20 and 25 °C; tropical - 25, 30 and 35 °C. CO2 response curves of An were used to model maximal rates of RuBP (ribulose-1,5-bisphosphate) carboxylation (Vcmax ) and electron transport (Jmax ) at each treatment's respective Tgrowth and at a common measurement T (25 °C). SDS-PAGE gels were used to determine abundance of the CO2 -fixing enzyme, Rubisco. Leaf chlorophyll, nitrogen (N) and mass per unit leaf area (LMA) were also determined. For all species and Tgrowth , An at current atmospheric CO2 partial pressure was Rubisco-limited. Across all species, LMA decreased with increasing Tgrowth . Similarly, area-based rates of Vcmax at a measurement T of 25 °C (Vcmax25 ) linearly declined with increasing Tgrowth , linked to a concomitant decline in total leaf protein per unit leaf area and Rubisco as a percentage of leaf N. The decline in Rubisco constrained Vcmax and An for leaves developed at higher Tgrowth and resulted in poor predictions of photosynthesis by currently widely used models that do not account for Tgrowth -mediated changes in Rubisco abundance that underpin the thermal acclimation response of photosynthesis in wet-forest tree species. A new model is proposed that accounts for the effect of Tgrowth -mediated declines in Vcmax25 on An , complementing current photosynthetic thermal acclimation models that do not account for T sensitivity of Vcmax25 .

Factors affecting projected Arctic surface shortwave heating and albedo change in coupled climate models.

We use a large ensemble of simulations from the Community Earth System Model to quantify simulated changes in the twentieth and twenty-first century Arctic surface shortwave heating associated with changing incoming solar radiation and changing ice conditions. For increases in shortwave absorption associated with albedo reductions, the relative influence of changing sea ice surface properties and changing sea ice areal coverage is assessed. Changes in the surface sea ice properties are associated with an earlier melt season onset, a longer snow-free season and enhanced surface ponding. Because many of these changes occur during peak solar insolation, they have a considerable influence on Arctic surface shortwave heating that is comparable to the influence of ice area loss in the early twenty-first century. As ice area loss continues through the twenty-first century, it overwhelms the influence of changes in the sea ice surface state, and is responsible for a majority of the net shortwave increases by the mid-twenty-first century. A comparison with the Arctic surface albedo and shortwave heating in CMIP5 models indicates a large spread in projected twenty-first century change. This is in part related to different ice loss rates among the models and different representations of the late twentieth century ice albedo and associated sea ice surface state.

The inconstancy of the transient climate response parameter under increasing CO2.

In the Coupled Model Intercomparison Project Phase 5 (CMIP5), the model-mean increase in global mean surface air temperature T under the 1pctCO2 scenario (atmospheric CO(2) increasing at 1% yr(-1)) during the second doubling of CO(2) is 40% larger than the transient climate response (TCR), i.e. the increase in T during the first doubling. We identify four possible contributory effects. First, the surface climate system loses heat less readily into the ocean beneath as the latter warms. The model spread in the thermal coupling between the upper and deep ocean largely explains the model spread in ocean heat uptake efficiency. Second, CO(2) radiative forcing may rise more rapidly than logarithmically with CO(2) concentration. Third, the climate feedback parameter may decline as the CO(2) concentration rises. With CMIP5 data, we cannot distinguish the second and third possibilities. Fourth, the climate feedback parameter declines as time passes or T rises; in 1pctCO2, this effect is less important than the others. We find that T projected for the end of the twenty-first century correlates more highly with T at the time of quadrupled CO(2) in 1pctCO2 than with the TCR, and we suggest that the TCR may be underestimated from observed climate change.

How changing climate may influence air pollution control strategies for 2030?

Air pollution is a global threat leading to large impacts on human health and ecosystems. In Europe, air quality remains poor in many areas, despite reductions in emissions and ambient concentrations. Air pollution and climate change are the biggest environmental concerns for Europeans, implying concerted and integrated actions to tackle them. The revised 2016 European National Emission Ceilings Directive (NECD) enforces Member States to implement strategies, based on emission reduction measures, aimed to comply with targets by 2030 and achieve European Union (EU) and World Health Organization air quality objectives for environment and health protection. Despite those strategies are designed for 2030, the influence of climate change on air quality is not accounted for. In this sense, the purpose of this paper is the evaluation of the climate change impact on future air quality, taking into consideration emission reduction measures. The WRF-CAMx air quality modelling system was applied over Europe for one year selected as representative of a short-term changing climate (around 2030), and compared to a base case year, to estimate to what extent the climate variables by themselves could positively or negatively influence air quality. Results indicate that meteorological conditions may be decisive for the air quality state in the future. Differences between future and present simulations pointed to a global decrease of ozone levels in the future; increases and decreases in particulate matter and nitrogen dioxide concentrations over different seasons and European regions. This work is intended to contribute to a better understanding of the influence of climate variables on air quality improvement strategies as an additional support to European environmental authorities in developing the National Air Pollution Control Programmes in the scope of NECD.

Precipitation-optimised targeting of nitrogen fertilisers in a model maize cropping system.

Typically, half of the nitrogen (N) fertiliser applied to agricultural fields is lost to the wider environment. This inefficiency is driven by soil processes such as denitrification, volatilisation, surface run-off and leaching. Rainfall plays an important role in regulating these processes, ultimately governing when and where N fertiliser moves in soil and its susceptibility to gaseous loss. The interaction between rainfall, plant N uptake and N losses, however, remains poorly understood. In this study we use numerical modelling to predict the optimal N fertilisation strategy with respect to rainfall patterns and offer mechanistic explanations to the resultant differences in optimal times of fertiliser application. We developed a modelling framework that describes water and N transport in soil over a growing season and assesses nitrogen use efficiency (NUE) of split fertilisations within the context of different rainfall patterns. We used ninety rainfall patterns to determine their impact on optimal N fertilisation times. We considered the effects of root growth, root N uptake, microbial transformation of N and the effect of soil water saturation and flow on N movement in the soil profile. On average, we show that weather-optimised fertilisation strategies could improve crop N uptake by 20% compared to the mean uptake. In drier years, weather-optimising N applications improved the efficiency of crop N recovery by 35%. Further analysis shows that maximum plant N uptake is greatest under drier conditions due to reduced leaching, but it is harder to find the maximum due to low N mobility. The model could capture contrasting trends in NUE seen in previous arable cropping field trials. Furthermore, the model predicted that the variability in NUE seen in the field could be associated with precipitation-driven differences in N leaching and mobility. In conclusion, our results show that NUE in cropping systems could be significantly enhanced by synchronising fertiliser timings with both crop N demand and local weather patterns.

A High-Throughput Assay for Quantifying Phenotypic Traits of Microalgae.

High-throughput methods for phenotyping microalgae are in demand across a variety of research and commercial purposes. Many microalgae can be readily cultivated in multi-well plates for experimental studies which can reduce overall costs, while measuring traits from low volume samples can reduce handling. Here we develop a high-throughput quantitative phenotypic assay (QPA) that can be used to phenotype microalgae grown in multi-well plates. The QPA integrates 10 low-volume, relatively high-throughput trait measurements (growth rate, cell size, granularity, chlorophyll a, neutral lipid content, silicification, reactive oxygen species accumulation, and photophysiology parameters: ETRmax, Ik, and alpha) into one workflow. We demonstrate the utility of the QPA on Thalassiosira spp., a cosmopolitan marine diatom, phenotyping six strains in a standard nutrient rich environment (f/2 media) using the full 10-trait assay. The multivariate phenotypes of strains can be simplified into two dimensions using principal component analysis, generating a trait-scape. We determine that traits show a consistent pattern when grown in small volume compared to more typical large volumes. The QPA can thus be used for quantifying traits across different growth environments without requiring exhaustive large-scale culturing experiments, which facilitates experiments on trait plasticity. We confirm that this assay can be used to phenotype newly isolated diatom strains within 4 weeks of isolation. The QPA described here is highly amenable to customisation for other traits or unicellular taxa and provides a framework for designing high-throughput experiments. This method will have applications in experimental evolution, modelling, and for commercial applications where screening of phytoplankton traits is of high importance.

Dynamical landscape and multistability of a climate model.

We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyse their interplay. First, drawing from the theory of quasi-potentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, and specifically manifold learning, we characterize the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two versions of PLASIM, the climate model used in this study. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth's climate.