Search

The paper considers the results of climate change modelling for 1850–2100 using the INM-CM6M climate model of the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. The calculations were performed according to the CMIP6 protocol for modelling the present-day climate for the period from 1850 to 2014 and the IPCC scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 of anthropogenic forcing changes in 2015–2100. We analyse changes in such characteristics of the climate system as global mean near-surface temperature, spatial distributions of near-surface temperature, precipitation, sea level pressure, and radiative forcing characteristics in comparison with observational data and results obtained with the previous (INM-CM5) version of the model. We conclude that the new version of the model is superior to the previous one in terms of the quality of simulation of the observed climate and its changes in 1960–2022. The amplitude of global warming predicted by the INM-CM6M for moderate IPCC scenarios is close to the average value of the CMIP6 model ensemble, and for severe scenarios it is closer to the upper limit of its range. For the SSP3-7.0 and SSP5-8.5 scenarios, INM-CM6M predicts a complete loss of Arctic sea ice in summer. As the rate of global warming increases, so does the amplitude and duration of extreme weather and climate events. [ABSTRACT FROM AUTHOR]

This paper explores the capabilities of two types of recurrent neural networks, unidirectional and bidirectional long short-term memory networks, to build a surrogate model for a coupled fast–slow dynamic system and predicting its nonlinear chaotic behaviour. The dynamical system in question, comprising two versions of the classical Lorenz model with a small time-scale separation factor, is treated as an atmosphere–ocean research simulator. In numerical experiments, the number of hidden layers and the number of nodes in each hidden layer varied from 1 to 5 and from 16 to 256, respectively. The basic configuration of the surrogate model, determined experimentally, has three hidden layers, each comprising between 16 and 128 nodes. The findings revealed the advantages of bidirectional neural networks over unidirectional ones in terms of forecasting accuracy. As the forecast horizon increases, the accuracy of forecasts deteriorates, which was quite expected, primarily due to the chaotic behaviour of the fast subsystem. All other things being equal, increasing the number of neurons in hidden layers facilitates the improvement of forecast accuracy. The obtained results indicate that the quality of short-term forecasts with a lead time of up to 0.75 model time units (MTU) improves most significantly. The predictability limit of the fast subsystem ("atmosphere") is somewhat greater than the Lyapunov time. [ABSTRACT FROM AUTHOR]

The European summer (June–August) 2022 was characterised by warm and dry anomalies across much of the continent, likely influenced by a northward‐shifted jet stream. These general features were well predicted by European Centre for Medium‐Range Weather Forecasts' system 5 seasonal forecast, initialised on May 1. Such successful predictions for European summers are relatively uncommon, particularly for atmospheric circulation. In this study, a set of hindcast experiments is employed to investigate the role that initialisation of the ocean, atmosphere, and land surface played in the 2022 forecast. We find that the trend from external forcing was the strongest contributor to the forecast near‐surface temperature anomalies, with atmospheric circulation and land‐surface interactions playing a secondary role. On the other hand, atmospheric circulation made a strong contribution to precipitation anomalies. Modelled Euro‐Atlantic circulation anomalies in 2022 were consistent with a La Niña‐forced teleconnection from the tropical Pacific. However, a northward jet trend in the model hindcasts with increasing greenhouse gas concentrations also contributed to the predicted circulation anomalies in 2022. In contrast, the observed linear trend in the jet over the past four decades was a southward shift, though it is unclear whether this trend was driven by external forcings or natural variability. Nevertheless, this case study demonstrates that important features of at least some European summers are predictable at the seasonal time‐scale. [ABSTRACT FROM AUTHOR]

Multi-decadal trends in the wintertime North Atlantic Oscillation (NAO) are under-represented by coupled general circulation models (CGCMs), consistent with a lack of autocorrelation in their NAO index series. This study proposes and tests two simple "reddening" approaches for correcting this problem in simulated indices based on simple one parameter short-term (AR; Auto-Regressive order 1) and long-term (FD; Fractional-Difference) time series filters. Using CGCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the FD filter successfully improves the autocorrelation structure of the NAO, and in turn the simulation of extreme trends, while the AR filter is less successful. The 1963–1993 NAO trend is the maximum 31-year trend in the historical period. Raw CGCMs underestimate the likelihood of this trend by a factor of ten but this discrepancy is corrected after reddening. CMIP6 future projections show that long-term (2024–2094) NAO ensemble mean trends systematically increase with the magnitude of radiative forcing: -2.4 to 3.5 hPa/century for low-to-high forcing after reddening (more than double the range using raw output). The related likelihood of future maximum 31year trends comparable to 1963–1993 ranges from 3 to 7% whereas none of these CMIP6 projections simulate this without reddening. Near-term projections of the next 31 years (2024–2054) are less sensitive than long term trends to the future scenario, showing weak-to-no forced trend. However, reddening increases the ensemble range by 74% (to +/-1 standard deviation/decade), which could increase/decrease regional climate change signals in the Northern Hemisphere by magnitudes that are underestimated when using raw CGCM output. [ABSTRACT FROM AUTHOR]

Ensemble forecasts have been shown to better predict observed Atlantic climate variability than that of their own ensemble members. This phenomenon—termed the signal‐to‐noise paradox—is found to be widespread across models, timescales, and climate variables, and has wide implications. The signal‐to‐noise paradox can be interpreted as forecasts underestimating the amplitude of predictable signals on seasonal‐to‐decadal timescales. The cause of this remains unknown. Here, we examine sea level pressure variability from a very large multi‐model ensemble of uninitialized atmosphere‐only simulations, focusing on boreal winter. To assess signal‐to‐noise errors, the ratio of predictable components (RPC) is examined globally, as well as for regional climate indices: the North Atlantic Oscillation, Arctic Oscillation, Southern Annular Mode, and an Arctic index. Our analyses reveal significant correlations between the multi‐model ensemble‐mean and observations over large portions of the globe, particularly the tropics, North Atlantic, and North Pacific. However, RPC values greater than one are apparent over many extratropical regions and in all four climate indices. Higher‐resolution models produce greater observation‐model correlations and greater RPC values than lower‐resolution models in all four climate indices. We find that signal‐to‐noise errors emerge more clearly at higher resolution, but the amplitudes of predictable signals do not increase with resolution, at least across the range of resolutions considered here. Our results suggest that free‐running atmospheric models underestimate predictable signals in the absence of sea surface temperature biases, implying that signal‐to‐noise errors originate in the atmosphere or in ocean–atmosphere coupling. [ABSTRACT FROM AUTHOR]

In the 1980s, one of the fundamental topics of scientific debate was the discussion of the global consequences of nuclear war. At that time, the hypothesis of “nuclear winter” emerged, in which scientists attempted to analyse the theoretical possibility of a sharp cooling of the Earth after mass fires, and to find natural analogues of a potential climatic catastrophe. In the following decades, the “nuclear winter” hypothesis went through various stages of criticism and reassessment. In the early 2000s, studies of the problem became regional in nature, focusing mainly on the possible consequences of local nuclear conflicts. In this article, the authors analyse the stages of development of research on the “nuclear winter” hypothesis, focusing on the Russian and American approaches, and conclude that the body of work produced by American and Soviet scientists in 1983–1985 represents the first attempts by the scientific community to conceptualise the problem of responsible nuclear policy.

The present study models potential regions for Argan tree plantations in Algeria using the MaxEnt approach, taking into account the sites of successful plantations at national level. There has been exhaustive sampling of 45 sites of plantations, with results collected over three years, 2021, 2022 and 2023. Current climate data were used from the Worldclim database (version 2.0). The results obtained indicate that the north-western regions of the country, the eastern and central coasts, are highly suitable (≥75%), but the desert and steppe regions are weakly suitable (<25%). Therefore, a suitable bioclimatic environment for planting argan trees (with at least 50% probability) depends on a minimum temperature of 2.5 to 6.5°C and annual precipitation of 160 to 1,400 mm. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea is a climate change hotspot since it provides a magnified warming signal. Heavily populated areas (e.g., Spanish Mediterranean coasts) are vulnerable to negative socio-economic impacts. This is particularly important for climate-related economic sectors such as coastal tourism, the focus of this paper. To promote a sustainable development of these activities and provide key information to stakeholders, it is necessary to anticipate changes in climate. Thus, it is fundamental to use climate modelling tools which account for air-sea interactions, which largely determine the climate signal of the Mediterranean coasts. In this paper, a set of regional air-sea coupled climate model simulations from Med-CORDEX are used to (i) study the climatic conditions on the Spanish Mediterranean coasts in the next decade(s) and (ii) to assess the possibility of extending the coastal tourist season towards spring-fall. We show that climate conditions are getting warmer and drier in the area, especially in summer. Heat waves and heavy precipitation will become more frequent. Thermal discomfort will increase in summer and summer conditions are extending towards spring and fall. Our work remarks the urgent need of adaptation measures of the sector, including the extension of the high tourist season to spring-fall, especially in the long term. We make a special effort to compile a set of adaptation measures for stakeholders. This study is part of the project ECOAZUL-MED, which aims to create a climate service tool to optimize the management of relevant sectors of the blue economy in the Spanish Mediterranean coasts.

Climate models help us simulate and predict how the Earth's climate is going to change in the future. Wind speed data is critical for developing and validating such models. However, in the real world, often owing to many factors such as station maintenance and sensor failures, a considerable amount of wind data goes missing. The Generative Adversarial Network (GAN) has been used to impute missing wind data, but the handling of unrealistic GAN output has remained largely unstudied. In this paper, we propose a novel hybrid approach that combines both the GAN and dual annealing algorithms to not only impute missing wind speed data but also counter unrealistic GAN outcomes. The hourly mean wind data has been collected from the National Centers for Environmental Information for four Indian stations, viz. Ahmedabad, Indore, Mangaluru and Mumbai. We compared the performance of the proposed approach with those of k-nn, soft imputation, and plain GAN-based approaches on mean, variance, standard deviation, kurtosis, skewness, and R-square. We found that our approach ranks number one based on the R-square value for all the considered stations. Our model consistently produces realistic results, unlike plain GAN. We observed that Mumbai has the lowest percentage of missing data (13.14%) and the highest R-square value (0.9999186451). However, Indore has the highest percentage of missing data (46.6463%) and the lowest R-square value (0.9046885604). [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are the most sophisticated tools at our disposal for studying future climates, but there are limitations to overcome. These include resolutions that may be too coarse for impact assessments, limited or zero availability of some policy‐relevant scenarios, and limited time‐series length for assessing the risk of extreme events. We illustrate how these limitations can be addressed by combining a stochastic precipitation generator (SPG) with pattern scaling (PS) of its key parameters. Computationally inexpensive, SPG parameters can be perturbed to generate time‐series representative of weather under a future climate with high spatial and temporal resolution. If the SPG parameter perturbations are derived directly from GCM simulations projections can only be made for scenarios already simulated by the GCM. Instead, we obtain the parameter perturbations using PS, facilitating emulation of scenarios not necessarily explicitly simulated by the GCM, and where we scale perturbations approximately linearly with global temperature change. PS is commonly applied to estimate perturbations in the mean of climate variables, but rarely to higher‐order parameters as we demonstrate here. We apply PS for the first time, globally, to the parameters of a daily, first‐order Markov‐chain gamma‐distribution SPG using output from the IPSL‐CM6A‐LR GCM to perturb an SPG fitted to observed data from two stations in diverse climates (Santarém, Brazil and Reykjavik, Iceland) to illustrate this novel approach. We produce time series corresponding to a range of GWLs and demonstrate the capability of the combined SPG‐PS approach to study local‐scale, future daily precipitation characteristics, climate and subsequent risk of extreme weather events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A number of authors, including me, have argued that the output of our most complex climate models, that is, of global climate models and Earth system models, should be assessed possibilistically. Worries about the viability of doing so have also been expressed. I examine the assessment of the output of relatively simple climate models in the context of discovery and point out that this assessment is of epistemic possibilities. At the same time, I show that the concept of epistemic possibility used in the relevant studies does not fit available analyses of this concept. Moreover, I provide an alternative analysis that does fit the studies and broad climate modelling practices as well as meshes with my existing view that climate model assessment should typically be of real possibilities. On my analysis, to assert that a proposition is epistemically possible is to assert that it is not known to be false and is consistent with at least approximate knowledge of the basic way things are. I, finally, consider some of the implications of my discussion for available possibilistic views of climate model assessment and for worries about such views. I conclude that my view helps to address worries about such assessment and permits using the full range of climate models in it. [ABSTRACT FROM AUTHOR]

We introduce a flexible method to simultaneously infer both the drift and volatility functions of a discretely observed scalar diffusion. We introduce spline bases to represent these functions and develop a Markov chain Monte Carlo algorithm to infer, a posteriori, the coefficients of these functions in the spline basis. A key innovation is that we use spline bases to model transformed versions of the drift and volatility functions rather than the functions themselves. The output of the algorithm is a posterior sample of plausible drift and volatility functions that are not constrained to any particular parametric family. The flexibility of this approach provides practitioners a powerful investigative tool, allowing them to posit a variety of parametric models to better capture the underlying dynamics of their processes of interest. We illustrate the versatility of our method by applying it to challenging datasets from finance, paleoclimatology, and astrophysics. In view of the parametric diffusion models widely employed in the literature for those examples, some of our results are surprising since they call into question some aspects of these models. [ABSTRACT FROM AUTHOR]

The effect of anthropogenic climate change in the ocean is challenging to project because atmosphere-ocean general circulation models (AOGCMs) respond differently to forcing. This study focuses on changes in the Atlantic Meridional Overturning Circulation (AMOC), ocean heat content (Δ OHC), and the spatial pattern of ocean dynamic sea level ( Δ ζ ). We analyse experiments following the FAFMIP protocol, in which AOGCMs are forced at the ocean surface with standardised heat, freshwater and momentum flux perturbations, typical of those produced by doubling CO 2 . Using two new heat-flux-forced experiments, we find that the AMOC weakening is mainly caused by and linearly related to the North Atlantic heat flux perturbation, and further weakened by a positive coupled heat flux feedback. The quantitative relationships are model-dependent, but few models show significant AMOC change due to freshwater or momentum forcing, or to heat flux forcing outside the North Atlantic. AMOC decline causes warming at the South Atlantic-Southern Ocean interface. It does not strongly affect the global-mean vertical distribution of Δ OHC, which is dominated by the Southern Ocean. AMOC decline strongly affects Δ ζ in the North Atlantic, with smaller effects in the Southern Ocean and North Pacific. The ensemble-mean Δ ζ and Δ OHC patterns are mostly attributable to the heat added by the flux perturbation, with smaller effects from ocean heat and salinity redistribution. The ensemble spread, on the other hand, is largely due to redistribution, with pronounced disagreement among the AOGCMs. [ABSTRACT FROM AUTHOR]

Sea ice is an important and complex component of the Earth system and is considered a sensitive indicator of climate change. The seasonal sea ice cycle regulates the exchange of heat and salinity, altering the energy balance between high and low latitudes as well as the ocean and atmospheric circulation. The accurate representation of Antarctic sea ice has been considered a hot topic in the climate modelling community and lacks conclusive answers. In this paper, we evaluated the ability of 11 climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) to simulate the sea ice seasonal cycle in Antarctica in terms of area (SIA) and concentration (SIC), as well as the improvements in the most recent models' version, submitted to CMIP6. The results indicated that all models are able to accurately capture the seasonal cycle of the Antarctic SIA, with the minimum (maximum) occurring in February (September). In the Weddell Sea, Amundsen Sea, Bellingshausen Sea, and the Ross Sea, the simulated sea ice concentration revealed a large and systematic bias in February when compared to observations. In September, a large and systematic bias was found nearby the Southern Ocean's northern limit in the Polar Front. Several CMIP6 models exhibited slight improvements on the SIA and SIC estimate over the previous version (CMIP5). All models indicated a significant sea ice loss in the coming years as a response to CO2 forcing. Despite the advancements in the sea ice representation, our findings show that the models are still unable to accurately represent the regional sea ice changes [ABSTRACT FROM AUTHOR]

This work is devoted to the comparison of one- (1-D) and zero-dimensional (0-D) models of sea ice thermodynamics. 1-D thermodynamics solvers imply the solution of the diffusion equation with penetrating radiation in the moving domain (moving boundary problem), while 0-D implementations neglect the heat capacity of ice and penetrating radiation, that leads to a linear temperature profile by the construction. So far, some climate models use 0-D implementation of thermodynamics in a block of sea ice, while other models use 1-D implementation with a small number of nodes. In this work we present our fully implicit 0-D and 1-D version of snow–ice thermodynamics, which relieves the model of Courant-type time step constraints. Implicitness is achieved by the relaxation method for nonlinear heat capacity and thermal conductivity coefficients with simultaneous search for the temperature that satisfies the boundary conditions. We introduce the experiment with analytical forcing to compare evolution of temperature profiles. This model experiment shows that 0-D model underestimates ice thickness and overestimates the amplitude of temperature profile variation compared to 1-D solution. Despite the large differences in thickness and internal temperatures, the surface temperatures are very close. The models were also validated on the data of the SHEBA field experiment. According to comparison with observation data, the 0-D model predicts ice thickness and spatiotemporal temperature distribution much worse compared to 1-D model, but the amplitude of error in surface temperature is slightly greater for 0-D code. It can be concluded that 0-D model of sea ice thermodynamics is a good tool for predicting surface temperature, but it gives a large error in thickness and temperature distribution. [ABSTRACT FROM AUTHOR]

The South and East Asian summer monsoons are globally significant meteorological features, creating a strongly seasonal pattern of precipitation. The stability of such a strongly seasonal hydrological cycle is of extreme importance for a vast range of ecosystems and for the livelihoods of a large share of the world's population. Simulations are performed with an intermediate complexity climate model, PLASIM, to assess the future response of the monsoons to changing concentrations of aerosols and greenhouse gases. The aerosol loading consists of a mid-tropospheric warming and a surface cooling, which is applied to India, Southeast Asia and East China, both concurrently and independently. The primary effect of increased aerosol loading is a decrease in summer precipitation in the vicinity of the applied forcing, although the regional response varies significantly. The decrease in precipitation is only partially ascribable to a decrease in the precipitable water, and instead derives from a reduction of the precipitation efficiency, due to changes in the stratification of the atmosphere. When the aerosol loading is added in all regions simultaneously, precipitation in East China is most strongly affected, with a quite distinct transition to a low precipitation regime as the radiative forcing increases beyond 60 W/m2. The response is less abrupt as we move westward, with precipitation in South India being least affected. This lower sensitivity in South India is attributed to aerosol forcing over East China. Additionally, the effect on precipitation is approximately linear with the forcing. The impact of doubling carbon dioxide levels is to increase precipitation over the regions and weaken the circulation. When the carbon dioxide and aerosol forcings are applied at the same time, the carbon dioxide forcing partially offsets the surface cooling and reduction in precipitation associated with the aerosol response. [ABSTRACT FROM AUTHOR]

Gravity waves generated by mountains are multiscale and three‐dimensional. Current orographic gravity wave drag parametrisation schemes assume that the waves are two‐dimensional, varying only in the vertical and along one horizontal direction. These schemes, therefore, do not represent the process of partial critical‐level filtering, whereby a portion of the wave spectrum is saturated where the winds parallel to the wavevectors become small. This results in an unrealistic vertical distribution of the momentum flux and forcing of the waves on the mean flow. In this paper, a method of accounting for partial critical‐level filtering in an orographic gravity wave drag parametrisation using the full spectrum of realistic topography is presented. This is achieved through binning of the expression for linear hydrostatic surface stresses, computed using Fourier transforms of the subgrid orographic heights within model grid boxes, into wavevector directions. The parametrisation is compared with idealised nonlinear simulations of flow over complex topography and is shown to perform well as the number of wavevector direction bins is increased. Implementation of the scheme into the Met Office Unified Model is tested using short‐range 5‐day forecasts. As is found from idealised simulations, the binned scheme leads to less forcing in the troposphere and increased forcing in the stratosphere within the model. The binned scheme is shown to alleviate biases in the upper stratosphere, between 45 and 65 km, as well as having significant local effects in the troposphere. [ABSTRACT FROM AUTHOR]

Wind is an infinitely renewable energy source that is not evenly distributed in space and time. The interconnection of energy‐demanding and energy‐resourceful (yet remote) regions would help prevent energy scarcity in a world where fossil fuels are no longer used. Previous studies have shown that South Greenland and West Europe have complementary wind regimes. In particular, the southern tip of Greenland, Cape Farewell, has gained growing interest for wind farm development as it is one of the windiest places on Earth. In order to gain new insights about future wind speed variations over South Greenland, the Modèle Atmosphérique Régional (MAR), validated against in situ observations over the tundra where wind turbines are most likely to be installed, is used to build climate projections under the emission scenario SSP5‐8.5 by downscaling an ensemble of CMIP6 Earth System Models (ESMs). It appeared that between 1981 and 2100, the wind speed is projected to decrease by ~−0.8 m·s−1 at 100 m a.g.l. over the tundra surrounding Cape Farewell. This decrease is particularly marked in winter while in summer, a wind speed acceleration is projected along the ice sheet margins. An analysis of two‐dimensional wind speed changes at different vertical levels indicates that the winter decrease is likely due to a large‐scale circulation change while in summer, the katabatic winds flowing down the ice sheet are expected to increase due to an enhanced temperature contrast between the ice sheet and the surroundings. As for the mean annual maximum wind power a turbine can yield, a decrease of ~−178.1 W is projected at 100 m a.g.l. Again, the decrease is especially pronounced in winter. Considering the very high winter wind speeds occurring in South Greenland which can cut off wind turbines if too intense, the projected wind speed decrease might be beneficial for the establishment of wind farms near Cape Farewell. [ABSTRACT FROM AUTHOR]

This study evaluates the uncertainty of four components of the hydroclimatic modelling chain on flood projections over 96 basins covering contrasting hydrometeorological regimes located in Canada and Mexico. Two ensembles of climate simulations are considered, a large ensemble of 22 global climate model simulations and a smaller ensemble of three high-resolution regional climate model simulations. The other components are two post-processing techniques, three lumped hydrological models and six probability distributions. These four sources are assessed through a method of variance decomposition applied to six flood indicators over a reference period and two future periods: 1976–2005, 2041–2070 and 2070–2099. Systematic differences are observed between basins with contrasting flood-generating processes. Snow-dominated basins consistently show larger variance contributions from hydrological models, while rain-dominated basins show climate simulations as their dominant source. These results underline the need to consider the variability of each component's uncertainty contribution and its link to hydroclimatic conditions and dominant processes. [ABSTRACT FROM AUTHOR]

Aims: Our objective was to establish a relationship between long-term variation in the climatic environment, tree canopy decline and observed effects on the population dynamics of avifauna in the Dryandra Woodlands in southwestern Australia. These geographically isolated remnant woodlands are rich in endemic species and sustain a diverse range of ecological communities, but are threatened by habitat degradation and a decline in rainfall. Methods: We used annual rainfall data, averaged from a series of weather stations within 100 km of the Dryandra Woodlands and a time series analysis to investigate long-term changes in annual rainfall. Satellite spectral observations of eight study sites at Dryandra was used to measure changes in Projected Foliage Cover (PFC) of old growth Eucalyptus wandoo at all sites. Our mist-net trapping study across three years and all eight sites, targeted two focal species; the rufous treecreeper (Climacteris rufa) and yellow-plumed honeyeater (Ptilotula ornata). We investigated the relationship between the captures of each species and variation in PFC, between sites and across years. Also in a separate demographic study, capture-mark-recapture data was used to estimate the apparent survival rate of each species, following the robust design for open and closed populations. Key results: We demonstrate a long-term and continuing decline in average annual rainfall that is accelerating. We found the rainfall trend is concomitant with a long-term decline in PFC of E. wandoo and that the previous year's annual rainfall is a predictor of average PFC across all sites. Additionally, we discovered that the PFC at each site, in each year, is a predictor of the number of yellow-plumed honeyeaters which prefer feeding on canopy insects and not a predictor of the predominantly ground-foraging rufous treecreeper. We also found a substantial difference in the apparent survival rates between the two species, with the apparent survival of yellow-plumed honeyeaters being approximately half that of rufous treecreepers. This difference was partially attributed to the likely movement outside of the study area due to decreasing habitat quality. Conclusions and implications: Overall, our results do suggest that some impacts of long-term rainfall trends can be traced to particular species through PFC variation, but the response between species to habitat change will differ and depend on species-specific habitat requirements. As increasing greenhouse emissions are associated with declining rainfall in southwestern Australia, this study shows if rainfall decline and habitat degradation continue, it will have catastrophic consequences for woodland ecosystems. In this paper, we explorehow declining rainfall in southwestern Australia is impacting old growtheucalypts and resident avifauna of the Dryandra woodlands. Using long term dataon rainfall and satellite imagery of Eucalyptuswandoo canopy cover, results show a continuing declining trend inaverage rainfall that is accelerating. Also, we found that rainfall is a predictor oftree foliage cover, which is also declining over time. Our analysis of bird mist-net trapping data and tree canopy data indicate that foliagecover is a predictor of the number of yellow-plumed honeyeaters captured, butnot a predictor of rufous treecreepers. The apparent survival rate of the yellow-plumedhoneyeater was half that of the rufous treecreeper. [ABSTRACT FROM AUTHOR]

There is much debate on how social values should influence scientific research. However, the question of practical applicability of philosophers’ normative proposals has received less attention. Here, we test the attainability of Matthew J. Brown’s (2020) Moral Imagination ideal (MI ideal), which aims to help scientists to make warranted value-judgements through reflecting on goals, options, values, and stakeholders of research. Here, the tools of the MI ideal are applied to a climate modelling setting, where researchers are developing aerosol-cloud interaction (ACI) parametrizations in an Earth System Model with the broader goal of improving climate sensitivity estimation. After the identification of minor obstacles to applying the MI ideal, we propose two ways to increase its applicability. First, its tools should be accompanied with more concrete guidance for identifying how social values enter more technical decisions in scientific research. Second, since research projects can have multiple goals, examining the alignment between broader societal aims of research and more technical goals should be part of the tools of the MI ideal. [ABSTRACT FROM AUTHOR]

It is now well established that global to local climate is impacted by volcanic forcing associated with major eruptions. Much attention has been given to the way in which climate is affected by such eruptions, particularly for the Northern Hemisphere, and to a considerably lesser extent for the Southern Hemisphere. Research knowledge gaps remain on such forcing impacts at finer sub‐regional spatial scales. To this end, we explore temperature responses across eight sub‐regions of Australia following three major volcanic eruptions (i.e., Krakatau, 1883; Santa Maria, 1902; Pinatubo, 1991) as simulated by CMIP5 models. We then compare such responses with station and reanalysis datasets. Model outputs indicate strongest temperature responses over more northerly regions than southerly regions of Australia, with weakest responses over Tasmania. Eastern regions of Australia seem to have strongest seasonal cooling during austral autumn, while that for northwestern coastal regions is during austral winter. In contrast, central regions of Australia cool most substantially during austral summer and/or winter, depending on the eruption. Despite such variability, initial temperature responses occur during the warmer austral months (September to February). Uncertainties exist in the reliability of CMIP5 data. For instance, temperature responses from the later Pinatubo eruption seem in stronger agreement with reanalysis data than earlier eruptions (Krakatau and Santa Maria), and while most seasonal temperature responses are stronger than those provided through reanalysis data, they are often weaker in austral spring. [ABSTRACT FROM AUTHOR]

Philosophers argue that many choices in science are influenced by values or have value-implications, ranging from the preference for some research method's qualities to ethical estimation of the consequences of error. Based on the argument that awareness of values in the scientific process is a necessary first step to both avoid bias and attune science best to the needs of society, an analysis of the role of values in the physical climate science production process is provided. Model-based assessment of climate sensitivity is taken as an illustrative example; climate sensitivity is useful here because of its key role in climate science and relevance for policy, by having been the subject of several assessments over the past decades including a recent shift in assessment method, and because it enables insights that apply to numerous other aspects of climate science. It is found that value-judgements are relevant at every step of the model-based assessment process, with a differentiated role of non-epistemic values across the steps, impacting the assessment in various ways. Scrutiny of current philosophical norms for value-management highlights the need for those norms to be re-worked for broader applicability to climate science. Recent development in climate science turning away from direct use of models for climate sensitivity assessment also gives the opportunity to start investigating the role of values in alternative assessment methods, highlighting similarities and differences in terms of the role of values that encourage further study. [ABSTRACT FROM AUTHOR]

New mathematical results are given for the radiative transfer equations alone and coupled with the temperature equation of a fluid: existence, uniqueness, a maximum principle, and a convergent monotone iterative scheme. Numerical tests for Earth's atmosphere and the heating of a pool by the Sun are included. [ABSTRACT FROM AUTHOR]

The growth of the Qinghai-Tibetan Plateau (QTP) during the Cenozoic drove dramatic climate and environmental change in this region. However, there has been limited comprehensive research into evolution of climate during this interval. Here we present a quantitative reconstruction using Bioclimatic Analysis (BA) and Joint Probability Density Functions (JPDFs) based on data available for 48 fossil floras, including macrofossils and palynological fossils collected in the QTP area from the Paleogene to Neogene (66–2.58 Ma). Both methods indicate that there was an overall decline in temperature and precipitation. Paleoclimatic simulations using Hadley Centre Coupled Model version3 (HadCM3) show that the most prominent climate change was very likely driven by QTP orographic evolution from the late Eocene, which was accompanied by a shift in temperature from a latitudinal distribution to a topographically controlled pattern. In addition, with the growth of the QTP, temperature and precipitation decreased gradually in the northeastern part of the plateau. Different sources of evidence, including plant fossil records, climate simulations and other proxies, indicate that the topographic evolution of the QTP and other geological events, in conjunction with global cooling, may have been the main factors driving climate change in this region. This research can provide insights into Cenozoic environmental change and ecosystem evolution. [ABSTRACT FROM AUTHOR]