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This paper compares the ideas contained in the main papers published on climate change since World War II to arrive at a suggested consensus of our present knowledge regarding climatic changes and their causes. Atmospheric carbon dioxide is only suggested as a cause in one theory, which, despite its wide acceptance by Politicians, the media, and the Public, ignores the findings in other studies, including the ideas found in the Milankovitch Cycles. It also does not explain the well-known NASA map of the changes between the global 1951–1978 and the 2010–2019 mean annual temperatures. The other theories by Oceanographers, Earth scientists, and Geographers fit together to indicate that the variations in climate are the result of differential solar heating of the Earth, resulting in a series of processes redistributing the heat to produce a more uniform range of climates around the surface of the Earth. Key factors are the shape of the Earth and the Milankovitch Cycles, the distribution of land and water bodies, the differences between heating land and water, ocean currents and gateways, air masses, and hurricanes. Low atmospheric carbon dioxide levels during cold events could result in too little of this gas to support photosynthesis in plants, resulting in the extermination of most life on Earth as we know it. The 23 ka Milankovitch cycle has begun to reduce the winter insolation received at the surface of the atmosphere in the mid-latitudes of the Northern Hemisphere starting in 2020. This results in extreme weather as the winter insolation reaching the surface of the atmosphere in the higher latitudes of the Northern Hemisphere decreases while the summer air temperatures increase. It heralds the start of the next glaciation. A brief outline is given of some of the climatic changes and consequences that may be expected in western Canada during the next 11.5 ka. [ABSTRACT FROM AUTHOR]

A nonlinear dynamical model for the plankton population in a fixed sea area under the influence of asymmetric multiple factors, including atmospheric CO 2 concentration, atmospheric temperature, nutrient concentration, seawater temperature, light intensity, and predator density is proposed to address the survival of the plankton population due to global warming. The model's accuracy is confirmed by comparison with actual data, and numerical simulations are carried out to justify the relevant findings. The results suggest that increasing plankton's ability to absorb atmospheric CO 2 or regulate atmospheric temperature can help to mitigate global warming. Furthermore, if the population density of fish, the primary predator of plankton, falls within a certain range, the increase in atmospheric temperature will be mitigated. Additionally, the stability conditions for the suggested model are obtained, along with the equilibrium point of the system. Overall, this paper considers the effects of asymmetric multifactor interaction on plankton population density and establishes a mathematical connection between environmental ecosystems and plankton that might aid in addressing the challenges posed by global warming and preserving the plankton population. [ABSTRACT FROM AUTHOR]

In this paper, we show that the projected global temperature change from CMIP5 models is remarkably similar to that obtained using simple regression models relating the global temperature to the atmospheric concentrations of CO2. This result is strengthened when we consider the projections obtained using the CMIP6 models. [ABSTRACT FROM AUTHOR]

Climate model comparisons show that there is considerable uncertainty in the atmospheric temperature response to CO2 perturbation. The uncertainty results from both the rapid adjustment that occurs before SST changes and the slow feedbacks that occur after SST changes. The analysis in this paper focuses on the rapid adjustment.We use a novel method to decompose the temperature change in AMIP-type climate simulation in order to understand the adjustment at the process level. We isolate the effects of different processes, including radiation, convection, and large-scale circulation in the temperature adjustment, through a set of numerical experiments using a hierarchy of climate models. We find that radiative adjustment triggers and largely controls the zonal mean atmospheric temperature response pattern. This pattern is characterized by stratospheric cooling, lower-tropospheric warming, and a warming center near the tropical tropopause. In contrast to conventional views, the warming center near the tropopause is found to be critically dependent on the shortwave absorption of CO2. The dynamical processes largely counteract the effect of the radiative process that increases the vertical temperature gradient in the free troposphere. The effect of local convection is to move atmospheric energy vertically, which cools the lower troposphere and warms the upper troposphere. The adjustment due to large-scale circulation further redistributes energy along the isentropic surfaces across the latitudes, which cools the low-latitude lower troposphere and warms the midlatitude upper troposphere and stratosphere. Our results highlight the importance of the radiative adjustment in the overall adjustment and provide a potential method to understand the spread in the models. [ABSTRACT FROM AUTHOR]

Increasing atmospheric carbon dioxide (CO2) is the main factor of global warming. Carbon satellites have proven that CO2 concentrations have nonuniform spatio‐temporal distributions. The relationship between unevenly distributed CO2 and global surface air temperature (SAT) is seldom known. The success of complex networks provides an opportunity to address this issue. This paper proposes a multilayer climate network approach to identify the impacts of nonuniform CO2 on SAT. The results show that the probability density function (PDF) of degrees, weighted degrees, and link lengths follows power‐law distributions. A large fraction of strong correlation links resides in proximal distance (smaller than 2,000 km), indicating that CO2 nodes are strongly connected to the surrounding SAT nodes. The enhanced distributions of large positive weights, time delays, and degree patterns are all consistent with the properties of Rossby waves. This framework can be useful for predicting future climate changes and policy‐making for carbon reduction. Plain Language Summary: Carbon dioxide (CO2) is a prominent anthropogenic greenhouse gas in the atmosphere, and the increasing CO2 is the main reason for global climate warming. The CO2 concentration from satellites has revealed that the spatial distribution of CO2 is uneven. In previous studies, the relationship between CO2 and temperature is based on global CO2 concentration mean assumptions. Here, we propose a multilayer climate network approach to identify their relations using satellite data. We find that the PDF of degrees, weighted degrees, and link lengths follows power‐law distributions. The most dominant links, with a geographical distance larger than 2,000 km, are found for distances of 7,000 km, coinciding with the wavelengths of atmospheric Rossby waves. The time delays associated with these distances are in agreement with the group velocity of the atmospheric Rossby waves. Moreover, the pronounced band over 50°S, the dominance in the Southern Hemisphere relative to the Northern Hemisphere, and the similar patterns to transient heat flux are consistent with the properties of Rossby waves. The proposed approach leads to a better understanding of the role played by CO2 in forcing global temperatures and future climate change. Key Points: There are short‐term correlations between global mid‐troposphere CO2 concentrations and global surface air temperatureThe degrees, weighted degrees, and link lengths of CO2 and SAT multilayer climate network are conformed to power‐law distributionsSignificant correlation links of the constructed climate network yield a clear association with Rossby waves [ABSTRACT FROM AUTHOR]

In this paper, a novel data-driven approach is used to investigate the presence of spatial differences in the dynamic linkage between temperature and atmospheric carbon dioxide concentrations. This linkage seems to be latitude dependent. The main findings of the study are as follows. In the latitude belts surrounding the equator (0°− 24° N and 0°− 24° S), the link seems very similar. On the opposite, the patterns of the temperature CO2 link in the Arctic is very distant from those concerning the equatorial regions and other latitude bands in the South Hemisphere. This big distance is consistent with the so-called Arctic amplification phenomenon. Further, it is important to underline that this observational data-based analysis provides an independent statistical confirmation of the results from global circulation modelling. [ABSTRACT FROM AUTHOR]

Carbon dioxide concentration is a major factor in air quality, animal welfare and air exchange rates inside livestock buildings. CO 2 concentration series show a dynamic, non-linear and non-stationary behavior. This type of process can be handled by Wavelet Neural Network (WNN) models, which have been developed in recent years. The purpose of this paper is to develop WNN models capable of predicting the dynamics of CO 2 in weaner buildings. Outdoor temperatures, CO 2 concentration and temperature in the animal zone and animal activity were recorded in a commercial piglet farm during two complete production cycles. Two WNN models were generated from the recorded data: an autoregressive model (AM) that used the CO 2 series and outdoor temperatures for the prediction, and an explanatory model (EM) that used only exogenous variables, namely outdoor temperature, temperature in the animal zone and animal activity. Because CO 2 is a highly autoregressive variable, the best results are obtained with the AM. The AM yield an RMSE of 26.330 ppm and a Pearson’s r of 0.995. The EM, with an RMSE of 154.361 ppm and a Pearson’s r of 0.895, reveal the importance of indoor and outdoor temperatures and, consequently, of ventilation rate, for CO 2 concentration inside the building. In addition, our results show the effects of animal activity on CO 2 concentration, which are delayed by 40–50 min. Based on these results, the CO 2 concentrations in the animal zone of weaner buildings can be accurately predicted by WNN models. Therefore, WNN modeling could be widely used to predict and understand the dynamics of environmental variables inside livestock buildings. [ABSTRACT FROM AUTHOR]

The kinetic temperature, T k , and carbon dioxide, CO 2 density, are key parameters that characterize the energetics and dynamics of the mesosphere and lower thermosphere (MLT) region. The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on-board the Thermosphere-Ionosphere-Mesosphere-Energetics and Dynamics (TIMED) satellite has been providing global, simultaneous measurements of limb radiance in 10 spectral channels continuously since late January 2002. In this paper we (1) present a methodology for a self-consistent simultaneous retrieval of temperature/pressure, T k ( p ), and CO 2 volume mixing ratio (VMR) from the broadband infrared limb measurements in the 15 and 4.3 μ m channels, and (2) qualitatively describe the first results on the CO 2 VMR and T k obtained from application of this technique to the SABER 15 and 4.3 μ m channels, including issues, which demand additional constraints to be applied. The self-consistent two-channel retrieval architecture updates parameters at all altitudes simultaneously, and it is built upon iterative switching between two retrieval modules, one for CO 2 and one for T k . A detailed study of sensitivity, stability and convergence was carried out to validate the algorithm. The T k /CO 2 VMR distribution can be reliably retrieved without biases connected with this non-linear inverse problem starting with an initial guess as far as ±20% of CO 2 VMR and ±15 K from the solution (as global shift, or somewhat larger if only local deviations are considered). In polar summer toward high latitudes the retrieved CO 2 VMR profile shows a local peak around 90 km. We discuss details of this feature and show that: (a) it is not an algorithm artifact or instability, (b) additional a priori constraints are needed in order to obtain a physical profile and to remove this peak, and (c) several possibilities are explored as to uncover the real cause of this feature, but no firm conclusion can be reached at this time. This algorithm has been applied to all available daytime SABER measurements since 2002, and the first results of the mean CO 2 VMR profiles and their distribution is discussed. In particular, the CO 2 VMR profiles depart from a well mixed value at altitudes of 65–70 km during equinoxes at high and mid-latitudes, but in the summer hemisphere solstice period the SABER data is more consistent with a well mixed VMR conditions extend up to 87–90 km especially toward high latitudes. [ABSTRACT FROM AUTHOR]

Venus is physically similar to Earth but with no oceans and a hot dense atmosphere. Its near-random distribution of impact craters led to the inferences of episodic global resurfacing and a stagnant lid regime, and imply that it is not currently able to lose proportionately as much heat as Earth. This paper shows that a CO 2 -induced asthenosphere and decoupling of the mantle lid from the crust, caused by the elevated surface temperature, enables lid rejuvenation. Global hypsography implies a rate of 4·0±0·5 km² a −1 and an implied heat loss rate of 32·8±3·6 TW, ~90% of a scaled Earth-like rate of heat loss of 36 TW. Estimates of the rate of lid rejuvenation by plume activity – 0·07 to 0·09 km² a −1 – imply that ten times the number of observed plumes are required to equal this rate of heat loss. However, lid rejuvenation by convection allows Venus to maintain a stable tectonic regime, with subcrustal horizontal extension (half-spreading) rates of between 25 and 50 mm a −1 determined from fits to topographic profiles across the principal rift systems. While the surface is largely detached from these processes, the association of rifting and other processes does imply that Venus is geologically active at the present day. [ABSTRACT FROM AUTHOR]

Climate changes are connected to long term variations of global and hemispheric temperatures, which are important for the work out of socio-political strategy for the near future. In the paper the annual temperature time series are modeled by linear multiple regression to identify important climate forcings including external climate factors such as atmospheric CO 2 content, volcanic emissions, and the total solar irradiation as well as internal factors such as El Niño-Southern oscillation, Pacific decadal oscillation and Atlantic multidecadal oscillation. Adjusted temperatures were determined by removal of all significant influences except CO 2 . The adjusted temperatures follow a linear dependence toward the logarithm of the CO 2 content, and the coefficient of determination is about 0.91. The evolution of the adjusted temperatures suggests that the warming due to CO 2 from the beginning of the studied here time interval in 1900 has never stopped and is going on up to now. The global warming rate deduced from the adjusted temperatures since 1980 is about 0.14 ± 0.02 °C/decade. The warming rate reported in the IPCC assessment report 4 based on observed global surface temperature set is about 20% higher, due to the warming by the Atlantic multidecadal oscillation additional to the anthropogenic warming. The predicted temperature evolution based on long time changes of CO 2 and the Atlantic multidecadal oscillation index shows that the Northern Hemispheric temperatures are modulated by the Atlantic multidecadal oscillation influence and will not change significantly to about 2040, after that they will increase speedily, just like during the last decades of the past century. The temperatures of the Southern Hemisphere will increase almost linearly and don’t show significant periodic changes due to Atlantic multidecadal oscillation. The concrete warming rates of course are strongly depending on the future atmospheric CO 2 content. [ABSTRACT FROM AUTHOR]

The relevance and role of aesthetic, thermal and physical facets to climatic tourism potential over Algeria at 12.00 UTC (13.00 local time) are analyzed. This role is revealed using the Climate-Tourism-Information-Scheme (CTIS) elaborated for six Algerian cities having indisputable tourism potentiality and representing different regions and climate regimes: Coastline, Highland and Sahara zone. In this paper, the relevance of the use of the 10-day time-scale resolution of the physiologically equivalent temperature (PET) derived from the body-atmosphere energy balance as well as the importance of the CTIS at this time-scale resolution is illustrated. The CTIS diagrams produced concern the period 1986–2015 and comprise detailed information on the frequencies of the occurrence of different climatological and human biometeorological parameters throughout the year, which could help tourists to plan ahead their vacations. A significant upward trend in discomfort conditions, as well as a substantial decline in human thermal comfort acceptability, are observed during the period of the study. The obtained results could help enormously in the promotion of tourism and leisure services in the country as a highly promising economic sector in the future and those who are involved, such as tour operators, by diversifying their products through the provision of alternative types of tourism offerings in line with the climate conditions. [ABSTRACT FROM AUTHOR]

Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]