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Annually averaged time series of differenced temperatures (UAH) and logarithms of CO₂ concentrations (Mauna Loa). Each dot represents the average of a one-year duration ending at the time of its abscissa.

Annually averaged time series of differenced temperatures (UAH) and logarithms of CO₂ concentrations (Mauna Loa). Each dot represents the average of a one-year duration ending at the time of its abscissa.

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It is common knowledge that increasing CO 2 concentration plays a major role in enhancement of the greenhouse effect and contributes to global warming. The purpose of this study is to complement the conventional and established theory, that increased CO 2 concentration due to human emissions causes an increase in temperature, by considering the rev...

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... Cost reductions and global adoptions of low-emissions technologies are mainly attributed to innovation policies pursued [27]. Nationally determined contributions announced before COP26 indicated that it was likely that warming will exceed 1.5 • C during the 21st century, and recent contributions in the literature also discuss and put these problems at the forefront [2,3,7,8,24,28,29]. In [2] it was concluded that the number of deaths increased significantly with the repetition of extreme weather events. ...
... Accounting for the last 425 million years, [8] points to a pressing need for research on the relationship between CO 2 , biodiversity extinction, and related carbon policies, concluding that changes in emissions did not cause a temperature change in the ancient climate. Additionally, [24] looks at both cause and causality relating to the "hen-or-egg" effect. Results support the hypothesis that the dominant direction is from temperature to CO 2 emissions (1980-2019). ...
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The connection between Earth’s global temperature and carbon dioxide (CO2) emissions is one of the highest challenges in climate change science since there is some controversy about the real impact of CO2 emissions on the increase of global temperature. This work contributes to the existing literature by analyzing the relationship between CO2 emissions and the Earth’s global temperature for 61 years, providing a recent review of the emerging literature as well. Through a statistical approach based on maximum entropy, this study supports the results of other techniques that identify a positive impact of CO2 in the increase of the Earth’s global temperature. Given the well-known difficulties in the measurement of global temperature and CO2 emissions with high precision, this statistical approach is particularly appealing around climate change science, as it allows the replication of the original time series with the subsequent construction of confidence intervals for the model parameters. To prevent future risks, besides the present urgent decrease of greenhouse gas emissions, it is necessary to stop using the planet and nature as if resources were infinite.
... The impacts on various aspects of the ecosystem (van Nes et al 2015, Deryng et al 2016, Demirhan 2020 and socio-economic patterns (Deryng et al 2016, Appiah et al 2018 have been studied from many perspectives using different approaches. Several studies have sought to understand the causality within this interaction using either controlled numerical simulations (for instance, Friedlingstein et al (2001) and Seneviratne et al (2013)) or data-driven approaches (for instance, Attanasio (2012) and Koutsoyiannis and Kundzewicz (2020)), both of which have their strengths and limitations. Based on these approaches, previous studies have explored the multiple temporal characteristics of the interaction (Faes et al 2017) in historical records, ranging from paleoclimate timescales (Stips et al 2016, Barral et al 2017 to annual and 6 month timescales , and even to daily timescales (Kotz et al 2021). ...
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Two centuries of studies have demonstrated the importance of understanding the interaction between the air temperature and carbon dioxide (CO2) emissions, which can impact the climate system and human life in various ways, and across different timescales. While historical interactions have been consistently studied, the nature of future interactions and the impacts of confounding factors still requires more investigation in keeping with the continuous updates of climate projections to the end of the 21st century. The Phase 6 of the Coupled Model Intercomparison Project (CMIP6), like its earlier projects, provides ScenarioMIP multi-model projections to assess the climate under different radiative forcings ranging from a low-end (SSP1-2.6) to a high-end (SSP5-8.5) pathway. In this study, we analyze the localized causal structure of CO2, and near-surface mean air temperature (meanT) interaction for four scenarios from three CMIP6 models using a rigorous multivariate information flow (IF) causality, which can separate the cause from the effect within the interaction (CO2-meanT and meanT-CO2) by measuring the rate of IF between parameters. Firstly, we obtained patterns of the CO2 and meanT causal structures over space and time. We found a contrasting emission-based impact of soil moisture and vegetation (LAI) changes on the meanT-CO2 causal patterns. That is, soil moisture influenced CO2 sink regions in SSP1-2.6 and source regions in SSP58.5, and vice versa found for LAI influences. On the other hand, they similarly function to constrain the future CO2 impact on meanT. These findings are essential for improving long-term predictability where the climate models might be limited.
... As an illustration of our framework applicable to empirical data, we use climate data gathered at Mauna Loa Observatory, CO 2 content and local temperatures [43][44][45]. For clarity, we are not attempting to examine whether carbon dioxide content is driving temperature, or vice versa, but to show that the consensus problem can be identified in data coming from a dynamical system whose dynamics need not be known. ...
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... In order to investigate the potential for survival in the current crisis, we can focus on the unexpected and unfortunate real-world experiment: the COVID-19 lockdown in 2020, which caused an unprecedented decrease in carbon emissions that characterize energy consumption [159], leading energy prices to fall. ...
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... Convergent cross mapping, which is applied to the 800 ka recordings in another study, finds a bidirectional causal influence between both CO 2 -T and CH 4 -T 93 . Another recent study, that infers causation using lagged cross-correlations between monthly CO 2 and temperature, taken from the period 1980-2019, has found a bidirectional relationship on the recent monthly scale, with the dominant influence being from T → CO 2 94 . In the light of the limitations of CCM 95,96 , especially for irregularly sampled or missing data 42 , and of the widely known pitfalls of correlation coefficient 97 , it is difficult to rely on the inferences of the latter two studies. ...
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... Based on such understanding, fossil fuels should be gradually replaced by renewable energy sources that do not result in significant CO 2 emissions, and nuclear energy, in spite of the well-known concerns, has been now reconsidered in that light as a "clean" or "green" energy option. However, the assumption of cause-effect relation between CO 2 and global warming is not so clear, as there is also a reverse causality process where temperature increases also cause CO 2 increases at various temporal scales [1]. Moreover, CO 2 concentrations in the atmosphere are very homogeneous around the globe whereas temperature anomalies are spatially quite variable with urban or regional "heat islands" indicating that other factors also play a significant role. ...
... On the other hand, Koutsoyiannis and Kundzewicz (2020) asserted that: ...
... But if indeed the system dynamics were of low dimensionality, it would be preferable to model the system by deduction, rather than induction based upon doubtful statistical techniques. As pointed out by Koutsoyiannis and Kundzewicz (2020) (also referring to Koutsoyiannis, 2006), A more satisfactory framework was proposed by Hannart et al. (2016), based on the works by Pearl (2009) and Pearl et al. (2016). In it they used the so-called causal graph reflecting the assumed dependencies among the studied variables along with the notion of exogeneity (perhaps borrowed from Wold (1960), and Strotz and Wold (1960)). ...
... However, the framework has several drawbacks and can fail, as illustrated by the following counter-example by Koutsoyiannis and Kundzewicz (2020): When the atmospheric temperature is high people wear light clothes and also sweat much more than when it is cold. Thus, the weight of clothes improves the prediction of the sweat quantity. ...
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... Namely, in studies #23 -#28 we investigate the links between atmospheric temperature and CO₂ concentration (cf. Koutsoyiannis and Kundzewicz, 2020) and in studies #29 -#30 we investigate the links between atmospheric temperature and El-Niño Southern Oscillation (ENSO; cf. Kundzewicz et al., 2020). ...
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In a companion paper, we develop the theoretical background of a stochastic approach to causality with the objective of formulating necessary conditions that are operationally useful in identifying or falsifying causality claims. Starting from the idea of stochastic causal systems, the approach extends it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. The framework developed is applicable to large-scale open systems, which are neither controllable nor repeatable. In this paper, we illustrate and showcase the proposed framework in a number of case studies. Some of them are controlled synthetic examples and are conducted as a proof of applicability of the theoretical concept, to test the methodology with a priori known system properties. Others are real-world studies on interesting scientific problems in geophysics, and in particular hydrology and climatology.
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... Судлаачдын дүгнэж байгаагаар хавар IV сард агаарын температур (зураг 2-аас харахад) тэг градусаас дээш гарахад хөрсний температур ч мөн өсөж түүнд агуулагдах органик карбоны ялгарал өсдөг (Diallo et al., 2017). Хөрсөнд агуулагдах органик карбоны ялгарал нь температур өсөхөд дагаад өсдөг (Koutsoyiannis & Kundzewicz, 2020) хэдий ч ургамлын фотосинтезийн процесс V сараас нэмэгдсэнээр агаарт агуулагдах CO₂ -г шингээх нь үйл явц нь ялгаралтай харьцуулахад давамгайлдаг. Иймээс зуны улиралд CO₂ хамгийн бага агууламж VIII сард ажиглагдах үндсэн шалтгаан болдог. ...
... Энэ хандлага бидний судалгааны үр дүнтэй таарч байна. Гэвч хүний үйл ажиллагаанаас үүдэлтэй нүүрсхүчлийн хийн ялгарал нь дэлхийн нүүрсхүчлийн массын тэнцвэрт байдлыг алдагдуулсаар байгааг (Koutsoyiannis & Kundzewicz, 2020) тэмдэглэсэн байдаг. ...
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Уур амьсгалын дулаарлын гол шалтгаан болоод байгаа нүүрсхүчлийн хийн агууламжийг бууруулахад түүний жилийн доторх явцыг тодорхойлж, нөлөөлөх хүчин зүйлийг судлах нь чухал. Бид энэхүү судалгаагаар Монгол орны агаар дахь нүүрсхүчлийн хийн агууламжийн сарын дундаж утгыг авч үзэж ‘MODIS’ ‘NDVI’, агаарын температур, салхины хурднаас хэрхэн хамаарч байгааг корреляцийн коэффициентоор тооцов. Судалгааны үр дүнгээс үзвэл нүүрсхүчлийн хийн агууламжийн жилийн явц нь ‘NDVI’ (R=-0.88, p<0.001) болон агаарын температурынхтай урвуу хамааралтай (R=-0.67, p<0.05), салхины хурдныхтай шууд хамааралтай (R=0.91, p<0.001) байлаа. Нүүрсхүчлийн хийн агууламж нь улирлын хэлбэлзэлтэйгээс гадна ургамал бүрхэвч нь байгалийн нүүрстөрөгчийн ялгарал болон шингээлтэд гол үүргийг гүйцэтгэдэг төдийгүй нүүрсхүчлийн хийн хуурай газрын улирлын хэлбэлзлийг илэрхийлэх хүчин зүйл болж байна. Мөн судалгааны хугацааны хандлагыг Тейл-Сенийн налуугаар тооцоход нүүрсхүчлийн хийн агууламж (Q=2.32 ppm/жил, p<0.001), ‘NDVI’ (Q=0.002 нэгж/жил, p<0.05), агаарын температур (Q=0.2°C/жил, p<0.01) болон салхины хурд (Q=0.01 м/с/жил, p>0.05) нь сүүлийн 11 жилд өссөн хандлагатай гарсан. Гэхдээ статистик ач холбогдлыг харьцуулж үзвэл агаарын температур нь нүүрсхүчлийн хийн агууламжтай харьцуулахад өндөр биш. ‘NDVI’-нь нүүрсхүчлийн хийн агууламж болон агаарын температуртай харьцуулахад статистик ач холбогдол бага байгаа бол салхины хурд нь өссөн хэдий ч статистик ач холбогдолгүй байна. Улирлаар авч үзвэл нүүрсхүчлийн хийн агууламж бүхий л улиралд өссөн. Нүүрсхүчлийн агууламжийн энэ өсөлт нь хаврын улирлын температурт нөлөөлж байгаагаас гадна ургамал эрт ургаж эхлэх нөхцөлийг бүрдүүлсэн. Үүний нөлөөгөөр судалгааны хугацаанд хавар ‘NDVI’ утга статистик ач холбогдолтой өссөн хандлагатай гарлаа. Агаарын температур мөн адил судалгааны хугацаанд өссөн хандлагатай байна. Улирлаар нь авч үзвэл хаврын улиралд статистик ач холбогдолтой өссөн бол намрын улиралд буурчээ. Салхины хурдны хувьд хаврын улирлаас бусад хугацаанд өссөн хандлагатай боловч статистикийн хувьд ач холбогдолгүй гарсан.