In the past, the planetary radiation balance served to quantify the atmospheric greenhouse effect by the difference between the globally averaged near-surface temperature of and the respective effective radiation temp...In the past, the planetary radiation balance served to quantify the atmospheric greenhouse effect by the difference between the globally averaged near-surface temperature of and the respective effective radiation temperature of the Earth without atmosphere of resulting in . Since such a “thought experiment” prohibits any rigorous assessment of its results, this study considered the Moon as a testbed for the Earth in the absence of its atmosphere. Since the angular velocity of Moon’s rotation is 27.4 times slower than that of the Earth, the forcing method, the force-restore method, and a multilayer-force-restore method, used in climate modeling during the past four decades, were alternatively applied to address the influence of the angular velocity in determining the Moon’s globally averaged skin (or slab) temperature, . The multilayer-force-restore method always provides?the highest values for , followed by the force-restore method and the forcing method, but the differences are marginal. Assuming a solar albedo of , a relative emissivity , and a solar constant of and applying the multilayer-force-restore method yielded and for the Moon. Using the same values for α, ε, and S, but assuming the Earth’s angular velocity for the Moon yielded and quantifying the effect of the terrestrial atmosphere by . A sensitivity study for a solar albedo of commonly assumed for the Earth in the absence of its atmosphere yielded , , and . This means that the atmospheric effect would be more than twice as large as the aforementioned difference of 33 K. To generalize the findings, twelve synodic months (i.e., 354 Earth days) and 365 Earth days, where , a Sun-zenith-distance dependent solar albedo, and the variation of the solar radiation in dependence of the actual orbit position and the tilt angle of the corresponding rotation axis to the ecliptic were considered. The case of Moon’s true angular velocity yielded and . Whereas Earth’s 27.4 times higher angular velocity yielded , and . In both cases, the effective radiation temperature is ,?because the computed global albedo is . Thus, the effective radiation temperature yields flawed results when used for quantifying the atmospheric greenhouse effect.展开更多
The solar climate of our Moon is analyzed using the results of numerical simulations and the recently released data of the Diviner Lunar Radiometer Experiment (DLRE) to assess (a) the resulting distribution of the sur...The solar climate of our Moon is analyzed using the results of numerical simulations and the recently released data of the Diviner Lunar Radiometer Experiment (DLRE) to assess (a) the resulting distribution of the surface temperature, (b) the related global mean surface temperature T<sub>s</sub>>, and (c) the effective radiation temperature T<sub>e</sub> <sub></sub>often considered as a proxy for T<sub>s</sub>> of rocky planets and/or their natural satellites, where T<sub>e</sub> <sub></sub>is based on the global radiation budget of the well-known “thought model” of the Earth in the absence of its atmosphere. Because the Moon consists of similar rocky material like the Earth, it comes close to this thought model. However, the Moon’s astronomical features (e.g., obliquity, angular velocity of rotation, position relative to the disc of the solar system) differ from that of the Earth. Being tidally locked to the Earth, the Moon’s orbit around the Sun shows additional variation as compared to the Earth’s orbit. Since the astronomical parameters affect the solar climate, we predicted the Moon’s orbit coordinates both relative to the Sun and the Earth for a period of 20 lunations starting May 24, 2009, 00:00 UT1 with the planetary and lunar ephemeris DE430 of the Jet Propulsion Laboratory of the California Institute of Technology. The results revealed a mean heliocentric distance for the Moon and Earth of 1.00124279 AU and 1.00166376 AU, respectively. The mean geocentric distance of the Moon was 384792 km. The synodic and draconic months deviated from their respective means in a range of -5.7 h to 6.9 h and ±3.4 h, respectively. The deviations of the anomalistic months from their mean range between -2.83 d and 0.97 d with the largest negative deviations occurring around the points of inflection in the curve that represents the departure of the synodic month from its mean. Based on the two successive passages of the Sun through the ascending node of the lunar equator plane, the time interval between them corresponds to 347.29 days, i.e., it is slightly longer than the mean draconic year of 346.62 days. We computed the local solar insolation as input to the multilayer-force restore method of Kramm et al. (2017) that is based on the local energy budget equation. Due to the need to spin up the distribution of the regolith temperature to equilibrium, analysis of the model results covers only the last 12 lunations starting January 15, 2010, 07:11 UT1. The predicted slab temperatures, T<sub>slab</sub>, considered as the realistic surface temperatures, follow the bolometric temperatures, T<sub>bol</sub>, acceptably. According to all 24 DLRE datasets related to the subsolar longitude ø<sub>ss</sub>, the global averages of the bolometric temperature amounts to T<sub>bol</sub>=201.1k± 0.6K. Based on the globally averaged emitted infrared radiation of F<sub>IR</sub>>=290.5W·m<sup>-2</sup>± 3.0W·m<sup>-2</sup> derived from the 24 DLRE datasets, the effective radiative temperature of the Moon is T<sub>e, M</sub>>=T<sub>bol>1/4</sub>=271.0k± 0.7K so that T<sub>bol</sub>>≅0.742T<sub>e, M</sub>. The DLRE observations suggest that in the case of rocky planets and their natural satellites, the globally averaged surface temperature is notably lower than the effective radiation temperature. They differ by a factor that depends on the astronomical parameters especially on the angular velocity of rotation.展开更多
Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species.However,it is quite diffi...Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species.However,it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes.To solve these issues,we propose a transfer learning-based technique in which we use Efficient-Net,which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database,which is a large scale dataset.Furthermore,prior to the activation layer,we use Global Average Pooling(GAP)instead of dense layer with the aim of averaging the results of predictions along with having more information compared to the dense layer.To check the validity of our model,we validate our model on the validation set which achieves satisfactory results.Also,for the localization task,we propose an architecture that consists of localization aware block,which captures localization information for better prediction and residual connections to handle the over-fitting problem.Actually,the residual connections help the layer to combine missing information with the relevant one.In addition,we use class weights and Focal Loss(FL)to handle class imbalance problems along with reducing false predictions.Actually,class weights assign less weights to classes having fewer instances and large weights to classes having more number of instances.During the localization,the qualitative assessment shows that we achieve 57%Mean Intersection Over Union(IoU)on testing data,and the classification results show 75%precision,70%recall,78%accuracy and 74%F1-Score for 468 fish species.展开更多
The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation...The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.展开更多
When compared to the average annual global temperature record from 1880, no published climate model posited on the assumption that the increasing concentration of atmospheric carbon dioxide is the driver of climate ch...When compared to the average annual global temperature record from 1880, no published climate model posited on the assumption that the increasing concentration of atmospheric carbon dioxide is the driver of climate change can accurately replicate the significant variability in the annual temperature record. Therefore, new principles of atmospheric physics are developed for determining changes in the average annual global temperature based on changes in the average atmospheric concentration of water vapor. These new principles prove that: 1) Changes in average global temperature are not driven by changes in the concentration of carbon dioxide;2) Instead, autonomous changes in the concentration of water vapor, <span style="white-space:nowrap;">Δ</span>TPW, drive changes in water vapor heating, thus, the average global temperature, <span style="white-space:nowrap;">Δ</span>T<sub>Avg</sub>, in accordance with this principle, <span style="white-space:normal;"><span style="white-space:nowrap;">Δ</span>T</span><span style="white-space:normal;"><sub>Avg</sub>=0.4<span style="white-space:normal;"><span style="white-space:nowrap;">Δ</span>TPW </span></span>the average accuracy of which is ±0.14%, when compared to the variable annual, 1880-2019, temperature record;3) Changes in the concentration of water vapor and changes in water vapor heating are not a feedback response to changes in the concentration of CO<sub>2</sub>;4) Rather, increases in water vapor heating and increases in the concentration of water vapor drive each other in an autonomous positive feedback loop;5) This feedback loop can be brought to a halt if the average global rate of precipitation can be brought into balance with the average global rate of evaporation and maintained there;and, 6) The recent increases in average global temperature can be reversed, if average global precipitation can be increased sufficiently to slightly exceed the average rate of evaporation.展开更多
<p> A. <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">Changes </span></span></span><...<p> A. <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">Changes </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in</span></span></span><span><span><span style="font-family:" color:black;"=""><span style="font-family:Verdana;"> average global temperature are not driven by changes in the concentration of carbon dioxide;</span></span></span></span> </p> <p> <span style="font-family:Verdana;">B. </span><span style="font-family:Verdana;">Instead, autonomous changes in the concentration of water vapor, </span><span style="font-family:Verdana;">Δ</span><span style="font-family:Verdana;">TPW, </span><span color:black;"=""><span style="font-family:Verdana;">drive changes in water vapor heating, thus, </span><span style="background:#C00000;font-family:Verdana;">changes in</span><span style="font-family:Verdana;"> the average global temperature, </span></span><span style="font-family:Verdana;">Δ</span><span style="font-family:Verdana;"><i>T</i></span><span style="font-family:Verdana;"><sub>Avg</sub></span><span color:black;"=""><span style="font-family:Verdana;">, </span><span style="background:#C00000;font-family:Verdana;">in deg. Celsius are calculated</span><span style="font-family:Verdana;"> in accordance with this principle,</span></span> </p> <p style="text-align:center;margin-left:10pt;"> <span><span><span style="font-family:" color:black;"=""><span style="font-family:Verdana;"></span><img src="Edit_6e770969-a7c9-4192-a6ad-03de906a4d65.bmp" alt="" /><br /> </span></span></span> </p> <p align="center" style="margin-left:10.0pt;text-align:center;"> <span><span><span style="font-family:;" "=""><span></span></span></span><span><span><span style="font-family:" color:black;"=""></span></span></span></span> </p> <p> <span><span><span style="font-family:" color:black;background:#c00000;"=""><span style="font-family:Verdana;">measured in kg<span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#f7f7f7;"=""><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#f7f7f7;"="">·</span></span>m</span><sup><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#ffffff;"="">-</span>2</span></sup><span style="font-family:Verdana;">,</span></span></span></span><span><span><span style="font-family:" color:black;"=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the average accuracy of which is ±0.14%, when compared to the variable annual, 1880 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-</span></span></span><span><span><span style="font-family:" color:black;"=""><span style="font-family:Verdana;"> 2019, </span><span style="background:#C00000;font-family:Verdana;">average global </span><span style="font-family:Verdana;">temperature record;</span></span></span></span> </p>展开更多
Ten years after nearly 200 nations reached the Paris Agreement to address the impact of climate change,the international community is now at a critical juncture.The landmark agreement aims to keep the rise in global a...Ten years after nearly 200 nations reached the Paris Agreement to address the impact of climate change,the international community is now at a critical juncture.The landmark agreement aims to keep the rise in global average temperature well below 2 degrees Celsius above preindustrial levels and,if possible,below 15 degrees Celsius.It requires each party to prepare national action plans known as Nationally Determined Contributions(NDCs)every five years.By September 30,only about a third had submitted their new NDCs.展开更多
文摘In the past, the planetary radiation balance served to quantify the atmospheric greenhouse effect by the difference between the globally averaged near-surface temperature of and the respective effective radiation temperature of the Earth without atmosphere of resulting in . Since such a “thought experiment” prohibits any rigorous assessment of its results, this study considered the Moon as a testbed for the Earth in the absence of its atmosphere. Since the angular velocity of Moon’s rotation is 27.4 times slower than that of the Earth, the forcing method, the force-restore method, and a multilayer-force-restore method, used in climate modeling during the past four decades, were alternatively applied to address the influence of the angular velocity in determining the Moon’s globally averaged skin (or slab) temperature, . The multilayer-force-restore method always provides?the highest values for , followed by the force-restore method and the forcing method, but the differences are marginal. Assuming a solar albedo of , a relative emissivity , and a solar constant of and applying the multilayer-force-restore method yielded and for the Moon. Using the same values for α, ε, and S, but assuming the Earth’s angular velocity for the Moon yielded and quantifying the effect of the terrestrial atmosphere by . A sensitivity study for a solar albedo of commonly assumed for the Earth in the absence of its atmosphere yielded , , and . This means that the atmospheric effect would be more than twice as large as the aforementioned difference of 33 K. To generalize the findings, twelve synodic months (i.e., 354 Earth days) and 365 Earth days, where , a Sun-zenith-distance dependent solar albedo, and the variation of the solar radiation in dependence of the actual orbit position and the tilt angle of the corresponding rotation axis to the ecliptic were considered. The case of Moon’s true angular velocity yielded and . Whereas Earth’s 27.4 times higher angular velocity yielded , and . In both cases, the effective radiation temperature is ,?because the computed global albedo is . Thus, the effective radiation temperature yields flawed results when used for quantifying the atmospheric greenhouse effect.
文摘The solar climate of our Moon is analyzed using the results of numerical simulations and the recently released data of the Diviner Lunar Radiometer Experiment (DLRE) to assess (a) the resulting distribution of the surface temperature, (b) the related global mean surface temperature T<sub>s</sub>>, and (c) the effective radiation temperature T<sub>e</sub> <sub></sub>often considered as a proxy for T<sub>s</sub>> of rocky planets and/or their natural satellites, where T<sub>e</sub> <sub></sub>is based on the global radiation budget of the well-known “thought model” of the Earth in the absence of its atmosphere. Because the Moon consists of similar rocky material like the Earth, it comes close to this thought model. However, the Moon’s astronomical features (e.g., obliquity, angular velocity of rotation, position relative to the disc of the solar system) differ from that of the Earth. Being tidally locked to the Earth, the Moon’s orbit around the Sun shows additional variation as compared to the Earth’s orbit. Since the astronomical parameters affect the solar climate, we predicted the Moon’s orbit coordinates both relative to the Sun and the Earth for a period of 20 lunations starting May 24, 2009, 00:00 UT1 with the planetary and lunar ephemeris DE430 of the Jet Propulsion Laboratory of the California Institute of Technology. The results revealed a mean heliocentric distance for the Moon and Earth of 1.00124279 AU and 1.00166376 AU, respectively. The mean geocentric distance of the Moon was 384792 km. The synodic and draconic months deviated from their respective means in a range of -5.7 h to 6.9 h and ±3.4 h, respectively. The deviations of the anomalistic months from their mean range between -2.83 d and 0.97 d with the largest negative deviations occurring around the points of inflection in the curve that represents the departure of the synodic month from its mean. Based on the two successive passages of the Sun through the ascending node of the lunar equator plane, the time interval between them corresponds to 347.29 days, i.e., it is slightly longer than the mean draconic year of 346.62 days. We computed the local solar insolation as input to the multilayer-force restore method of Kramm et al. (2017) that is based on the local energy budget equation. Due to the need to spin up the distribution of the regolith temperature to equilibrium, analysis of the model results covers only the last 12 lunations starting January 15, 2010, 07:11 UT1. The predicted slab temperatures, T<sub>slab</sub>, considered as the realistic surface temperatures, follow the bolometric temperatures, T<sub>bol</sub>, acceptably. According to all 24 DLRE datasets related to the subsolar longitude ø<sub>ss</sub>, the global averages of the bolometric temperature amounts to T<sub>bol</sub>=201.1k± 0.6K. Based on the globally averaged emitted infrared radiation of F<sub>IR</sub>>=290.5W·m<sup>-2</sup>± 3.0W·m<sup>-2</sup> derived from the 24 DLRE datasets, the effective radiative temperature of the Moon is T<sub>e, M</sub>>=T<sub>bol>1/4</sub>=271.0k± 0.7K so that T<sub>bol</sub>>≅0.742T<sub>e, M</sub>. The DLRE observations suggest that in the case of rocky planets and their natural satellites, the globally averaged surface temperature is notably lower than the effective radiation temperature. They differ by a factor that depends on the astronomical parameters especially on the angular velocity of rotation.
基金Zamil S.Alzamil would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-172.
文摘Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species.However,it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes.To solve these issues,we propose a transfer learning-based technique in which we use Efficient-Net,which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database,which is a large scale dataset.Furthermore,prior to the activation layer,we use Global Average Pooling(GAP)instead of dense layer with the aim of averaging the results of predictions along with having more information compared to the dense layer.To check the validity of our model,we validate our model on the validation set which achieves satisfactory results.Also,for the localization task,we propose an architecture that consists of localization aware block,which captures localization information for better prediction and residual connections to handle the over-fitting problem.Actually,the residual connections help the layer to combine missing information with the relevant one.In addition,we use class weights and Focal Loss(FL)to handle class imbalance problems along with reducing false predictions.Actually,class weights assign less weights to classes having fewer instances and large weights to classes having more number of instances.During the localization,the qualitative assessment shows that we achieve 57%Mean Intersection Over Union(IoU)on testing data,and the classification results show 75%precision,70%recall,78%accuracy and 74%F1-Score for 468 fish species.
基金The National Natural Science Foundation of China (62176048)provided funding for this research.
文摘The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.
文摘When compared to the average annual global temperature record from 1880, no published climate model posited on the assumption that the increasing concentration of atmospheric carbon dioxide is the driver of climate change can accurately replicate the significant variability in the annual temperature record. Therefore, new principles of atmospheric physics are developed for determining changes in the average annual global temperature based on changes in the average atmospheric concentration of water vapor. These new principles prove that: 1) Changes in average global temperature are not driven by changes in the concentration of carbon dioxide;2) Instead, autonomous changes in the concentration of water vapor, <span style="white-space:nowrap;">Δ</span>TPW, drive changes in water vapor heating, thus, the average global temperature, <span style="white-space:nowrap;">Δ</span>T<sub>Avg</sub>, in accordance with this principle, <span style="white-space:normal;"><span style="white-space:nowrap;">Δ</span>T</span><span style="white-space:normal;"><sub>Avg</sub>=0.4<span style="white-space:normal;"><span style="white-space:nowrap;">Δ</span>TPW </span></span>the average accuracy of which is ±0.14%, when compared to the variable annual, 1880-2019, temperature record;3) Changes in the concentration of water vapor and changes in water vapor heating are not a feedback response to changes in the concentration of CO<sub>2</sub>;4) Rather, increases in water vapor heating and increases in the concentration of water vapor drive each other in an autonomous positive feedback loop;5) This feedback loop can be brought to a halt if the average global rate of precipitation can be brought into balance with the average global rate of evaporation and maintained there;and, 6) The recent increases in average global temperature can be reversed, if average global precipitation can be increased sufficiently to slightly exceed the average rate of evaporation.
文摘<p> A. <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">Changes </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in</span></span></span><span><span><span style="font-family:" color:black;"=""><span style="font-family:Verdana;"> average global temperature are not driven by changes in the concentration of carbon dioxide;</span></span></span></span> </p> <p> <span style="font-family:Verdana;">B. </span><span style="font-family:Verdana;">Instead, autonomous changes in the concentration of water vapor, </span><span style="font-family:Verdana;">Δ</span><span style="font-family:Verdana;">TPW, </span><span color:black;"=""><span style="font-family:Verdana;">drive changes in water vapor heating, thus, </span><span style="background:#C00000;font-family:Verdana;">changes in</span><span style="font-family:Verdana;"> the average global temperature, </span></span><span style="font-family:Verdana;">Δ</span><span style="font-family:Verdana;"><i>T</i></span><span style="font-family:Verdana;"><sub>Avg</sub></span><span color:black;"=""><span style="font-family:Verdana;">, </span><span style="background:#C00000;font-family:Verdana;">in deg. Celsius are calculated</span><span style="font-family:Verdana;"> in accordance with this principle,</span></span> </p> <p style="text-align:center;margin-left:10pt;"> <span><span><span style="font-family:" color:black;"=""><span style="font-family:Verdana;"></span><img src="Edit_6e770969-a7c9-4192-a6ad-03de906a4d65.bmp" alt="" /><br /> </span></span></span> </p> <p align="center" style="margin-left:10.0pt;text-align:center;"> <span><span><span style="font-family:;" "=""><span></span></span></span><span><span><span style="font-family:" color:black;"=""></span></span></span></span> </p> <p> <span><span><span style="font-family:" color:black;background:#c00000;"=""><span style="font-family:Verdana;">measured in kg<span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#f7f7f7;"=""><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#f7f7f7;"="">·</span></span>m</span><sup><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#ffffff;"="">-</span>2</span></sup><span style="font-family:Verdana;">,</span></span></span></span><span><span><span style="font-family:" color:black;"=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the average accuracy of which is ±0.14%, when compared to the variable annual, 1880 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-</span></span></span><span><span><span style="font-family:" color:black;"=""><span style="font-family:Verdana;"> 2019, </span><span style="background:#C00000;font-family:Verdana;">average global </span><span style="font-family:Verdana;">temperature record;</span></span></span></span> </p>
文摘Ten years after nearly 200 nations reached the Paris Agreement to address the impact of climate change,the international community is now at a critical juncture.The landmark agreement aims to keep the rise in global average temperature well below 2 degrees Celsius above preindustrial levels and,if possible,below 15 degrees Celsius.It requires each party to prepare national action plans known as Nationally Determined Contributions(NDCs)every five years.By September 30,only about a third had submitted their new NDCs.
