Analysis of Carbon Dioxide and Cloud Effects on Temperature in Northeast China

Effects of Carbon Dioxide and Clouds on Temperature

[1] Svensmark and Friis-Christensen [1997] (hereinafter referred to as SFC97) proposed a ‘‘galactic cosmic ray (GCR), clouds, and climate’’ hypothesis that cosmic ray flux, modulated by solar activity, may modify global total cloud cover (TCC) and thus global surface temperature by changing the number of ions in the atmosphere and thus the cloud droplet formation. This GCR-TCC hypothesis has been questioned by many authors who examined correlations of GCR with various satellite cloud properties and over different time intervals. We note that the SFC97 hypothesis differs from the one proposed by Marsh and Svensmark [2000] (hereinafter referred to as MS00), who postulated that the GCR influence on cloudiness is restricted to low cloud cover (LCC). [2] All the articles in this debate were based on satellite data and limited to the ocean. By contrast, Sun and Bradley [2002] (hereinafter SB02) combined short-period satellite data (from the International Satellite Cloud Climatology Project, ISCCP) with long-term visual total cloud observations to reassess the GCR-TCC relationship over the ocean and the land as well. The SB02 study was initialized in early 2000, focusing on the GCR-TCC hypothesis. In the early spring of 2001 we added to our revision several comments regarding the GCR-LCC hypothesis. We concluded in our analysis that there is no solid evidence for the existence of a GCR-cloud correlation (either total cloud or low cloud cover). [3] Marsh and Svensmark [2004] (hereinafter referred to as MS04) raised five comments about our analysis and finally claimed that ‘‘On the basis of satellite observations, there continue to be strong indications of a globally distributed correlation between cosmic rays and low-cloud cover.’’ We will demonstrate in this reply that their comments are either unfounded or illogical. All of their comments are dealt with in section 2, but issues relevant to the GCR-LCC hypothesis will be separately addressed in section 3. New evidence will be present in section 3 to indicate that the quality of the ISCCP infrared (IR) LCC data set, which MS00 and Marsh and Svensmark [2003] (hereinafter referred to as MS03) used to create the GCR-LCC hypothesis, is highly questionable. This reply confirms the view we expressed on the work of SB02 that there is no solid GCR-cloud relationship.

Land‐use change and soil management play a vital role in influencing losses of soil carbon (C) by respiration. The aim of this experiment was to examine the impact of natural vegetation restoration and long‐term fertilization on the seasonal pattern of soil respiration and cumulative carbon dioxide (CO2) emission from a black soil of northeast China. Soil respiration rate fluctuated greatly during the growing season in grassland (GL), ranging from 278 to 1030 mg CO2 m−2 h−1 with an average of 606 mg CO2 m−2 h−1. By contrast, soil CO2 emission did not change in bareland (BL) as much as in GL. For cropland (CL), including three treatments [CK (no fertilizer application), nitrogen, phosphorus and potassium application (NPK), and NPK together with organic manure (OM)], soil CO2 emission gradually increased with the growth of maize after seedling with an increasing order of CK < NPM < OM, reaching a maximum on 17 August and declining thereafter. A highly significant exponential correlation was observed between soil temperature and soil CO2 emission for GL during the late growing season (from 3 August to 28 September) with Q10 = 2.46, which accounted for approximately 75% of emission variability. However, no correlation was found between the two parameters for BL and CL. Seasonal CO2 emission from rhizosphere soil changed in line with the overall soil respiration, which averaged 184, 407, and 584 mg CO2 m−2 h−1, with peaks at 614, 1260, and 1770 mg CO2 m−2 h−1 for CK, NPK, and OM, respectively. SOM‐derived CO2 emission of root free‐soil, including basal soil respiration and plant residue–derived microbial decomposition, averaged 132, 132, and 136 mg CO2 m−2 h−1, respectively, showing no difference for the three CL treatments. Cumulative soil CO2 emissions decreased in the order OM > GL > NPK > CK > BL. The cumulative rhizosphere‐derived CO2 emissions during the growing season of maize in cropland accounted for about 67, 74, and 80% of the overall CO2 emissions for CK, NPK, and OM, respectively. Cumulative CO2 emissions were found to significantly correlate with SOC stocks (r = 0.92, n = 5, P < 0.05) as well as with SOC concentration (r = 0.97, n = 5, P < 0.01). We concluded that natural vegetation restoration and long‐term application of organic manure substantially increased C sequestration into soil rather than C losses for the black soil. These results are of great significance to properly manage black soil as a large C pool in northeast China.

Various aspects of the connection between low cloud cover (LCC) and cosmic rays (CRs) are considered. Most features of this connection point to the absence of substantial causal relationship between LCC and CRs. Even on the assumption that some LCC fraction is related to CR intensity and varies with it, its most likely value is about 2% (although, within two standard deviations it can be as high as 20%). The most serious argument against the causal relationship between CRs and LCC is the existence of negative correlation between low and medium cloud cover (MCC). The scenario of simultaneous influence of solar activity on CRs and cloud cover is discussed, which might lead to the observed correlations.

The controversial connection between cosmic rays, solar activity, and cloud cover is investigated using a climatological reconstructed reanalysis product: the North American Regional Reanalysis which provides high-resolution, low, mid-level, high, and total cloud cover data over a Lambert conformal conic projection permitting land/ocean discrimination. Pearson’s product-moment regional correlations were obtained between monthly cloud cover data and solar variability indicators, cosmic ray neutron monitors, several climatological indices, including the Atlantic Multidecadal Oscillation (AMO), and between cloud layers. Regions of the mid-latitude oceans exhibited a positive correlation with cosmic ray flux. Additionally, this maritime low cloud cover exhibits the only failed correlation significance with other altitudes. The cross correlation reveals that cloud cover is positively correlated everywhere but for ocean low cloud cover, supporting the unique response of the marine layer. The results of this investigation suggest that with the assumption that solar forcing does impact cloud cover, measurements of solar activity exhibits a slightly higher correlation than GCRs. The only instance where GCRs exhibit a positive regional correlation with cloud cover is for maritime low clouds. The AMO exerts the greatest control of cloud cover in the NARR domain.

Seasonal trends in low and total cloud cover, as well as for associated climate variables diurnal temperature range (DTR) and number of rain days, are investigated for South Africa. It is also investigated whether the observed trends and variability in cloud cover could be related to the El Nino-Southern Oscillation (ENSO) phenomenon, which has a major influence on the variability of summer rainfall in South Africa. These trends have not been investigated recently and in such detail. In the light of the climate change debate, updated studies of historical climate change are important, especially for regions and climate variables of which such studies are not published often. Seasonal trends of daily means were examined from quality-controlled data time series of 28 climate stations over South Africa, for the period 1960 to 2005. Regional trends could be determined by averaging series of stations showing similar trends, within areas delineated in such a way that the trend of the averaged series would be statistically significant. In this way the intra-seasonal spatial variability of same trend regions, as well as the spatial relationships between trends of the different climate variables under discussion, could be established. The main results, taking all seasons into account, is a general decrease in mean daily low cloud cover, and to a lesser extent total cloud cover, over most of South Africa, but an increase in the south and south-west of the country. However, the sizes of same trend regions show considerable variability between seasons. While trends in DTR and rain days are the opposite and the same, respectively, of trends in cloud cover in most cases, it is shown that this is not always the case. A region covering the northern, central and western interior of South Africa, with late-summer (JFM) cloud cover negatively correlated with equatorial Pacific sea-surface temperatures (SSTs), shows only a non-significant decrease in total and low cloud cover for JFM, which corresponds to a non-significant increase in equatorial Pacific SSTs during the same period.

The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam

Short-term changes in global cloud cover and in cosmic radiation

A dynamic STIRPAT model used in the current study is based on panel data from the eight most populous countries from 1975 to 2020, revealing the nonlinear effects of urbanization routes (percentage of total urbanization, percentage of small cities and percentage of large cities) on carbon dioxide (CO2) emissions. Using “Dynamic Display Unrelated Regression (DSUR)” and “Fully Modified Ordinary Least Squares (FMOLS)” regressions, the outcomes reflect that percentage of total urbanization and percentage of small cities have an incremental influence on carbon dioxide emissions. However, square percentage of small cities and square percentage of total urbanization have significant adverse effects on carbon dioxide (CO2) emissions. The positive relationship between the percentage of small cities, percentage of total urbanization and CO2 emissions and the negative relationship between the square percentage of small cities, square percentage of total urbanization and CO2 emissions legitimize the inverted U-shaped EKC hypothesis. The impact of the percentage of large cities on carbon dioxide emissions is significantly negative, while the impact of the square percentage of large cities on carbon dioxide emissions is significantly positive, validating a U-shaped EKC hypothesis. The incremental effect of percentage of small cities and percentage of total urbanization on long-term environmental degradation can provide support for ecological modernization theory. Energy intensity, Gross Domestic Product (GDP), industrial growth and transport infrastructure stimulate long-term CO2 emissions. Country-level findings from the AMG estimator support a U-shaped link between the percentage of small cities and CO2 emissions for each country in the entire panel except the United States. In addition, the Dumitrescu and Hulin causality tests yield a two-way causality between emission of carbon dioxide and squared percentage of total urbanization, between the percentage of the large cities and emission of carbon dioxide, and between energy intensity and emission of carbon dioxide. This study proposes renewable energy options and green city-friendly technologies to improve the environmental quality of urban areas.

Clouds are important in a climate system due to their impact on the radiation budget and precipitation. Changes in cloud cover are hard to infer due to lack of reliable long‐term cloud datasets. An attempt is made in this study to investigate the changes in cloud cover using the relationship between precipitation extremes and clouds. Heavy precipitation is associated with convective clouds while light precipitation occurs mostly with low clouds. The Global Precipitation and Climatology Project (GPCP) precipitation data are used in this study to relate the changes in heavy and light precipitation with those in convective and low cloud cover, respectively, from the Visible and Infrared Scanner data of the Tropical Rainfall Measuring Mission available from 1998 to 2014. Slopes were derived between changes in precipitation extremes and cloud cover using monthly data. These slopes were applied to long‐term trends of precipitation extremes from GPCP data (1979–2016) to infer long‐term changes in convective and low cloud cover. Cloud cover derived using this technique shows substantial inter‐monthly and inter‐annual variability. The results show an increase of about 4.48 ± 1.9% per decade in convective cloud cover over tropical ocean (25 ° S–25 ° N). This is consistent with National Oceanic and Atmospheric Administration (NOAA) High Resolution Infrared Radiometer Sounder (HIRS) observations, which show an increase of about 5.04 ± 2.18% per decade in convective cloud cover over tropical ocean. In the present study an increasing trend of about 5.54 ± 2.07% in convective cloud cover over land (20 °–60 ° N) is also derived, which is comparable to the NOAA HIRS trend of about 6.57 ± 2.53% increase per decade. Decreases of about 3.52 ± 1.69% and 4.26 ± 1.48% per decade in low cloud cover over tropical ocean and northern mid‐latitude land, respectively, are reported and are consistent with decreases of about 3.05 ± 1.68% and 5.31 ± 2.22% from NOAA HIRS data over those regions.

دراینمقاله،میزانو ارزش انتشارگازهایگلخانه‌ای اکسید‌نیتروس(N2O) و دی‌اکسید‌کربن(CO2)حاصلازتولید حبوبات منتخب ایران (شامل نخود، لوبیا و عدس) با استفاده از مدل GHGE،برایسالزراعی91-90برآورد شده است.نتایج نشان‌داد که استان‌هایفارسوبوشهر، به‌ترتیبباتولیدسالانه271/79 و 004/0 تنN2O، بیشترینوکمترینمیزانتولیدگاز گلخانه‌ایN2Oرا دارامی‌باشند. همچنین استان‌هایلرستانوبوشهر نیز به‌ترتیب باتولیدسالانه83/10327 و33/1‌تنCO2،بیشترینوکمترینمیزانتولیدگاز گلخانه‌ایCO2را به‌خود اختصاص داده‌اند. مجموعهزینه‌هایزیست‌محیطی انتشار گازهای گلخانه‌ای N2O و CO2 کلکشورنیزحدود705/32‌میلیاردریالبرآوردگردید. باتوجهبه یافته‌ها، مدیریت کودهای نیتروژنه مصرفی در مزارعوتوسعهسیاست‌کاهشمیزانانتشاربه‌همراه مالیات زیست‌محیطی انتشار گازهای گلخانه‌ای بر سطوح مختلف تولید پیشنهاد شده ‌است. واژه‌های کلیدی: اکسید‌نیتروس، دی‌اکسید‌کربن، حبوبات، گازهای گلخانه‌ای

&amp;lt;p&amp;gt;We investigated the spatial structure of the intraseasonal variation (15-30 day) in cloud cover in the mid-latitudes during winter. We attempted to interpret the spatial pattern of clouds in&amp;amp;#12288;the context of Rossby waves.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We used the total cloud cover in H-series dataset (1984-2016) by the International Satellite Cloud Climatology Project (ISCCP) based on the satellite observations, and ERA-Interim re-analysis data (1980-2016) including high, medium, and low cloud covers defined by &amp;amp;#963; coordinate.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We calculated correlation coefficients between the geopotential height at 300hPa (Z300) at a certain position and the cloud covers, meridional wind, and vertical velocity in the surrounding area. The positions of the maximum of high (0.45&amp;amp;#8807;&amp;amp;#963;) and medium cloud cover (0.8&amp;amp;#8807;&amp;amp;#963;&amp;amp;#65310;0.45) relative to Z300 are longitudinally constant for all longitudes except the region from east Asia to western part of the Pacific. The position of the maximum of the high cloud cover is located just west of the ridge and just east of the maximum positions of the upward motions of re-analysis vertical velocity and its adiabatic component. These results suggest that the adiabatic upward motion in the southerly wind region west of the ridge contributes to the generation of high cloud cover. In contrast, the position of the maximum of medium cloud cover is located just east of the trough. The position of the maximum of diabatic upward motion, which is consider to be due to condensation process is located near the maximum of medium cloud cover. These results suggest that Rossby waves modulate activity of short-period disturbances with precipitation. Apart from high and medium cloud covers, the position of the maximum of low cloud cover (&amp;amp;#963;&amp;amp;#65310;0.8) has large longitudinal dependency. While the position of the maximum is located at almost the same as that of medium cloud cover mainly over the continent, the position of the maximum is located just east of the ridge mainly over the ocean.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The correlation coefficients between ISCCP total cloud cover and Z300 are statistically significant only over the continent, where the positions of the maximum of high, medium, and low cloud covers are all located east of the trough and west of the ridge.&amp;lt;/p&amp;gt;

Annual CO2 emissions (in 1000 Mt) %

With the goal of understanding the relative roles of anthropogenic and natural factors in driving observed cloud trends, this study investigates cloud changes associated with decadal variability including the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). In the preindustrial simulations of CMIP5 global climate models (GCMs), the spatial patterns and the vertical structures of the PDO-related cloud cover changes in the Pacific are consistent among models. Meanwhile, the models show consistent AMO impacts on high cloud cover in the tropical Atlantic, subtropical eastern Pacific, and equatorial central Pacific, and on low cloud cover in the North Atlantic and subtropical northeast Pacific. The cloud cover changes associated with the PDO and the AMO can be understood via the relationships between large-scale meteorological parameters and clouds on interannual time scales. When compared to the satellite records during the period of 1983–2009, the patterns of total and low cloud cover trends associated with decadal variability are significantly correlated with patterns of cloud cover trends in ISCCP observations. On the other hand, the pattern of the estimated greenhouse gas (GHG)-forced trends of total cloud cover differs from that related to decadal variability, and may explain the positive trends in the subtropical southeast Pacific, negative trends in the midlatitudes, and positive trends poleward of 50°N/S. In most models, the magnitude of the estimated decadal variability contribution to the observed cloud cover trends is larger than that contributed by GHG, suggesting the observed cloud cover trends are more closely related to decadal variability than to GHG-induced warming.

Soil is an important source of atmospheric carbon dioxide (CO2) and nitrous oxide (N2O). Studies of CO2 and N2O emissions from bare soil may explain annual changes in carbon (C) in soil organic matter (SOM) and help analyze N2O production from SOM. Therefore, CO2 and N2O emissions associated with the decomposition of SOM from bare soil are important factors for assessing the C budget and N2O emission in agricultural fields. We conducted a study over 7 years to assess the controlling factors of CO2 and N2O emissions from unplanted and unfertilized soil in Mikasa, Hokkaido, Japan. The CO2 flux increased in summer and there were significant positive correlations between the CO2 flux and soil temperature in the first 4 years. However, apparent relationships between CO2 flux and water-filled pore space, soil NH4 and NO3 concentrations were not observed. The slope of monthly CO2 emission against mean monthly temperature was positively correlated with monthly precipitation. These results suggest that the response of CO2 production to increases in soil temperature became more sensitive in wet soils. The average CO2 emission during the study period was 2.53 Mg C ha−1 year−1, and uncertainty in the annual CO2 emission was 24%. Annual precipitation explained the yearly variation (CO2 emission [Mg C ha−1 year−1] = 0.0021 × annual precipitation [mm year−1] −0.0499, R = 0.976, P < 0.001). Nitrous oxide flux increased from July to October and was positively correlated with CO2 flux. Based on the ratio of N2O-N : NO-N of fluxes, N2O appeared to be the main product of denitrification. The average N2O emission over the study period was 4.88 kg N ha−1 year−1, and uncertainty in the annual N2O emission was 58.5%. Strong relationships between the monthly emissions of CO2 and N2O suggest that N2O production by denitrification is strongly affected by SOM decomposition. Unlike CO2 emission, a relationship between N2O emission and precipitation was not observed because of the multiple pathways of nitrification and denitrification for N2O production induced by SOM decomposition.