Global-mean Air Temperature Research Articles

Utilization Potential of Tree-of-Heaven Species Biomass—A Review

Climate change and the subsequent increase in global mean air temperature already present a significant impact on forest vegetation. Especially in the near future, several forest species are expected to be in danger of extinction or compelled to migrate to colder regions. Some common species will be replaced by highly climate-tolerant species, primarily exotic plants, among others. The tree of Ailanthus, or “tree-of-heaven”, as it is widely known, constitutes a rapidly growing plant, considered to be native to parts of China, while since the middle of the 18th century, it gradually spread to Europe and North America. This species demonstrates a preference for warmer, drier environments, although it can also survive in a variety of habitats and endure pollution of urban areas. It is a species with several uses, such as for animal feed, fuel, timber, pharmaceutical applications, etc., while its suitability for specialized applications of high-added-value is constantly being investigated. Its wood has a desirable appearance and characteristics that are comparable to those of other hardwood species of similar densities/weight. This article discusses some of the most important characteristics of Ailanthus wood and presents a comprehensive and constructive review of the chemistry, pharmacology, traditional and innovative uses, quality control, biological resistance, potential utilization in bioenergy and biofuels and wood products (e.g., wood-based panels, other advanced structure materials, etc.), use challenges and limitations, in order to contribute to the utilization potential assessment of this species biomass.

Large-ensemble climate simulations to assess changes in snow stability over northern Japan

AbstractTo examine the influence of global warming including increased heavy snowfall frequency on the potential of natural dry snow avalanche frequency and magnitude, we estimated the frequency of weak layer formation and the associated slab overload above the weak layer over northern Japan. The estimation was numerically performed using climate models' output for 1800 winter simulations in each of the historical (1951–2010) and +4°C experiments by forcing a physical-based snowpack model with the result of the climate models. Here the +4°C experiment was defined as a climate when the global mean air temperature had increased by 4°C from the preindustrial level. The estimation results showed that the probability of weak layer formation, identified by the natural stability index, would decrease all over the area because of the shorter age of the weak layers caused by a warmer climate, indirectly indicating a potential decrease in avalanche frequency. However, because of increased heavy snowfall frequency, slab overload would increase by 10–15% in inland areas for weak layers of decomposing fragments/precipitation particles and the mountainous area facing the Sea of Japan for weak layers of facets/depth hoar, thereby potentially indicating an increased magnitude of avalanches.

Changes in the global mean air temperature over land since 1980

Changes in frequency of record-breaking land surface temperature over Earth's since 1980 are assessed here using six different gridded datasets comprising monthly mean air temperature and weather station data retrieved from Global Historical Climatology Network and NOAA Global Summary of the Day. The 2010–2020 decade was the warmest in the history of modern measurements for more than one third of the Earth surface, i.e., Europe, United States, southern Africa, northern Siberia and most of Australia, according to four different datasets. Since 1980, new high/maximum temperature records have been observed in most parts of Europe, eastern Asia and the eastern of North America. 2020 was the warmest year recorded, while 2010 was the hottest year for parts of North America (≈34% of the territory). Approximately 60% of the Earth's land surface recorded a new maximum annual mean temperature after 2010. In terms of datasets comparison, ERA5 came the closest to the weather stations data regarding the hottest year ever recorded at various locations while Berkeley Earth and ERA5 indicated the highest correlations for the total number of record-breaking temperature events.

Methane Emissions from Wetlands in China and Their Climate Feedbacks in the 21st Century.

Wetlands are large sinks of carbon dioxide (CO2) and sources of methane (CH4). Both fluxes can be altered by wetland management (e.g., restoration), leading to changes in the climate system. Here, we use multiple models to assess CH4 emissions and CO2 sequestration from the wetlands in China and the impacts on climate under three climate scenarios and four wetland management scenarios with various levels of wetland restoration in the 21st century. We find that wetland restoration leads to increased CH4 emissions with a national total of 0.32-11.31 Tg yr-1. These emissions induce an additional radiative forcing of 0.0005-0.0075 W m-2 yr-1 and global annual mean air temperature rise of 0.0003-0.0053 °C yr-1, across all future climate and management scenarios. However, wetland restoration also resulted in net CO2 sequestration, leading to a combined net greenhouse gas sink in all climate management scenarios, except in the highest restoration level combined with the hottest climate scenario. The highest climate cooling was achieved under medium restoration, with the climate scenario consistent with the Paris agreement target of below 2 °C, with a cumulative global warming potential of -3.2 Pg CO2-eq (2020-2100). Wetland restoration in the Qinghai-Tibet Plateau offers the greatest cooling effect.

Early Estimation of Daily Reference Evapotranspiration Using Machine Learning Techniques for Efficient Management of Irrigation Water

Assessing the right amount of water needs for a specific crop is a key task for farmers and agronomists to achieve efficient and optimal irrigation scheduling, and then an optimal crop yield. To this end, the reference evapotranspiration (ET0) was developed. It represents the atmospheric evaporation demand, and therefore an important variable for irrigation management. In this regard, several methods such as the FAO’s Penman-Monteith and Hargreaves have been used to model and estimate ET0. These methods use climatic parameters data for calculation procedures such as solar net radiation (Rn), saturation vapour pressure(es), and min-max air temperatures or a combination of them. In this paper, we investigated two proposed data-driven methods to predict ET0 values in a semi-arid region in Morocco. The first approach is based on forecasting techniques and the second one uses end-to-end modeling of ET0 based on meteorological data and machine learning models. The feature selection and engineering results show that solar global radiation (Rg) and mean air temperature (Ta) have a significance of more than 87% as relevant predictors features for the ET0. We then used them as input to machine learning regression models. Regression evaluation metrics showed that The XGboost regressor model performs well in both cross-validation with R2=0.93 in the first fold, and in hold-out validation with R2=0.92 and RMSE=0.55. As a final step, we compared the univariate time series forecasting of ET0 using the Facebook Prophet model versus the machine learning modeling method that we proposed. As goodness-of-fit measures, forecasting using machine learning modeling of ET0 showed better results in terms of both R2 and RMSE.

Simulating the impact of projected climate change on rice (Oryza sativa L.) yield and adaptation strategies in major rice growing regions of India

Global mean air temperatures are projected to increase by 1.8 to 4.0 oC by the end of this century. Higher temperatures coupled with increased CO2 levels could result in altered growth and productivity of crops. Since rice is sensitive to high temperatures, a simulation analysis was carried out to quantify the impact of increased temperatures (1, 2, 3, 4 and 5 oC) and elevated CO2 (369, 450,550 and 650ppm) alone and in combination on the yield using InfoCrop-RICE model. An assessment is also provided on the possible gains due to change in planting time and crop duration. Though elevated CO2 alone increased the rice yields, increase in temperature by 1oC (even at 369 ppm CO2 concentration) reduced irrigated rice yield by 6.6 to 11.1 % and rainfed rice yields by 4.6 % to 9.4 %. The combined impact of increased temperature and elevated CO2 resulted in net decline in yield in spite of CO2 fertilization. Planting cultivars in future with matching crop duration of current cultivars and change in planting time may reduce the adverse impacts of climate change on rice yields.

Attribution of global lake systems change to anthropogenic forcing

Lake ecosystems are jeopardized by the impacts of climate change on ice seasonality and water temperatures. Yet historical simulations have not been used to formally attribute changes in lake ice and temperature to anthropogenic drivers. In addition, future projections of these properties are limited to individual lakes or global simulations from single lake models. Here we uncover the human imprint on lakes worldwide using hindcasts and projections from five lake models. Reanalysed trends in lake temperature and ice cover in recent decades are extremely unlikely to be explained by pre-industrial climate variability alone. Ice-cover trends in reanalysis are consistent with lake model simulations under historical conditions, providing attribution of lake changes to anthropogenic climate change. Moreover, lake temperature, ice thickness and duration scale robustly with global mean air temperature across future climate scenarios (+0.9 °C °Cair–1, –0.033 m °Cair–1 and –9.7 d °Cair–1, respectively). These impacts would profoundly alter the functioning of lake ecosystems and the services they provide.

A novel method for assessing climate change impacts in ecotron experiments

Ecotron facilities allow accurate control of many environmental variables coupled with extensive monitoring of ecosystem processes. They therefore require multivariate perturbation of climate variables, close to what is observed in the field and projections for the future. Here, we present a new method for creating realistic climate forcing for manipulation experiments and apply it to the UHasselt Ecotron experiment. The new methodology uses data derived from the best available regional climate model projection and consists of generating climate forcing along a gradient representative of increasingly high global mean air temperature anomalies. We first identified the best-performing regional climate model simulation for the ecotron site from the Coordinated Regional Downscaling Experiment in the European domain (EURO-CORDEX) ensemble based on two criteria: (i) highest skill compared to observations from a nearby weather station and (ii) representativeness of the multi-model mean in future projections. The time window is subsequently selected from the model projection for each ecotron unit based on the global mean air temperature of the driving global climate model. The ecotron units are forced with 3-hourly output from the projections of the 5-year period in which the global mean air temperature crosses the predefined values. With the new approach, Ecotron facilities become able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability, well representing possible compound events. The presented methodology can also be applied to other manipulation experiments, aiming at investigating ecosystem responses to realistic future climate change.

Snowpack, Tree Size, and Ecological Legacies Promote Seedling Establishment in Tree Islands at the Treeline

At a global scale, mean air or soil temperatures appear to be drivers of treeline position. However, at finer scales, seed availability and microsite conditions may limit the germination, establishment, and growth of tree seedlings—and therefore the position of treeline. Tree islands are features of many treelines, and they can alter microsite conditions by producing seed, providing shelter, and redistributing snow. Near Churchill, Manitoba, Canada, tree islands have higher seedling establishment than nearby forest and treeline sites and could be hot spots for treeline expansion. However, seedling establishment and tree recruitment events may be stochastic, making predictions based on the relationship between seedling counts and microsite conditions difficult. We asked whether current tree island seedling establishment can be predicted by (1) historical recruitment and/or (2) tree and snowpack characteristics. To answer these questions, we measured tree island tree characteristics and monitored seedling establishment and snowpack in five tree islands at latitudinal treeline near Churchill, Manitoba. We fit hierarchical, generalized linear mixed effect models to assess the influence of our hypothesized predictors. We found that tree recruitment in the past was strongly positively associated with current seedling density. Mean basal area and age of trees were also positively associated with seedling density, whereas tree density was negatively associated with seedling density. We found weak positive effects of snowpack snow water equivalent. Our results provide evidence for positive feedbacks within tree islands and suggest useful factors to include in models for predicting future treeline change.

Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network

In this study, a coastal sea level estimation model was developed at an hourly temporal scale using the long short-term memory (LSTM) network, which is a type of recurrent neural networks. The model incorporates the effects of various phenomena on the coastal sea level such as the gravitational attractions of the sun and the moon, seasonality, storm surges, and changing climate. The relative positions of the moon and the sun from the target location at each hour were utilized to reflect the gravitational attractions of the sun and the moon in the model simulation. The wind speed and direction, mean sea level pressure (MSLP), and air temperature near the target point at each hour were used to consider the effects of storm surges and seasonality of the coastal sea level. In addition to the hourly local variables, the annual global mean air temperature was considered as input to the model to reflect the effect of global warming on the coastal sea level. The model was implemented using several input lengths of the annual global mean air temperature to estimate the coastal sea level at the Osaka gauging station in Japan. Several statistics such as the mean, the Nash–Sutcliffe efficiency, and the root mean square error were used to evaluate model performance. The results show that the proposed model accurately reconstructed the effects of the gravitational attractions of the sun and the moon on the coastal sea levels. The model also considered the effects of fluctuations in the wind speed and MSLP although the coastal sea levels during were underestimated strong winds and low MSLP conditions. Lastly, introducing a longer duration annual global mean air temperature improved model accuracy. Consequently, the best results show 0.720 of the NSE value for the test process.

Climatic causes of the current expansion of the Italian locust range in Russia and neighboring countries

Modeling of climatic ranges of a dangerous locust pest - the Italian locust on the territory of Russia and neighboring countries over two periods: 1956-1975 and 1996-2015 was performed. Analysis of the constructed map scheme demonstrates expansion of the climatic range of the Italian locust mainly in the northern direction in the period 1996-2015 compared with the period 1956-1975 resulting from continuing trend of increase in mean global air temperature observed in recent decades. Obtained data can be used in monitoring locust pests in agriculture.

Analysis of climate sensitivity via high-dimensional principal component regression

Uncertainty in the sensitivity of global mean air temperature to changes in atmospheric CO2 concentration arises from factors controlling the response of cloud amounts. Here, we present an analysis of these factors within an ensemble consisting of 164 variations of the NCAR Community Atmosphere Model version 3.1, each of which differs slightly in their treatment of convection and cloud radiative properties. The problem is to uniquely relate observable aspects of model output, which are inherently high dimensional such as the 64,000 spatially mapped values of 11 fields generated from each CAM3.1 model to a scalar prediction of each model’s sensitivity to CO2 forcing. We approach this problem using principal component regression and provide maps of observable predictors. Furthermore, based on this approach, we develop prediction intervals and show that they closely match their advertised coverage.

Identifying the early 2000s hiatus associated with internal climate variability

This study focuses on re-examining the early 2000s hiatus and the associated key components of the global mean surface temperature (GMST) using multiscale statistics for five well-known gridded surface temperature and two reanalysis datasets. The hiatus is characterized as a near-zero trend on the decadal scale corresponding to the maximum P-value via an F-test in statistics. The results reveal that the hiatus exists in both the GMST and global mean air temperature (GMAT) time series, rather than in global warming component, which has maintained an approximately constant rate of change of approximately 0.08 °C/decade over the past three decades. The hiatus’s duration is different from that of time series such as 2002–2012/2001–2013/2002–2014 in HadCRUT4, NOAA-old, ERA-Interim and NCEP-R2. The newly gridded datasets with data infilling or bias correction for interpreting the sea surface temperature (SST) measurement from the old versions show a slightly higher trend from 2002–2012 than the hiatus, which is thus regarded as a slowdown. Comparison suggests that the hiatus should be during the period 2002–2012. Orthogonal wavelet decomposition of the temperature time series shows that the hiatus was merely a decadal balance between cooling from interannual variability and global warming, in addition to weak warming from interdecadal and multidecadal climate oscillations. In addition, the evolutions of the GMST’s interannual composites are well coincided with Niño3.4 SST anomalies, which is consistent with the numerical simulation performed by Kosaka and Xie in 2013. Hence, it is the anomalous El Niño Southern Oscillation (ENSO) events in the early 2000s that caused the hiatus despite a constant rate of global warming and the maximum magnitude of the multidecadal composite that led to the limited contribution to the trend during this period. The multidecadal composite follows a downward path, which implies that future climate conditions will likely rely on competition between multidecadal cooling and global warming if the multidecadal climate cycle repeats, as was experienced during the second half of the twentieth century.

ASSESSING GLOBAL CLIMATE VARIABILITY UNDER COLDEST AND WARMEST PERIODS AT DIFFERENT LATITUDINAL REGIONS

Effect of climate change on water balance will play a key role in the biosphere system. To study the global climate change impact on water balance during 95-year period (1901-1995), long-term grid climatic data including global mean monthly temperature and precipitation at 0.5 x 0.5 degree resolution were analysed. The trend and variation of climate change, the time series of monthly air temperature and precipitation data were aggregated into annual arithmetic means for two extreme periods (1901-1920 and 1990-1995). The potential evapotranspiration (Eo) was calculated using Thornthwaite method.<br />The changes in mean annual value were obtained by subtracting the maximum period data from 1990 to 1995 (Max) with the minimum period data from 1901 to 1920 (Min). The results revealed that over 95-year period, mean global air temperature increased by 0.57oC. The temperature increase varied greatly in Asia, with more than 3.0oC, especially at 45-70oN, as well over the northern part of America (60-65oN) and Europe (55- 75oN). In low latitude across Asia, Africa, and South America, the variation was less than 1.5oC. In 80-85ºN region, the variation was relatively small and at higher latitudes it increased<br />significantly. Precipitation varied temporally and spatially. In the 40-45ºN and 40-45ºS regions, increasing precipitation of more than 100 mm occurred during the June-August and<br />September-November, especially in the northern hemisphere. The Eo increase of 2000 mm during 95 years occurred in the tropical northern America, middle Africa, and South-East Asia. A grid in Central Java of Indonesia showed that the Eo increase of 2500 mm during 95 years resulted in the decrease of growing period by 100 days. In coping with climate change, adjustment of cropping calendar is imperative.

ASSESSING GLOBAL CLIMATE VARIABILITY UNDER COLDEST AND WARMEST PERIODS AT DIFFERENT LATITUDINAL REGIONS

Effect of climate change on water balance will play a key role in the biosphere system. To study the global climate change impact on water balance during 95-year period (1901-1995), long-term grid climatic data including global mean monthly temperature and precipitation at 0.5 x 0.5 degree resolution were analysed. The trend and variation of climate change, the time series of monthly air temperature and precipitation data were aggregated into annual arithmetic means for two extreme periods (1901-1920 and 1990-1995). The potential evapotranspiration (Eo) was calculated using Thornthwaite method.<br />The changes in mean annual value were obtained by subtracting the maximum period data from 1990 to 1995 (Max) with the minimum period data from 1901 to 1920 (Min). The results revealed that over 95-year period, mean global air temperature increased by 0.57oC. The temperature increase varied greatly in Asia, with more than 3.0oC, especially at 45-70oN, as well over the northern part of America (60-65oN) and Europe (55- 75oN). In low latitude across Asia, Africa, and South America, the variation was less than 1.5oC. In 80-85ºN region, the variation was relatively small and at higher latitudes it increased<br />significantly. Precipitation varied temporally and spatially. In the 40-45ºN and 40-45ºS regions, increasing precipitation of more than 100 mm occurred during the June-August and<br />September-November, especially in the northern hemisphere. The Eo increase of 2000 mm during 95 years occurred in the tropical northern America, middle Africa, and South-East Asia. A grid in Central Java of Indonesia showed that the Eo increase of 2500 mm during 95 years resulted in the decrease of growing period by 100 days. In coping with climate change, adjustment of cropping calendar is imperative.

Revisiting the climate impacts of cool roofs around the globe using an Earth system model

Solar reflective ‘cool roofs’ absorb less sunlight than traditional dark roofs, reducing solar heat gain, and decreasing the amount of heat transferred to the atmosphere. Widespread adoption of cool roofs could therefore reduce temperatures in urban areas, partially mitigating the urban heat island effect, and contributing to reversing the local impacts of global climate change. The impacts of cool roofs on global climate remain debated by past research and are uncertain. Using a sophisticated Earth system model, the impacts of cool roofs on climate are investigated at urban, continental, and global scales. We find that global adoption of cool roofs in urban areas reduces urban heat islands everywhere, with an annual- and global-mean decrease from 1.6 to 1.2 K. Decreases are statistically significant, except for some areas in Africa and Mexico where urban fraction is low, and some high-latitude areas during wintertime. Analysis of the surface and TOA energy budget in urban regions at continental-scale shows cool roofs causing increases in solar radiation leaving the Earth–atmosphere system in most regions around the globe, though the presence of aerosols and clouds are found to partially offset increases in upward radiation. Aerosols dampen cool roof-induced increases in upward solar radiation, ranging from 4% in the United States to 18% in more polluted China. Adoption of cool roofs also causes statistically significant reductions in surface air temperatures in urbanized regions of China (−0.11 ± 0.10 K) and the United States (−0.14 ± 0.12 K); India and Europe show statistically insignificant changes. Though past research has disagreed on whether widespread adoption of cool roofs would cool or warm global climate, these studies have lacked analysis on the statistical significance of global temperature changes. The research presented here indicates that adoption of cool roofs around the globe would lead to statistically insignificant reductions in global mean air temperature (−0.0021 ± 0.026 K). Thus, we suggest that while cool roofs are an effective tool for reducing building energy use in hot climates, urban heat islands, and regional air temperatures, their influence on global climate is likely negligible.

Winter Soil Warming Exacerbates the Impacts of Spring Low Temperature Stress on Wheat

AbstractThe increase in global mean air temperature is likely to affect the soil temperatures in agricultural areas. This study aims to study the effects of winter soil warming on the responses of wheat to low temperature stress in spring. Wheat plants were grown under either normal or increased soil temperature by 2.5 °C for 82 days in winter. The physiological and yield responses of the plants to a 2‐day low temperature stress (4/2 °C in the day/night) at jointing stage were investigated. After exposing to low spring temperature, the plants that had experienced winter soil warming showed lower leaf and root water potential, lower oxygen scavenging capacity and poor photosynthetic performance as compared with the plants grown under normal soil temperature during winter. WL plants had significantly lower sugar content in shoot than the CL plants, which might have contributed to their higher susceptibility to low temperature. In addition, winter soil warming exacerbated the negative effects of low spring temperature on grain yield.

Potential of Changes in Climatic Range of Colorado Potato Beetle in Russia and Neighboring Countries under Different Scenarios of Anthropogenic Impact on Climate

Changes in climatic range of a dangerous pest Colorado potato beetle in the 21st century are computed and mapped on the basis of known requirements of this species to climatic conditions. Multi-model averages for two scenarios of anthropogenic impact on the Earth’s climate system (moderate RCP4.5 and extreme RCP8.5) are used as future climates. It is shown that in Russia and neighboring countries further warming will lead to northward, north-eastward and eastward expansion of the range under both scenarios. At the end of the 21st century, maximal increase in the range will take place under extreme scenario. Climatic ranges of Colorado potato beetle at the time periods when global mean air temperature exceeds the preindustrial level by 2 °С will be similar for both scenarios.

The Sea Level Budget Since 2003: Inference on the Deep Ocean Heat Content

This study provides an overview of the various components of the global mean sea level evolution over two time spans: (1) 2005–2012 (corresponding to the full deployment of the Argo program) and (2) 2003–2012. Using a sea level budget approach, we compare altimetry-based global mean sea level, global ocean mass from GRACE space gravimetry and steric sea level from Argo and other in situ measurements. One goal of this study is to investigate whether it is possible to constrain the deep ocean contribution to the global mean sea level rise over the last decade. This question is particularly relevant, considering the current debate about the ‘hiatus,’ i.e., the observed recent pause of the global mean air and sea surface temperature evolution while the planet is still in thermal imbalance. We consider a total of 16 different data sets. Differences are noticed between data sets related to each variable (sea level, ocean mass and steric sea level), mostly due to data processing issues. Therefore, we perform the analysis using averages of the available data sets. For each period, we find that, when removing from the global mean sea level, the contributions of the global mean ocean mass and steric sea level (estimated for the 0–1,500 m ocean layer), there remains a residual signal displaying a positive slope of 0.3 ± 0.6 and 0.55 ± 0.6 mm/year over 2005–2012 and 2003–2012, respectively. Comparing with an ocean reanalysis and according to direct (but sparse) ocean temperature measurements below 1,500 m, it seems unlikely that the observed residual signal can be attributed to deep (below 1,500 m) ocean warming, in agreement with other recently published results. We estimate that it possibly reflects, at least partly, the signature of a missing upper ocean steric signal in regions uncovered by current observing systems. Our study also shows a steady warming increase since 2003 of the 700–1,500 m ocean layer (amounting ~0.2 mm/year in steric sea level equivalent), confirming previous findings, but seen in our study in each of the eight different steric data sets considered.

Solar radiation variability in Nigeria based on multiyear RegCM3 simulations

This study evaluates the seasonal and interannual variability of global solar irradiance over Nigeria as simulated by the International Centre for Theoretical Physics' (ICTP) Regional Climate Model version 3 (RegCM3) over the period 1997 to 2007 using ECHAM5 General Circulation Model as the lateral boundary conditions. The ability of RegCM3 in representing the climatology is also evaluated. The model was validated with two observed datasets viz: gridded weather observations by the Nigerian Meteorological Agency (NIMET) and the National Aeronautic and Space Administration (NASA). RegCM3 was found to replicates the essential features of global solar irradiance and mean air temperature climate for Nigeria and adequately simulates their seasonal variations over the selected climatic zones. The model over-estimates the NIMET observations across the whole zones whereas with NASA dataset RegCM3 gave a slight over-estimation during the rainy season in the south zone and a perfect fit with no under/over-estimation across the whole middle-belt and north climatic regions. The best simulations for seasonal cycle of global solar irradiance were obtained over North zone whereas the worst is over the South region for both the NIMET and NASA observations. The spatial structure of RegCM3 model climatology was found to reproduce the observed global solar irradiance climatologies, though in some months, RegCM3 slightly underestimates the observations. RegCM3 was also found to indicate a poor performance of the interannual variability of solar irradiance and mean air temperature over the different subregions.