Анализ динамики временных характеристик сезонов года в арктической тундре Западно-Сибирской равнины в условиях глобального изменения климата

Changes in surface air temperature over China under the 1.5 and 2.0 °C global warming targets

Climatic change is one of the most important issues of present times, therefore, world-wide interest in global warming and climate change has led to numerous trend detection studies. Anthropogenic interference in the environment is one of the greatest causes of the process of climatic change in several regions of the world. This study focuses on the variability and trends of the mean annual, seasonal and monthly surface air temperature in Junagadh (Saurashtra region) of Gujarat, during the period 1980-2011. This study investigated monthly, seasonal and annual climatic variability in Junagadh (Saurashtra region) of Gujarat based on mean maximum, mean minimum and mean air temperatures. One of the main results of this study was the confirmation of a significant warming trend in average temperatures in Junagadh (Saurashtra region) of Gujarat. Analysis of maximum and minimum temperatures revealed a warming trend for the annual and all seasonal series. The warming trend for the summer and winter seasons was statistically significant at P < 0.01 level with a rate of increase of 0.006 C/year and less 0.055C/year. The air temperature time series were analyzed, so that the variability and trends can be described.

Climate change is one of the most important issues of today’s World. Climate scientists have concluded that the earth’s surface air temperature warmed by 0.6 ± 0.2℃ during the 20th century, accompanied by changes in the hydrologic cycle. Of all the climate elements, temperature plays a major role in detecting climate change brought about by urbanization and industrialization. This study focuses on the variability and trends of the mean annual, seasonal and monthly surface air temperature in Taiz city, Republic of Yemen, during the period 1979-2006. The results of the analysis of the whole period reveal a statistically significant increasing trend in practically all the months and seasons. A tendency has also been observed towards warmer years, with significantly warmer summer and spring periods and slightly warmer autumn and winter, an increase of 1.79℃ and 1.18℃ has been observed in the mean summer and mean winter temperature, respectively. Positive trends of about 1.5℃ in the annual mean temperature were found for the whole period. The air temperature time series are analyzed, so that the variability and trends can be described.

A detailed analysis of the spatial and temporal changes in mean seasonal and annual surface air temperature (SAT) in the Arctic is presented mainly for the period 1951–2005. Mean seasonal and annual homogenized and complete series of SAT from up to 35 Arctic stations were used in the analysis. The focus in this paper is on the 11 years 1995–2005, a period which saw dramatic warming in the Arctic (&gt;1˚C for annual values in relation to the 1951–90 mean). An abrupt rise in SAT occurred in the mid-1990s and was most pronounced in autumn and winter (&gt;2˚C). The greatest warming in the period 1995–2005 occurred in the Pacific and Canadian regions (&gt;1˚C), while the lowest was in the Siberian region (0.82˚C). This period has been the warmest since at least the 17th century. In particular, 2005 was an exceptionally warm year (&gt;2˚C in relation to the 1951–90 mean) and was warmer than 1938, the warmest year in the 20th century. The seasonal and annual trends of the areally averaged Arctic SAT for the periods 1936–2005, 1951–2005 and 1976–2005 are positive, with the exception of winter and autumn for the first period. The majority of trends calculated for the last two periods are statistically significant. While there are varying opinions about the forces driving the present warming, it seems likely that the marked rise in SAT in the mid-1990s (mainly from 1994 to 1995) was caused by (i) a set of natural factors, (ii) non-linear effects of greenhouse-gas loading, or (iii) the combined effect of these two groups of factors.

Soil temperature is an important indicator of frozen ground status, driven at least partly by air temperature variability. In this study we apply singular spectrum analysis (SSA) to detect trends and oscillations in annual and seasonal time series of surface air temperature (SAT) and soil temperature (ST). We investigate soil temperatures at depths of 0.4, 1.6, and 3.2 m for five permafrost‐occupied regions in Russia. We use SAT data for 1902–1995 and ST data for 1960–1990. The trends show an increase in annual SAT and ST from the end of the 1960s across all five regions, and this warming exceeds that of the preceding period in the Central Siberian Plateau and Transbaikalia. Oscillations in annual SAT and ST time series are coincident in the West Siberian Plain (7.7 year period) and in the western Central Siberian Plateau and Transbaikalia (2.7 year period). In general, on a seasonal basis, 2–3 year oscillations in ST and SAT are coincident during winter, spring, and autumn across the regions and are also evident in the annual ST time series in the Central Siberian Plateau and Transbaikalia. We also find a decadal oscillation (9.8 year period), which is coincident for winter SAT and ST, over the western Central Siberian Plateau only. Although summer SAT and ST oscillations (5–8 year periods) are coincident for all investigated territories (except to the east of the Lena River), in the annual ST time series they are identified only for the West Siberian Plain. We document the degree to which SAT controls ST in each region and explore the causative factors for some of the dominant periods. The maximum effect of SAT increases on permafrost may be observed in the Central Siberian Plateau and Transbaikalia, while elsewhere the observed ST increases do not threaten permafrost areas.

A dataset from 763 national Reference Climate and Basic Meteorological Stations (RCBMS) was used to analyze surface air temperature (SAT) change in mainland China. The monthly historical observational records had been adjusted for urbanization bias existing in the data series of size-varied urban stations, after they were corrected for data inhomogeneities mainly caused by relocation and instrumentation. The standard procedures for creating area-averaged temperature time series and for calculating linear trend were used. Analyses were made for annual and seasonal mean temperature. Annual mean SAT in mainland China as a whole rose by 1.24°C for the last 55 years, with a warming rate of 0.23°C decade−1. This was close to the warming of 1.09°C observed in global mean land SAT over the period 1951–2010. Compared to the SAT before correction, after-corrected data showed that the urbanization bias had caused an overestimate of the annual warming rate of more than 19.6% during 1961–2015. The winter, autumn, spring, and summer mean warming rates were 0.28°, 0.23°, 0.23°, and 0.15°C decade−1, respectively. The spatial patterns of the annual and seasonal mean SAT trends also exhibited an obvious difference from those of the previous analyses. The largest contrast was a weak warming area appearing in central parts of mainland China, which included a small part of southwestern North China, the northwestern Yangtze River, and the eastern part of Southwest China. The annual mean warming trends in Northeast and North China obviously decreased compared to the previous analyses, which caused a relatively more significant cooling in Northeast China after 1998 under the background of global warming slowdown.

The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p = 0.04 ) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p = 0.05 ). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p = 0.01 ∼ 0.27 ) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p = 0.04 ∼ 0.42 ) with a peak over the higher latitudes.

We examine equilibrium climate responses to the shortwave and/or longwave direct radiative effect of mineral dust aerosol using the Global transport Model of Dust (GMOD) embedded within a general circulation model (GCM). The presence of mineral dust aerosol in the atmosphere is estimated to exert global mean shortwave and longwave radiative forcings (RF) of −0.25 W m−2 and +0.27 W m−2, respectively, at the top of the atmosphere (TOA) and −1.95 W m−2 and +0.61 W m−2 at the surface. Climatic effect of dust is simulated using two different approaches. In the first approach, monthly mean fields of dust simulated a priori are used in the radiative transfer module of the GCM to drive climate change, with levels of dust fixed during the climate integration (denoted as simulation FIXDST). In the second approach, dust aerosol interacts online with meteorology through the dust cycle and its direct radiative effect (denoted as simulation CPLD). With both longwave and shortwave RF of dust, predicted changes in global and annual mean surface air temperature and air temperature at 200 hPa are zero and +0.12 K, respectively, in FIXDST, and −0.06 K and +0.05 K in the CPLD simulation. The stronger cooling in CPLD than in FIXDST is a result of a 13% higher dust burden in CPLD with dust‐climate interactions. Although dust longwave radiative effect is predicted to offset a large portion of its shortwave effect on a global and annual mean basis, dust shortwave effect dominates during the daytime, and the longwave effect prevails at night, which is found to be very important for predictions of temperature. For example, over the Sahara Desert, the changes in annual mean, annual mean daytime, and annual mean nighttime surface air temperature are predicted to be +0.32 K, −0.11 K, and +0.68 K, respectively, in the FIXDST simulation. The longwave and shortwave radiative effects of dust are predicted to have different impacts on the dust cycle in CPLD simulation; the solar radiative effect reduces dust emissions by increasing surface humidity and by reducing surface wind speed, while the thermal effect increases dust uplift through opposite changes in the meteorological parameters.

A comparison is made of the calculated values of solar radiation incident on the upper atmospheric boundary with the measured values of surface temperature on the territory of the Crimean Peninsula. It is shown that the long-term temperature regime on the territory of the Crimean Peninsula is characterized by a stability. It is determined that the stability of the long-term regime of mean annual surface air temperatures is associated with the characteristics of the latitudinal distribution of solar radiation incident on the upper atmospheric boundary. The incident solar radiation increases in the regions of heat sources and decreases in the regions of heat sink. Stability of long-term mean annual values of surface air temperature is associated with the location of the Crimea on the boundary of the regions of heat sources and sinks. The study revealed the chronological structure of long-term changes in surface air temperature. The anomaly in the long-term surface air temperature variability is characterized by short-duration variations. An analysis is made of the chronological structure of interannual variability in surface air temperature on the territory of the peninsula. The dominant interannual and 2–3-year periodicities in the temperature regime variations are correlated with variations in incident solar radiation. In 62.7% of cases, the sign of interannual variability in surface air temperature corresponds to the sign of interannual variability in incident solar radiation. Thus it is shown that a small tendency in the long-term surface air temperature variability on the territory of the Crimean Peninsula, and the characteristics of its variations are determined largely by the specific character of the input and distribution of solar radiation incident on the upper atmospheric boundary.

Surface air temperature changes over the Tibetan Plateau: Historical evaluation and future projection based on CMIP6 models

A detailed analysis of the spatial and temporal changes in mean seasonal and annual surface air temperatures over the period of instrumental observations in the Arctic is presented. In addition, the role of atmospheric circulation in controlling the instrumental and decadal-scale changes of air temperature in the Arctic is investigated. Mean monthly temperature and temperature anomalies data from 37 Arctic, 7 sub-Arctic and 30 grid-boxes were used for analysis. The presented analysis shows that the observed variations in air temperature in the real Arctic (defined on the basis of climatic as opposed to other criteria, e.g. astronomical or botanical) are in many aspects not consistent with the projected climatic changes computed by climatic models for the enhanced greenhouse effect. The highest temperatures since the beginning of instrumental observation occurred clearly in the 1930s and can be attributed to changes in atmospheric circulation. The second phase of contemporary global warming (after 1975) is, at most, weakly marked in the Arctic. For example, the mean rate of warming for the period 1991–1995 was 2–3 times lower in the Arctic than the global average. Temperature levels observed in Greenland in the last 10–20 years are similar to those observed in the 19th century. Increases of temperature in the Arctic are more significant in the warm half-year than in the cold half-year. This seasonal pattern in temperature change confirms the view that positive feedback mechanisms (e.g. sea-ice–albedo–temperature) as yet play only a small role in enhancing temperature in the Arctic. Hypotheses are presented to explain the lack of warming in the Arctic after 1975. It is shown that in some parts of the Arctic atmospheric circulation changes, in particular in the cold half-year, can explain up to 10–50% of the temperature variance. For Arctic temperature, the most important factor is a change in the atmospheric circulation over the North Atlantic. The influence of atmospheric circulation change over the Pacific (both in the northern and in the tropical parts) is significantly lower. Copyright © 2000 Royal Meteorological Society

Basing on statistical processing of the data on Indian subcontinent climate available in the USSR the estimates have boon obtained of geographical distribution of temperature and precipitation fields with changing mean annual surface air temperature of the northern hemisphere.&#x0D; &#x0D; Signs of changes of surface air temperature averaged over the hemisphere and these over India are usually the same. But it appears, 1hat for every season there exist regions on the subcontinent where the probability of such events is less than 0.5. In summer (Jun-Aug) the area of these regions is maximum. They Include northern, northwestern and central parts of the subcontinent.&#x0D; &#x0D; It is shown that an increase in mean annual surface air temperature of the northern hemisphere is accompanied by an increase of precipitation totals over the entire India, specially along the western coast of the subcontinent. However, to obtain the detailed rainfall pattern associated with global thermal regime changes, a more comprehensive analysis is needed.&#x0D;

This study examines the sampling error uncertainties in the monthly surface air temperature (SAT) change in China over recent decades, focusing on the uncertainties of gridded data, national averages, and linear trends. Results indicate that large sampling error variances appear at the station-sparse area of northern and western China with the maximum value exceeding 2.0 K2 while small sampling error variances are found at the station-dense area of southern and eastern China with most grid values being less than 0.05 K2. In general, the negative temperature existed in each month prior to the 1980s, and a warming in temperature began thereafter, which accelerated in the early and mid-1990s. The increasing trend in the SAT series was observed for each month of the year with the largest temperature increase and highest uncertainty of 0.51 ± 0.29 K (10 year)−1 occurring in February and the weakest trend and smallest uncertainty of 0.13 ± 0.07 K (10 year)−1 in August. The sampling error uncertainties in the national average annual mean SAT series are not sufficiently large to alter the conclusion of the persistent warming in China. In addition, the sampling error uncertainties in the SAT series show a clear variation compared with other uncertainty estimation methods, which is a plausible reason for the inconsistent variations between our estimate and other studies during this period.

Studies of the present-day Arctic climate are becoming increasingly relevant and in high demand in the light of the observed global warming and the expansion of long-term programs for the development of the Arctic regions. A quantitative assessment of changes and variability in surface air temperature (SAT) is presented for the climate norm period of 1991–2020, based on data from 31 meteorological stations (MSs), which reflect the diversity of climatic conditions in the area studied. Average monthly SAT values were taken as indicators of changes in the thermal regime, and the standard deviations (SD) of average monthly SAT were used as indicators of the thermal regime variability. The annual course of SAT (one maximum and one minimum) mainly reflects the radiation factor. The annual course of SD (in the northern part of the area — one maximum and one minimum, in the southern part — two maxima and two minima) reflects the patterns of the atmospheric and ocean circulation and the type of the underlying surface. The assessment of the changes and variability in the thermal regime of the surface atmosphere was based on a comprehensive analysis of the annual cycle of indicators on the SAT-SD plane using closed SAT-SD curves characterizing annual and seasonal cycles. 31 SAT- SD curves were classified, and the corresponding regions of the Barents and Kara Seas were identified. A typical SAT-SD curve was obtained for each region. The boundaries of natural climatic seasons (NCS) were determined based on a comparative analysis of the seasonal cycle of SAT-SD indicators and the absolute SAT-SD values characteristic of different seasons within each region. Zoning and determining the duration of the NCS refine the general understanding of the climate of the Western sector of the Arctic.

A Bayesian approach is applied to the observed global surface air temperature (SAT) changes using multimodel ensembles (MMEs) of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations and single-model ensembles (SMEs) with the ECHO-G coupled climate model. A Bayesian decision method is used as a tool for classifying observations into given scenarios (or hypotheses). The prior probability of the scenarios, which represents a degree of subjective belief in the scenarios, is changed into the posterior probability through the likelihood where observations enter, and the posterior is used as a decision function. In the identical prior case the Bayes factor (or likelihood ratio) becomes a decision function and provides observational evidence for each scenario against a predefined reference scenario. Four scenarios are used to explain observed SAT changes: “CTL” (control or no change), “Nat” (natural forcing induced change), “GHG” (greenhouse gas–induced change), and “All” (natural plus anthropogenic forcing–induced change). Observed and simulated global mean SATs are decomposed into temporal components of overall mean, linear trend, and decadal variabilities through Legendre series expansions, coefficients of which are used as detection variables. Parameters (means and covariance matrices) needed to define the four scenarios are estimated from SMEs or MMEs. Taking the CTL scenario as reference one, application results for global mean SAT changes for the whole twentieth century (1900–99) show “decisive” evidence (logarithm of Bayes factor &amp;gt;5) for the All scenario only. While “strong” evidence (log of Bayes factor &amp;gt;2.5) for both the Nat and All scenarios are found in SAT changes for the first half (1900–49), there is decisive evidence for the All scenario for SAT changes in the second half (1950–99), supporting previous results. It is demonstrated that the Bayesian decision results for global mean SATs are largely insensitive to both intermodel uncertainties and prior probabilities.