Daily Mean Surface Air Temperature Research Articles

Historical global and regional spatiotemporal patterns in daily temperature

The abrupt increase in surface air temperature over the last few decades has received abundant scholarly and popular attention. However, less attention has focused on the specific nature of the warming spatially and seasonally, using high-resolution reanalysis output based on historical temperature observations. This research uses the European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5) output to identify spatiotemporal features of daily mean surface air temperature, defined both as the mean of the maximum and minimum temperatures over the calendar day (“meanmaxmin”) and as the mean of the 24 hourly observations per day (“meanhourly”), across the terrestrial Earth. Results suggest temporal warming throughout the year, with several “hot spots” of significantly increasing temperature, including in the Arctic transition seasons, Northern Hemisphere mid-latitudes in July, Eurasia in spring, Europe and the lower latitudes in summer, and tropical autumn. Cooling is also observed, but generally at rates more likely to be statistically insignificant than warming rates. These trends are nearly identical regardless of whether calculated as “meanmaxmin” or “meanhourly.” These results may assist scientists and citizens to understand more fully observed agricultural, commercial, ecological, economic, and recreational trends in light of climate change considerations.

Microclimatic Variation in Miri Region (NW Borneo): Inference from Rainfall and Temperature Trends, Isotopic Signature and Air Mass Movement

Trend analysis is frequently utilised to identify the changes in meteorological and hydrologic time series data, such as rainfall and temperature. The variations in the intensity, rainfall pattern and temperature have gradually changed globally. Hence, in this study, an attempt was made to analyse the decadal rainfall and surface air temperature data to understand the microclimatic variations in the Miri coastal region of NW Borneo. A data series of records for daily total rainfall amount and daily surface temperature of 11 years from 2010 to 2021 was studied and analysed. In addition, representative rainwater and groundwater samples were collected and analysed for hydrochemical parameters and oxygen and hydrogen isotopes. A detailed literature review was carried out on rainfall patterns in Malaysia, which was used for the comparative study. Interpretation of results shows that the northeast monsoon (NEM) contributed a higher total rainfall rate with lower daily mean surface air temperature over the years compared to the southwest monsoon (SWM). The recorded data for rainfall amounts in SWM for the month of May, July, August and September were higher, particularly for the years 2010 and 2020. During NEM, a higher rainfall amount was recorded in the month of January for several years. February month has always been among the driest month in NEM, and September has been the wettest month throughout the year during SWM. The isotopic values of rainwater indicate a similar moisture source to the regional precipitation trend. Groundwater isotopes reveal the low water-rock ratio of retrograde exchange between water and primary silicate minerals in the aquifer. The moisture source of the precipitation was contributed from both oceanic and continent, affecting the rainfall intensity in this region. This study is a crucial outcome to determine the potential impacts of microclimatic variations on the rainfall patterns in the Miri coastal region.

Windborne migration routes of newly-emerged fall armyworm from Qinling Mountains–Huaihe River region, China

The fall armyworm (FAW, Lepidoptera: Noctuidae), Spodoptera frugiperda (J. E. Smith), invaded China in mid-December 2018; since then, it has become a great threat to Chinese agricultural production. Qinling Mountains–Huaihe River region (QM–HRR) is the transitional zone between northern and southern China, an important region for both corn and wheat production. Based on the actual occurrence of QM–HRR invaded by FAW in 2019, daily mean surface air temperature and nocturnal wind conditions at 925 hPa were examined, and migratory routes of FAW moths originated in QM–HRR were modeled by a forward-trajectory-analysis approach. The results indicated that migratory activities of FAW adults emerged in QM–HRR were initiated from late June. The moths from western QM–HRR, where has complex topographic terrain, mainly flied to Ningxia and Inner Mongolia before mid September. However, FAW moths from the eastern QM–HRR primarily engaged in high-altitude northward transport assisted by the prevailing southerly winds before mid August, and the North China Plain was identified as the main destination of FAW. Meanwhile, the migration trajectories of FAW moths had a possibility to reach the Northeast China Plain. From mid August, FAW moths in eastern QM–HRR largely migrated southward and returned to the Yangtze River Valley. This study provides detailed information on the occurrence and migration routes of FAW moths from QM–HRR and will be helpful for early warning and development of integrated pest management strategies for the control of this exotic insect pest.

Comparisons of urban-related warming in Beijing using different methods to calculate the daily mean temperature

To evaluate the contribution of urban surface expansion to regional warming using different methods to calculate the daily mean surface air temperature (SAT), satellite-based images displaying urban surface expansion over the past 37 years (1980–2016) across China were collected for use in nested numerical experiments using the weather research and forecasting (WRF) regional climate model. The contribution of urban surface expansion to urban-related warming was determined using the daily mean SAT averages based on four time records each day (00, 06, 12, and 18 h UTC, T 4) and averages of the SAT maximum ( T max) and minimum ( T min) ( T xn). The contribution of urban surface expansion to urban-related warming (relative value) in Beijing was 0.110°C per decade (22.8% of total warming) for T 4 and 0.094°C per decade (20.2%) for T xn. The values obtained when using T 4 were larger than those obtained when using T xn. Differences in the urban-related warming calculated using T 4 and T xn could be attributed to the smaller changing trends in T xn in the urban-surface expansion experiment, which resulted from a large changing trend in T min and a much smaller changing trend in T max. The changes in the diurnal cycle of the energy budget due to urban surface expansion induced changes in the diurnal cycle of SAT, as evidenced by the four time records each day, as well as T max and T min. This was especially true for periods of intense urban surface expansion, although the annual mean SAT calculated using T xn was larger than that calculated using T 4. The increase in impervious area (walls, streets, etc.) due to urban surface expansion, as well as the widespread use of building materials with a large heat capacity resulted in a marked increase in ground heat flux in the daytime. This restricted the increase in SAT in the daytime, but promoted it at night. The increases in SAT due to urban surface expansion were not symmetrical, being smaller in the daytime and larger at night.

Modelling Daily Mean Surface Air Temperature Calculated from Different Methods and Its Impact on Urban-Related Warming Evaluations over Guangzhou and Shenzhen Using the WRF Model

The impacts of urban surface expansion, based on satellite-derived data displaying urban surface expansion in China at different spatial scales from 1980 to 2016, were investigated using nested dynamical downscaling methods with the Weather Research and Forecasting (WRF) regional climate model at a 3.3-km resolution over a city and city cluster scale. Urban-related warming, based on daily mean surface air temperature at 2 m (SAT), calculated from the averages of four time records each day (00, 06, 12, and 18 h UTC, T4) and averages of SAT maximum (Tmax) and minimum (Tmin) (Txn), was evaluated. Differences in urban-related warming contributions calculated using T4 and Txn were small, whereas annual mean SAT and trends calculated using Txn were respectively and significantly larger and smaller than those calculated using T4 over Guangzhou and Shenzhen, excluding the trends over middle-northern Shenzhen. The differences in annual mean SAT calculated using T4 and Txn are attributed to nonlinear or asymmetric variations with time for the diurnal cycle of SAT. Meanwhile, differences in trends between T4 and Txn are interpreted as a strong trend for Tmin and a weak one for Tmax, which mitigated the trend for Txn. The impacts on the evaluations of urban-related warming contributions calculated from different methods were the largest over the areas classified as urban surfaces in both time periods (U2U), especially during intense urban-surface-expansion periods between 2000 and 2016. The subregional performances in the changes in annual mean SAT, trends, and urban-related warming are attributed to urban-surface-expansion, which induced varied changes in the diurnal cycle due to asymmetric warming during the daytime and nighttime over different subregions.

Characteristics of Winter Surface Air Temperature Anomalies in Moscow in 1970–2016 under Conditions of Reduced Sea Ice Area in the Barents Sea

On the basis of observational data on daily mean surface air temperature (SAT) and sea ice concentration (SIC) in the Barents Sea (BS), the characteristics of strong positive and negative winter SAT anomalies in Moscow have been studied in comparison with BS SIC data obtained in 1949–2016. An analysis of surface backward trajectories of air-particle motions has revealed the most probable paths of both cold and warm air invasions into Moscow and located regions that mostly affect strong winter SAT anomalies in Moscow. Atmospheric circulation anomalies that cause strong winter SAT anomalies in Moscow have been revealed. Changes in the ways of both cold and warm air invasions have been found, as well as an increase in the frequency of blocking anticyclones in 2005–2016 when compared to 1970–1999. The results suggest that a winter SIC decrease in the BS in 2005–2016 affects strong winter SAT anomalies in Moscow due to an increase in the frequency of occurrence of blocking anticyclones to the south of and over the BS.

OBSERVATIONS-BASED COMPUTATIONAL ANALYTICS ON LOCAL CLIMATE DYNAMICS. PART 3: FORECASTING

Computational decision making is discussed in application to seasonal temperature forecasts taking into account inevitable nonlinear nature of local climate systems and deficiency of data on reliable observations. We focus on temperature extremes in terms of daily means and first involve the alternative conceptual model of local climate dynamics (the model of hysteresis regulation with double synchronization, so-called HDS-model) into such analytics. Recent years the HDS-model is describing successfully abnormal interannual temperature variability, on the basis of which it becomes potentially possible to extend forecasts of local daily means up to more than 1 year in future. In this connection the novel method of bifurcation traps is proposed, realized and tested. Results of processing the time series of temperature observations on daily mean surface air temperature illustrate peculiarities of this method in comparison with the traditional viewpoint on the forecasts. We believe that the discussion could be interesting in science and practice in order to increase the confidence of estimations on coming climate changes.

OBSERVATIONS-BASED COMPUTATIONAL ANALYTICS ON LOCAL CLIMATE DYNAMICS. PART 2: SEASONALITY

The paper continues the discussion concerning the computational decision making on evolution of local climate dynamics taking into account inevitable nonlinear nature of such systems and deficiency of reliable data on its dynamics. Here we focus on seasonality in the context of bifurcation phenomena described by the model of the hysteresis regulator with double synchronization (so-called HDS-model). From this conception, the method of structuring and analysis of meteorological data (method of relative scales) is proposed, where new useful information on local seasonal evolution becomes available. First of all, it concerns increase in analytical resolution (daily description in a climate scale). The key procedures of this method provide building the specialized seasonal structures in relative time scales. Advantages are illustrated in comparison with the traditional processing the time series of temperature observations on daily mean surface air temperature over last century. We believe that the results could be interesting in order to increase the confidence of estimations on coming climate changes.

OBSERVATIONS-BASED COMPUTATIONAL ANALYTICS ON LOCAL CLIMATE DYNAMICS: CHANGE-POINTS

The paper concerns the problem on the computational decision making on evolution of local climate dynamics taking into account inevitable nonlinear nature of such systems and deficiency of reliable data on climate dynamics. With this purpose we consider annual temperature variation in the context of the bifurcation phenomena under the hysteresis regulation with double synchronization. The corresponding conceptual model (HDS-model) provides the homogeniety of the analysed states as well as the ability to derive some constituent of regional (external) impacts on a local climate system. In accordance with the peculiarities of HDS-model dynamics, we formalize three mechanisms of local temperature changes and introduce the corresponding indicators of change-points. It allows to substitute, at least partly, expert analytics concerning identification of qualitative changes in local climate dynamics. Examples of applications of the indicators are presented on the basis of processing the time series of temperature observations on daily mean surface air temperature made over last century. We believe that the results could be applied in order to increase the confidence of estimations about local climate changes.

Global risk of deadly heat

Climatic conditions that challenge human thermoregulatory capacity currently affect around a quarter of the world’s population annually. Such conditions are projected to increase in line with CO2 emissions particularly in the humid tropics. Climate change can increase the risk of conditions that exceed human thermoregulatory capacity1,2,3,4,5,6. Although numerous studies report increased mortality associated with extreme heat events1,2,3,4,5,6,7, quantifying the global risk of heat-related mortality remains challenging due to a lack of comparable data on heat-related deaths2,3,4,5. Here we conducted a global analysis of documented lethal heat events to identify the climatic conditions associated with human death and then quantified the current and projected occurrence of such deadly climatic conditions worldwide. We reviewed papers published between 1980 and 2014, and found 783 cases of excess human mortality associated with heat from 164 cities in 36 countries. Based on the climatic conditions of those lethal heat events, we identified a global threshold beyond which daily mean surface air temperature and relative humidity become deadly. Around 30% of the world’s population is currently exposed to climatic conditions exceeding this deadly threshold for at least 20 days a year. By 2100, this percentage is projected to increase to ∼48% under a scenario with drastic reductions of greenhouse gas emissions and ∼74% under a scenario of growing emissions. An increasing threat to human life from excess heat now seems almost inevitable, but will be greatly aggravated if greenhouse gases are not considerably reduced.

Robust increase in extreme summer rainfall intensity during the past four decades observed in China.

Global warming increases the moisture holding capacity of the atmosphere and consequently the potential risks of extreme rainfall. Here we show that maximum hourly summer rainfall intensity has increased by about 11.2% on average, using continuous hourly gauge records for 1971–2013 from 721 weather stations in China. The corresponding event accumulated precipitation has on average increased by more than 10% aided by a small positive trend in events duration. Linear regression of the 95th percentile daily precipitation intensity with daily mean surface air temperature shows a negative scaling of −9.6%/K, in contrast to a positive scaling of 10.6%/K for hourly data. This is made up of a positive scaling below the summer mean temperature and a negative scaling above. Using seasonal means instead of daily means, we find a consistent scaling rate for the region of 6.7–7%/K for both daily and hourly precipitation extremes, about 10% higher than the regional Clausius-Clapeyron scaling of 6.1%/K based on a mean temperature of 24.6 °C. With up to 18% further increase in extreme precipitation under continuing global warming towards the IPCC’s 1.5 °C target, risks of flash floods will exacerbate on top of the current incapability of urban drainage systems in a rapidly urbanizing China.

Modeling the contribution of long‐term urbanization to temperature increase in three extensive urban agglomerations in China

AbstractThis study simulated the effects of changes in the underlying surface induced by long‐term urbanization on trends in surface air temperature (SAT) over three extensive urban agglomerations (Beijing‐Tianjin‐Hebei, BTH; the Yangtze River Delta, YRD; and the Pearl River Delta, PRD) in China during 1980–2009. To isolate the effects of continuous urban expansion on SAT with the least computation cost, we employed the Community Land Model (CLM4.5) in an off‐line mode for a relatively long period. Based on a high‐quality land use data set dating back to the 1980s, two scenarios were designed to represent the distributions of both nonurban and historically urban land use. By comparing the results of two numerical experiments, urban‐induced warming in daily mean SAT (Tmean) over the three urban agglomerations, BTH, YRD, and PRD, were found to be 0.13°C/30 yrs, 0.12°C/30 yrs, and 0.09°C/30 yrs, contributing about 9.70%, 10.3%, and 9.68% to the mean long‐term SAT trends, respectively. In addition, a higher contribution of urban‐related warming was found in winter for BTH and in summer for the other two regions. However, urban‐related warming had no significant effect on the trends of daily maximum SAT (Tmax) when compared with daily minimum SAT (Tmin). Specifically, at a local scale, the contributions of urban warming to the background warming in three representative cities, Beijing, Shanghai, and Guangzhou, were 12.7%, 29.0%, and 23.6%, respectively.

한반도 혹한 발생시 종관장 특성과 대규모 기후 변동성 간의 연관성

This study explores the synoptic characteristics of cold days over South Korea and their relationship with large-scale climate variability. The cold day, which is different from cold surge, is defined when daily-mean surface air temperature, averaged over 11 KMA stations, is colder than 1-percentile temperature in each year by considering its long-term trend over 1960~2012. Such event is detected by quantile regression and the related synoptic patterns are identified in reanalysis data. Composite geopotential height anomalies at 500 hPa show that cold days are often preceded by positive anomalies in high latitudes and negative anomalies in midlatitudes on the west of Korea. While the formers are quasi-stationary and quasi-barotropic, and often qualified as blocking highs, the latters are associated with transient cyclones. At cold days, the north-south dipole in geopotential height anomalies becomes west-east dipole in the lower troposphere as high-latitude anticyclone expands equatorward to the Northern China and mid-latitude cyclone moves eastward and rapidly develops over the East Sea. The resulting northerlies cause cold days in Korea. By performing composite analyses of large-scale climate indices, it is further found that the occurrence of these cold days are preferable when the Arctic Oscillation is in its negative phase and/or East Asian monsoon circulation and Siberian high are anomalously strong.

Multiscale Atmospheric Dynamics: Cross-Frequency Phase-Amplitude Coupling in the Air Temperature

Interactions between dynamics on different temporal scales of about a century long record of data of the daily mean surface air temperature from various European locations have been detected using a form of the conditional mutual information, statistically tested using the Fourier-transform and multifractal surrogate data methods. An information transfer from larger to smaller time scales has been observed as the influence of the phase of slow oscillatory phenomena with the periods around 6-11yr on the amplitudes of the variability characterized by the smaller temporal scales from a few months to 4-5yr. The overall effect of the slow oscillations on the interannual temperature variability within the range 1-2 ° C has been observed in large areas of Europe.

Present and projected degree days in China from observation, reanalysis and simulations

Degree days are usually defined as the accumulated daily mean temperature varying with the base temperature, and are one of the most important indicators of climate changes. In this study, the present-day and projected changes of four degree days indices from daily mean surface air temperature output simulated by Max Planck Institute, Earth Systems Model of low resolution (MPI-ESM-LR) model are evaluated with the high resolution gridded-observation dataset and two modern reanalyses in China. During 1979–2005, the heating degree days (HDD) and the numbers of HDD (NHDD) have decreased for observation, reanalyses (ERA-Interim and NCEP/NCAR) and model simulations (historical and decadal experiments), consistent with the increasing cooling degree days (CDD) and the numbers of CDD (NCDD). These changes reflect the general warming in China during the past decades. In most cases, ERA-Interim is closer to observation than NCEP/NCAR and model simulations. There are discrepancies between observation, reanalyses and model simulations in the spatial patterns and regional means. The decadal hindcast/forecast simulation performance of MPI-ESM-LR produce warmer than the observed mean temperature in China during the entire period, and the hindcasts forecast a trend lower than the observed. Under different representative concentration pathway (RCP) emissions scenarios, HDD and NHDD show significant decreases, and CDD and NCDD consistently increase during 2006–2100 under RCP8.5, RCP4.5 and RCP2.6, especially before the mid-21 century. More pronounced changes occur under RCP8.5, which is associated with a high rate of radiative forcing. The 20th century runs reflect the sensitivity to the initial conditions, and the uncertainties in terms of the inter-ensemble are small, whereas the long-term trend is well represented with no differences among ensembles.

Twenty‐first century changes in daily temperature variability in CMIP3 climate models

AbstractChanges in the daily mean surface air temperature distribution between the time periods 1981–2000 and 2081–2100 in 15 climate model simulations participating in the Third Coupled Model Intercomparison Project (CMIP3) are analysed. Variability is defined by first removing the annual cycle after which the range and skewness of the temperature distribution are calculated. Present‐day biases for the different aspects of the distribution are also studied, using the ERA‐Interim reanalysis as the main reference dataset. The variability in the simulations compared to reanalysis is generally smaller over oceans and larger over land areas. For skewness of the temperature distribution, inter‐model variability is much larger and systematic model biases are more local. Model performance regarding the mean values is potentially a good indicator of the model performance for range, but for skewness this relation is weaker. For future changes in the range of daily temperature variability, the models agree best over the Northern Hemisphere mid‐ to high‐latitude land areas, where variability is simulated to decrease. Elsewhere, the inter‐model differences are generally larger than the multi‐model mean changes. Near the sea ice border, significant negative correlations are found between the changes in mean temperature and the temperature range. For changes in skewness, the signal‐to‐noise ratio is very low over most areas and no firm conclusions can be drawn from the ensemble results. Our results are relatively insensitive to the precise definition of the range and skewness metrics and to internal variability of climate.

Changes in the Amplitude of the Temperature Annual Cycle in China and Their Implication for Climate Change Research

Abstract Climate change is not only reflected in the changes in annual means of climate variables but also in the changes in their annual cycles (seasonality), especially in the regions outside the tropics. In this study, the ensemble empirical mode decomposition (EEMD) method is applied to investigate the nonlinear trend in the amplitude of the annual cycle (which contributes 96% of the total variance) of China’s daily mean surface air temperature for the period 1961–2007. The results show that the variation and change in the amplitude are significant, with a peak-to-peak annual amplitude variation of 13% (1.8°C) of its mean amplitude and a significant linear decrease in amplitude by 4.6% (0.63°C) for this period. Also identified is a multidecadal change in amplitude from significant decreasing (−1.7% decade−1 or −0.23°C decade−1) to significant increasing (2.2% decade−1 or 0.29°C decade−1) occurring around 1993 that overlaps the systematic linear trend. This multidecadal change can be mainly attributed to the change in surface solar radiation, from dimming to brightening, rather than to a warming trend or an enhanced greenhouse effect. The study further proposes that the combined effect of the global dimming–brightening transition and a gradual increase in greenhouse warming has led to a perceived warming trend that is much larger in winter than in summer and to a perceived accelerated warming in the annual mean since the early 1990s in China. It also notes that the deseasonalization method (considering either the conventional repetitive climatological annual cycle or the time-varying annual cycle) can also affect trend estimation.

Interannual Variation of Cold Frontal Activity in Spring in Mongolia

In Mongolia and northern China, most of the dust storms in spring occur in association with a passage of cold front formed at the leading edge of cold air outbreak. In this study, we propose an index to evaluate a cold frontal activity by identifying “cooling days”. These are days when there is a strong decrease in daily mean surface air temperature of greater than 5 K, using the European Centre for Medium-Range Weather Forecasts 45-year reanalysis data for 1967 through 2002.The geographical distribution of seasonal mean cooling day frequencies showed that Mongolia is the most frequent area over Eurasia with the highest frequency of cooling days in spring.Interannual variations in the area-averaged frequency of cooling days in Mongolia showed that most of the years with less-than-average frequencies in spring occurred in the last 20 years. To determine the main cause(s) for the differences in cooling day frequencies, we conducted a composite study for the 8 years with the highest area-averaged spring cooling day frequencies (active years), and the 8 years with the lowest (inactive years). This study revealed that there were no apparent differences between the active and inactive years with regard to geographical route of cold air, or total number of cooling events. In contrast, the cooling intensity in the inactive years was approximately 20% below that in the active years. These results suggest that recent weakening of cooling intensity over eastern Mongolia has resulted from warming over Siberia.

Relationships between Climate Variability and Winter Temperature Extremes in the United States

Time series representing two of the climate systems leading patterns of variability, namely El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO), are used together with 50 yr of daily mean surface air temperature data over the conterminous United States to diagnose relationships between winter temperature extremes and interannual climate variability. The aim is to focus attention on some of the physical phenomena that climate models must be able to simulate in order to be deemed credible for use in weather and climate forecasts and assessments. Since the 1950s there has been considerable decadal variability in winter surface air temperature extremes. At most locations in the United States the number of daily extremes is reduced during El Niño, and increased during La Niña and ENSO-neutral years. These changes are qualitatively consistent with a decrease in the daily mean surface air temperature variance during El Niño relative to La Niña and ENSO neutral. Changes in the number of warm extremes during a particular AO phase are largely compensated for by changes in the number of cold extremes, so that the net change in the numbers of surface air temperature extremes is close to zero. However, the AO is associated with larger changes in mean temperature than ENSO.

Statistical Procedures for Making Inferences about Climate Variability

Abstract A statistical procedure is described for making inferences about changes in climate variability. The fundamental question of how to define climate variability is first addressed, and a definition of intrinsic climate variability based on a “prewhitening” of the data is advocated. A test for changes in variability that is not sensitive to departures from the assumption of a Gaussian distribution for the data is outlined. In addition to establishing whether observed differences in variability are statistically significant, the procedure provides confidence intervals for the ratio of variability. The technique is applied to time series of daily mean surface air temperature generated by the Oregon State University atmospheric general circulation model. The test application provides estimates of the magnitude of change in variability that the procedure should be likely to detect.