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Global warming and its climatic and environmental effects have mainly been investigated in terms of the absolute warming rate. Little attention has been paid to the contribution of absolute warming rate to variability on various time scales of surface air temperature (SAT), which may be a more direct index for measuring the ecoclimatic effect of warming trend. The present study analyzed the role of secular warming trend in the variations of global land SAT for 1901–2016. Less than one-third of annual SAT variations were contributed by the warming trend over large parts of the globe generally. The ratios were up to two-thirds over eastern South America, parts of South Africa and the regions around the southwestern Mediterranean and Sunda islands where the absolute warming rate was moderate but the endemic species were undergoing exceptional loss of habitat. The ratios also exhibited smallest seasonal difference over these regions. Therefore, the ratio of the warming trend to the SAT variations may be a better measure compared to the absolute warming rate for the local ecoclimate. We should also pay more attention to the regions with high ratio, not only the regions with the high absolute warming rate. [ABSTRACT FROM AUTHOR]

This study investigates the variability of summer surface air temperature (SAT) over northern Eurasia and its precursory signals in the tropical Atlantic and northern Asian land. The leading mode of summer SAT variations features a northern Eurasian SAT tripole (NEST) pattern, with two same‐sign SAT anomaly regions over eastern Europe–western Siberia and the Far East region and an opposite‐sign SAT anomaly region around the Baikal Lake. It is found that sea surface temperature (SST) or SAT anomalies in the tropical Atlantic and rainfall‐soil moisture anomalies around the Baikal Lake during May can modulate the NEST pattern. The SST anomalies in the tropical Atlantic persist from May to summer and induce a downstream zonal wave train across northern Eurasia, consequently causing the variation in the summer NEST pattern. May rainfall anomalies around the Baikal Lake affect the overlying atmospheric circulation during summer through the 'memory' effect of soil moisture and the soil moisture‐rainfall interaction, correspondingly modulating the downstream wave train and the associated NEST pattern. Based on the above results, a statistical prediction model is established using the two precursory signals, that is, SAT in the tropical Atlantic and rainfall around the Baikal Lake during May. The leave‐three‐out cross‐validation shows that the model has a high skill in predicting the summer NEST pattern, with a correlation coefficient of 0.51 (significant at the 99.8% confidence level) between observation and prediction during the period 1980–2016. [ABSTRACT FROM AUTHOR]

The daily average land surface air temperature (SAT) simulated by 8 CMIP5 models historical experiments and that from NCEP data during 1960–2005, are used to evaluate the performance of the CMIP5 model based on detrended fluctuation analysis (DFA) method. The DFA results of NCEP data show that SAT in most regions of the world exhibit long-range correlation. The scaling exponents of NCEP SAT show the zonal distribution characteristics of larg in tropics while small in medium and high latitudes. The distribution characteristics of the zonal average scaling exponents of CMCC-CMS, GFDL-ESM2G, IPSL-CM5A-MR are similar to that of NCEP data. From the DFA errors of model-simulated SAT, the performance of IPSL-CM5A-MR is the best among the 8 models throughout the year, the performance of FGOALS-g2 is good in spring and summer, GFDL-ESM2G is the best in autumn, CNRM-CM5 and CMCC-CMS is good in winter. The scaling exponents of model-simulated SAT are closer to that of NCEP data in most areas of the mid-high latitude on the northern hemisphere. However, simulations of SAT in East Asia and Central North American are generally less effective. In spring, most models have better performance in Siberian (SIB), Central Asia (CAS) and Tibetan (TIB). SAT in Northern Europe area are well simulated by most models in summer. In autumn, areas with better performance of most models are Mediterranean, SIB and TIB regions. In winter, SAT in Greenland, SIB and TIB areas are well simulated by most models. Generally speaking, the performance of CMIP5 models for SAT on global continents varies in different seasons and different regions.

The effect of atmospheric circulation on monthly, seasonal and annual mean surface air temperature trends in the years 1979–2018 is studied by applying a trajectory-based method on the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data. To the extent that the method captures the effects of atmospheric circulation, the results suggest that circulation trends only had a minor impact on observed annual mean temperature trends in most areas. Exceptions include, for example, a decrease in annual mean warming by about 1 °C in western Siberia, and increased warming in central Europe and the Arctic Ocean. However, the effect of circulation trends on seasonal and particularly monthly temperature trends is more substantial. Subtracting the effect of circulation changes from the ERA5 temperature trends leaves residual trends with a smoother annual cycle than the original trends. The residual monthly mean temperature trends also tend to agree better with the multi-model mean temperature trends from models in the 5th Coupled Model Intercomparison Project (CMIP5) than the original ERA5 trends do, with a 42% decrease in the mean square difference over the global land area. However, the corresponding decrease in the mean square difference of the annual mean temperature trends is only 6%. [ABSTRACT FROM AUTHOR]

The western Pacific pattern (WP), characterized by a meridional dipole structure in geopotential height anomalies centred at approximately 50°N over the North Pacific, is one of the most prominent teleconnection patterns over the Northern Hemisphere (NH) in boreal winter. Several methods to identify the WP exist in the literature. Based on eight WP indices defined by different methods, this study analyses their dominant frequencies, spatial structures, climatic impacts, and relations to the Arctic Oscillation (AO) and El Niño‐Southern Oscillation (ENSO). The correlation coefficients of the eight WP indices show a considerable spread, but most of them are statistically significant. Most of the WPIs capture a pronounced power peak at approximately 5 years. The meridional dipole structure of the WP can be captured by all the WPIs, but it shows a large spread in the location of the centre. Several WPIs are significantly correlated with the winter AO, with marked signals of atmospheric anomalies over the Arctic region. The connections of the WP with the simultaneous winter ENSO depend largely on their definitions. The impacts of the WP on the surface air temperature over many parts of Eurasia and North America are also sensitive to its definition. Differences in surface air temperature anomalies are related to differences in the spatial structure of the WP as captured by the range of WPIs. [ABSTRACT FROM AUTHOR]

The soil temperature (ST) is closely related to the surface air temperature (AT), but their coupling may be affected by other factors. In this study, significant effects of the AT on the underlying ST were found, and the time taken to propagate downward to 320 cm can be up to 10 months. Besides the AT, the ST is also affected by memory effects—namely, its prior thermal conditions. At deeper depth (i.e., 320 cm), the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season. At shallower layers (i.e., < 80 cm), the effects of the AT may be blocked by the snow cover, resulting in a poorly synchronous correlation between the AT and the ST. In northeastern China, this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring. Due to the thermal insulation effect of the snow cover, the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period. These findings may be instructive for better understanding ST variations, as well as land-atmosphere interactions. [ABSTRACT FROM AUTHOR]

The current research reports a univariate analysis of 2 important climatological parameters surface air temperature and rainfall over North East India over annual scale characterized by various degrees of non-linearity. An Autocorrelation study reveals that although the surface air temperature is characterized by an approximate sinusoidal pattern, the rainfall has no apparent pattern. As both the time series have continuous random variables associated with it we need a discretization to make them suitable for study in time domain. First, we test for Markovian behavior against serial independence. By appropriate discretization of both the time series the transition probabilities are computed to test for Two State Markov Chain model with different orders. A chi square test reveals that although the surface air temperature time series follows the Markov Chain model of 1st Order, the annual rainfall is serially independent. The steady state transition probabilities are computed for surface air temperature and subsequently autoregressive models upto order 4 with the help of Yule-Walker Equations. It was further observed that the 2nd order model satisfied the stationarity conditions. Subsequently to have a general overview of the stationarity, Unit Root Test is carried out for both the time series. For surface air temperature a root was found to exist inside the circumference of the unit circle while for rainfall time series finds a root of the characteristic equation outside unit circle. This indicates stationarity of the surface air temperature time series for its 1st order differences and non-stationarity of rainfall time series. Based on this, Autoregressive Integrated Moving Average (ARIMA(p,q,d)) model is fitted to the rainfall time series for various orders of Autoregression and differencing. Subsequently the performance of ARIMA has been compared to univariate artificial neural network model with 4 successive realizations as predictor and the 5th one as predictand. In order to test the relative contributions to these meteorological phenomenon, maximization of Shannon entropy is used. [ABSTRACT FROM AUTHOR]

The impact of sea surface temperature anomalies (SSTAs) over the North Pacific (32°–45° N, 140° E–150° W) and North Atlantic (north: 52°–68° N, 60°–20° W; south: 0°–30° N, 100°–40° W) on the summer surface air temperature (SAT) over the Mongolian Plateau (MP) is studied using NCEP/NCAR and Climatic Research Unit (CRU) reanalysis data. The results show that the circumglobal teleconnection (CGT) wave train related to the SSTAs in the North Atlantic propagates along with the westerly jet, resulting in a geopotential height anomaly at 200 hPa that favors an SAT anomalies (SATAs) on the MP. In addition, an anticyclonic (cyclonic) circulation anomaly with the characteristics of an atmospheric ultralong wave at 200 hPa over the Eurasia–Pacific region associated with the SSTAs in the North Pacific is also responsible for the positive (negative) SATAs on the MP. The results further reveal that the SSTAs in the North Pacific and the northern North Atlantic act synergistically on the SATAs on the MP. If there are strongly positive (negative) SSTAs in the North Pacific and the northern North Atlantic at the same time, the probability of a strongly positive (negative) SATAs on the MP is 67% (100%) for the research period of this paper. However, if the strongly positive (negative) SSTAs only appear in the North Pacific or the northern North Atlantic, the probability of a strongly positive (negative) SATAs on the MP is less than 34% (31%). The synergism of the SSTAs in the North Pacific and the northern North Atlantic enhances Rossby wave energy in Eurasia, with more Rossby wave energy spreading to the MP that in turn causes the circulation anomaly on the MP. Meanwhile, baroclinic conditions at 850 hPa north of the North Pacific, the northern and southern North Atlantic favor a northward shift of the westerly jet that guides the Rossby wave downstream so that the circulation anomaly that produces the SATAs on the MP is located in the mid–high latitudes. [ABSTRACT FROM AUTHOR]

The surface air temperature lapse rate (SATLR) plays a key role in the hydrological, glacial and ecological modeling, the regional downscaling, and the reconstruction of high-resolution surface air temperature. However, how to accurately estimate the SATLR in the regions with complex terrain and climatic condition has been a great challenge for researchers. The geographically weighted regression (GWR) model was applied in this paper to estimate the SATLR in China's mainland, and then the assessment and validation for the GWR model were made. The spatial pattern of regression residuals which was identified by Moran's Index indicated that the GWR model was broadly reasonable for the estimation of SATLR. The small mean absolute error (MAE) in all months indicated that the GWR model had a strong predictive ability for the surface air temperature. The comparison with previous studies for the seasonal mean SATLR further evidenced the accuracy of the estimation. Therefore, the GWR method has potential application for estimating the SATLR in a large region with complex terrain and climatic condition. [ABSTRACT FROM AUTHOR]

The thawing and freezing conditions on the Qinghai‐Tibet Plateau (QTP) are considered effective indicators that are widely used in ecology, climate change, cold‐region engineering design, and permafrost mapping. The purpose of this study is to utilize reanalysis to detect spatial variations in freeze–thaw conditions across the QTP from 1981–2017. From a comparison of five recent reanalysis models (MERRA2: National Aeronautics and Space Administration Modern‐Era Reanalysis for Research and Applications, Version 2; ERA: European Center for Medium‐Range Weather Forecasts Interim Reanalysis; GLDAS: Global Land Data Assimilation System NOAH; CFS: National Centers for Environmental Prediction Climate Forecast System Reanalysis, Version 2; and CMFD: China Meteorological Forcing Dataset) against existing sparse observations of 2‐m surface air temperature (SAT), we find that NASA MERRA2 dataset is the most applicable to the QTP. Then, the MERRA2 SAT dataset was selected for this study and was corrected and validated by utilizing calibration models built from the observed data. The results revealed that the correlation coefficients between the corrected MERRA2 SAT and meteorological station observed data increased from 0.52 to 0.93, 0.49 to 0.90, 0.56 to 0.93, and 0.69 to 0.93 in spring, summer, autumn, and winter, respectively. The corrected parameters performed better in the southern and southeastern portions of the QTP. Finally, we utilize the corrected MERRA2 dataset to develop freeze–thaw indices and evaluate statistical trends. We find that relatively high air freezing indices are one of the important factors for the presence of permafrost in the central and northeastern portions of the QTP. Trends in the air thawing and freezing indices in the permafrost and seasonally frozen ground regions indicate that, from 1981 to 2017 in the permafrost regions, the warming was more significant in the summer than in the other seasons. However, the winter warming rates in permafrost and seasonally frozen ground regions are almost the same. [ABSTRACT FROM AUTHOR]

BCC-ESM1 is the first version of the Beijing Climate Center's Earth System Model, and is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved. All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted. The DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations have also been run as the entry card of CMIP6. The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies. To facilitate the use of the BCC-ESM1 datasets, this study describes the experiment settings and summarizes the model outputs in detail. Preliminary evaluations of BCC-ESM1 are also presented, revealing that: the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5; the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured, despite some common precipitation biases as in CMIP5 and CMIP6 models; a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1, as in most other ESMs; and the mean states of surface sulfate concentrations can also be reasonably reproduced, as well as their temporal evolution at regional scales. These datasets have been archived on the Earth System Grid Federation (ESGF) node for atmospheric chemistry studies. [ABSTRACT FROM AUTHOR]

Downscaling is a state-of-the-art technique to generate fine-resolution climate change prediction and an obvious tool for forecasting future climate scenarios for many data-scarce areas like Bangladesh. The Educational Global Climate Model (EdGCM) predicts numerically and its performance was not evaluated for Bangladesh earlier. Due to this reason, an attempt has been made to apply a new geostatistical approach with the help of transform software to downscale EdGCM for identifying the trend of surface air temperature at the Sylhet district. Both Doubled_CO2 and Global_Warming_01 are simulated from EdGCM and maps are generated to depict global temperature variations. Downscaling is applied to the outputs from Doubled_CO2 scenario. Percent of bias (PBIAS), Nash-Sutcliffe efficiency (NSE) and the ratio of root mean square error to the standard deviation of measured data (RSR) values are satisfactory and acceptable. The trend analysis was performed using the Mann-Kendall Trend test and Sen's slope estimator. Temperature changes are significant for both downscaled and observed results of p-value which is less than alpha = 0.05. Mann-Kendall Z tests for annual downscaled and IPCC during (2006-2020) show a positive trend. Downscaled predicted annual average temperature (simulations by Doubled_CO2) for 2020 is 21.67°C for the Sylhet district. [ABSTRACT FROM AUTHOR]

Introduction: Daily maximum (Tmax) and minimum (Tmin) temperatures and Diurnal temperature range (DTR) profoundly affect the ecological environment and socioeconomic systems. In this study, we project future changes in Tmax, Tmin and DTR for RCP4.5 and RCP8.5 using fine-resolution downscaled data from the 18global coupled models over the main regions of the Belt and Road Initiative (BRI). Outcomes: The Multi-Model Ensemble (MME) mean projections show that future warming is stronger in Tmax than in Tmin, leading to the increased DTR over central and southern Europe, many areas surrounding the Black Sea and the Caspian Sea, and southeastern China. By contrast, the DTR is projected to decline over the regions north of 55°N and other some areas due to the more rapid increase in Tmin than in Tmax. As a whole, the diurnal asymmetry of projected future temperature changes is found to mainly occur from November to March. Conclusions: Our findings contribute to the knowledgebase on climate change over the main BRI regions. Regarding uneven spatiotemporal changes in Tmax, Tmin and DTR, appropriate climate change adaptation strategies, and options should be adopted to reduce or avoid disadvantaged consequences to the natural system and human society over specific regions.

A multimodel superensemble technique was applied to improve the accuracy of a model predicting the subseasonal surface air temperatures in East Asia. Superensemble forecast data based on seven subseasonal-to-seasonal (S2S) forecast models were produced for the initial times from May to August in 2016–2020 and forecast days in weeks 1–4. Superensemble techniques based on the root-mean-square error ensemble mean (SUPR) and the linear regression coefficient ensemble mean (SUPL) were evaluated and compared with the forecast bias-removed ensemble mean (ENS) data. The magnitudes of the forecasting root-mean-square errors (RMSE) of the SUPR and SUPL methods were 4.2% and 13.8% lower, respectively, than that of the ENS method, and the most significant effect was in 2020. The RMSE improvements of SUPR and SUPL were high when the initial date was in May, June, and August, and the forecast date was within weeks 1–4. Applying multimodel superensemble methods effectively reduced the forecasting errors of the S2S models. If weight coefficients based on the characteristics of the models are appropriately applied, the forecasting performance can be significantly improved.

The contributions of urban surface expansion to regional warming over subregions of Shanghai and Shanghai as a whole using different methods to calculate the daily mean surface temperature (SAT), including the averages of four daily time-records (0000, 0600, 1200, and 1800 UTC; T4), eight daily time-records (0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC; T8), and the averages of the SAT maximum (Tmax) and minimum (Tmin), Txn, were compared based on simulated results using nested numerical intergrations with the Weather Research and Forecasting regional climate model, where only the satellite-retrieved urban surface distributions differed between two numerical experiments. The contributions from urban-related warming expressed similar intensities when using T8 and Txn, while the smallest values occurred when using T4 over different subregions of Shanghai (with the exception of areas that were defined as urban for both time periods (U2U)) and Shanghai as a whole. Similar values for the changing trends could be detected over different subregions when no urban surface expansion (EX1) was detected for both T4 and Txn. The corresponding values increased under urban surface expansion (EX2) and varied over different subregions, revealing much stronger intensities over urban-surface expansion areas; the weakest intensities occurred over U2U areas. The increasing trends for EX2 and relative contributions when using T4 were smaller than those when using Txn, with the exception of those over U2U areas, which could be explained by the changing trends in Tmax and Tmin due to urban surface expansion, especially during intense urban expansion periods.

The interannual variability of the east asian upper-tropospheric westerly jet (EAJ) in summer is characterized by the meridional displacement of its axis, or a seesaw pattern of zonal wind anomalies between the northern and southern flanks of the EAJ. This study reveals a close relationship between the surface air temperature in the russian far east and the northern flank of the EAJ. Related to a warmer surface in the russian far east, the westerly decelerates in the northern flank of the EAJ. The relationship can be explained by a positive feedback mechanism between the surface air temperature in the russian far east and the overhead circulation: the anticyclonic circulation anomaly related to a weakened westerly in the northern flank of the EAJ induces surface warming in the russian far east and the warmer surface can in turn act as a heat source and induces a local anticyclonic circulation anomaly in the upper troposphere, therefore decelerating the westerly in the northern flank of the EAJ. The result implies that a better description of the summer surface condition in the russian far east may benefit seasonal forecasts of the EAJ and, subsequently, east asian summer climate.

Satellite-derived land surface data in 1980 and 2010 were used to represent land use and land cover (LULC) changes caused by the rapid economic development and human activities that have occurred over the past few decades in East Asia and China. The effects of LULC changes on the radiation budget and 2-m surface air temperature (SAT) were explored for the period using the Weather Research and Forecasting (WRF) model. The mosaic approach, which considers the N-most abundant land use types within a model grid cell (here, N = 3) and precisely describes the subgrid-scale LULC changes, was adopted in the integrations. The impacts of LULC changes based on two 36-year integrations showed that SAT generally decreased, with the sole exception being over eastern China, resulting in decreased SAT in China (−0.062 °C) and East Asian land areas (EAL, −0.061 °C). The LULC changes induced changes in albedo, which influenced the radiation budget. The radiative forcings at the top of the atmosphere were −0.56 W m−2 across the whole of China, and −0.50 W m−2 over EAL. Meanwhile, the altered roughness length mainly influenced near-surface wind speeds, large-scale and upward moisture fluxes, latent heat fluxes, and cloud fractions at different altitudes. Though the impacts caused by the LULC changes were generally smaller at regional scales, the values at local scales were much stronger.

To detect the impacts of urban surface expansion on surface air temperature at 2-m (SAT) in Shanghai, China, nested numerical integrations based on satellite-derived urban data between the 1980s and 2010s were performed using the Weather Research and Forecasting (WRF) model. Urban surface expansion induced an annual-averaged warming of 0.31 °C from 1980 to 2016 across the whole of Shanghai, showing the greatest intensity between 2010 and 2016. The values were 0.36, 0.78, and 0.75 °C over grids that were classified as urban in both time periods (U2U), land-use grids that changed from non-urban to urban (N2U), and urban areas (including U2U and N2U), respectively, and revealed weak warming over the inner-ring areas because the urban surfaces had been there since the 1980s, whereas warming areas were coincident with the outward expansion of the urban surface. Meanwhile, marked seasonal variations could be detected, which were greater in spring and summer but less in autumn and winter. The approximately homogenously distributed SAT maximum (weaker) and heterogeneously SAT minimum (stronger) contributed to the decreased diurnal temperature range. Regional warming induced by urban surface expansion was approximately 0.12 °C per decade, which accounted for 19% of the overall warming across the whole of Shanghai. The values were 0.11 °C per decade and 0.39 °C per decade over U2U and N2U, which accounted for approximately 17% and 42% of the overall warming, respectively, and resulted in approximately 41% of the overall warming over urban areas.

A coupled atmosphere–ocean data assimilation system, the Meteorological Research Institute-Coupled Data Assimilation System Version 1 (MRI-CDA1), was developed based on the coupled atmosphere–ocean general circulation model and separate atmosphere and ocean analysis routines operated by the JapanMeteorological Agency (JMA). To implement coupled atmosphere–ocean data assimilation, 6-hr data assimilation cycles with the coupled model in the outer loop are adopted in atmospheric data assimilation, whereas incremental analysis updates with 10-day data assimilation cycles are adopted in ocean data assimilation. A coupled data assimilation (reanalysis) experiment (CDA-Exp) is conducted using MRI-CDA1 along with an uncoupled reanalysis experiment (UCPL-Exp) in which the same atmospheric component of the coupled model is forced by prescribed sea surface temperature (SST) similarly to the JMA reanalysis, JRA-55. Climatological precipitation and variations in precipitation and sea surface air temperature (SAT) are better represented in CDA-Exp than in JRA-55, which is brought about by the modification of the atmosphere model physics of the coupledmodel to better represent climate states. In CDA-Exp, the SST adjustment to the atmosphere amplifies the lead/lag correlations between subseasonal variations of SST and precipitation. The atmosphere–ocean coupling generates SST variations associated with tropical instability waves, and the SAT field responds to SST variations. The SST–precipitation and SST–SAT relationships on the weather timescale are also recovered in CDA-Exp, although they are hardly seen in UCPL-Exp. The coupled model physics generates weather-timescale SST variations consistent with the atmospheric state, and the atmospheric parameters respond to the SST variations through the coupled model physics. This study suggests that the benefits of coupled data assimilation would be more evident if the ability of MRI-CDA1 to represent SST variations and its interaction with the atmosphere are improved. [ABSTRACT FROM AUTHOR]

The low‐latitude highlands (LLH) of China are situated in the subtropical southwest China. The spring climatic characteristics of the LLH were previously regarded as being governed by remote sea surface temperature forcing and large‐scale atmospheric circulations. Recent studies qualitatively demonstrated that the LLH is a relatively strong soil moisture (SM)‐surface air temperature (SAT) coupling region in China during spring. In this study, the quantitative spring SM‐SAT coupling (mean of March and April before wet season) is further statistically analysed using two different SM data sources (one reanalysis data set and one land surface assimilation product). The results show that significant negative SM‐SAT feedbacks mainly occur in the western and middle part of the LLH. Over these areas, 10–50% of the total SAT variance could be explained by the SM‐induced negative feedbacks with a feedback parameter of −0.2°C·(standardized SM)−1 to −0.8°C·(standardized SM)−1. Further analyses show that SM negatively impacts Bowen ratio in the western and middle part of the LLH where Bowen ratio positively affects SAT, therefore significant negative SM‐SAT feedbacks occur therein. The potential impact of India–Burma Trough (IBT) on the SM‐SAT coupling is also investigated in this study, the results of which suggest that SM‐SAT coupling and the effects of SM on land surface energy balance may be immune to the influence of the IBT. Furthermore, SM and IBT may comparably affect SAT in the western and middle part of the LLH. This highlights that SM might have prominent effects on the subseasonal predictability of spring SAT over these areas. [ABSTRACT FROM AUTHOR]

Introduction: Daily maximum (Tmax) and minimum (Tmin) temperatures and Diurnal temperature range (DTR) profoundly affect the ecological environment and socioeconomic systems. In this study, we project future changes in Tmax, Tmin and DTR for RCP4.5 and RCP8.5 using fine-resolution downscaled data from the 18global coupled models over the main regions of the Belt and Road Initiative (BRI). Outcomes: The Multi-Model Ensemble (MME) mean projections show that future warming is stronger in Tmax than in Tmin, leading to the increased DTR over central and southern Europe, many areas surrounding the Black Sea and the Caspian Sea, and southeastern China. By contrast, the DTR is projected to decline over the regions north of 55°N and other some areas due to the more rapid increase in Tmin than in Tmax. As a whole, the diurnal asymmetry of projected future temperature changes is found to mainly occur from November to March. Conclusions: Our findings contribute to the knowledgebase on climate change over the main BRI regions. Regarding uneven spatiotemporal changes in Tmax, Tmin and DTR, appropriate climate change adaptation strategies, and options should be adopted to reduce or avoid disadvantaged consequences to the natural system and human society over specific regions. [ABSTRACT FROM AUTHOR]

The summer season (June–July–August) surface air temperatures (SATs) over Saudi Arabia and its association with the atmospheric circulation cells were identified and analysed using the NCEP‐NCAR reanalyses and the NOAA extended reconstructed sea surface temperature (SST) datasets from 1948 to 2016. The pressure vertical velocity and divergent wind were utilized to determine the atmospheric circulation cells such as Hadley, Walker, and Ferrel cells. Possible mechanisms associated with changes in Saudi Arabia summer SAT were investigated, with particular focus on the uppermost and lowermost summers. Changes in SAT are mainly linked to the significant changes in the meridional and zonal atmospheric circulations. Partial correlations between SAT and atmospheric circulation showed that the effect of the Hadley and Walker circulations on the SAT was significant. There is no obvious connection between either the Hadley East Pacific circulation or the Ferrel circulation and SAT. Moreover, there is no dependency between summer SAT and the Atlantic Hadley and Atlantic Walker circulations, which could be detected in the summer season. The Hadley circulation may affect the Indian monsoon depression, whereas the Walker circulation has less effect. Also, a strong relationship between the Indian‐Pacific warm pool (IPWP) and both the Hadley and Walker circulations were found. Simultaneously, IPWP SST influenced Indian monsoon depression. Therefore, the Hadley circulation, Walker circulation, and IPWP all play key roles in the displacement and intensity of the Indian monsoon, which in turn strongly influences SAT variability over Saudi Arabia during summertime. [ABSTRACT FROM AUTHOR]

Two long continuous regional climate simulations over China have been carried out using the Regional Climate Model version 4 (RegCM4) and Weather Research and Forecasting model (WRF). The Simulations are forced by NCEP Reanalysis II data with a horizontal grid spacing of 30 km during 1981–2010. It is demonstrated that the RegCM4 and WRF had pronounced temperature and precipitation downscaling ability, producing more regional details and smaller biases than the driving R2. Overall, WRF tended to better capture the temperature and precipitation pattern and magnitude, daily temperature frequency, the monsoon rain belt movement, and seasonal precipitation variations over most wet regions, while larger deficits than RegCM4 were shown over some dry regions. The extreme precipitation indices of the two RCMs were quite different, with strong regional and seasonal dependence. WRF was better at simulating the annual mean temperature and precipitation trends, with higher spatial pattern correlations. The optimal ensemble approach combining the advantages of RegCM4 and WRF showed improved simulation compared to the individual models. The optimal ensemble reduced the annual temperature biases from the two models by 15–30%, and increased the precipitation spatial pattern correlations by 0.08–0.13. Further works are needed to improve the performances of the ensemble approach by using more RCMs. [ABSTRACT FROM AUTHOR]

While the observed decline of sea ice over the Chukchi‐Bering Sea (CBS) has coincided with the "warm‐Arctic, cold‐continent" (WACC) pattern over the North America (NA) sector, there is a debate on the causes of the WACC pattern. Here we present a very similar WACC pattern over the NA sector on both interannual and subseasonal time scales. Lead‐lag regression analyses on the shorter time scale indicate that an anomalous anticyclonic circulation over Alaska/Yukon in conjunction with the downward surface turbulent heat flux and long‐wave radiation anomalies over CBS leads the formation of the WACC pattern by about 1–2 days, while the latter further leads CBS sea ice reduction by about 3 days. These results indicate that atmospheric variability may play an active role in driving both the WACC pattern over NA and CBS sea ice variability. Key Points: A similar "warm‐Arctic, cold‐continent" (WACC) pattern is found on both interannual and subseasonal time scales over North America (NA)On shorter time scales, the Alaska anomalous high leads the WACC pattern by 1 to 2 days, which further leads CBS sea ice loss by about 3 daysThis study indicates that atmospheric variability plays an active role in driving the WACC pattern over NA and CBS sea ice variability [ABSTRACT FROM AUTHOR]

Subseasonal climate prediction has emerged as a top forecast priority but remains a great challenge. Subseasonal extreme prediction is even more difficult than predicting the time‐mean variability. Here we show that the wintertime cold extremes, measured by the frequency of extreme cold days (ECDs), are skillfully predicted by the European Centre for Medium‐Range Weather Forecasts (ECMWF) model 2–4 weeks in advance over a large fraction of the Northern Hemisphere land region. The physical basis for such skill in predicting ECDs is primarily rooted in predicting a small subset of leading empirical orthogonal function (EOF) modes of ECDs identified from observations, including two modes in Eurasia (North Atlantic Oscillation and Eurasia Meridional Dipole mode) and three modes in North America (North Pacific Oscillation, Pacific‐North America teleconnection mode, and the North America Zonal Dipole mode). It is of interest to note that these two modes in Eurasia are more predictable than the three leading modes in North America mainly due to their longer persistence. The source of predictability for the leading EOF modes mainly originates from atmospheric internal modes and the land‐atmosphere coupling. All these modes are strongly coupled to dynamically coherent planetary‐scale atmospheric circulations, which not only amplify but also prolong the surface air temperature anomaly, serving as a source of predictability at subseasonal timescales. The Eurasian Meridional Dipole mode is also tied to the lower‐boundary snow anomaly, and the snow‐atmosphere coupling helps sustain this mode and provides a source of predictability. Key Points: The wintertime land extreme cold days can be largely predicted by the ECMWF model 2–4 weeks in advanceThe physical basis in predicting ECDs is mainly rooted in predicting a small subset of leading EOF modes of ECDs identified from observationThe predictability source for the leading EOF modes originates from atmospheric internal modes and the land‐atmosphere coupling [ABSTRACT FROM AUTHOR]

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Large-ensemble climate experiments were performed to simulate future climates for a + 1.5 K rise in the global mean surface air temperatures relative to preindustrial levels as a subset of the database for Policy Decision making for future climate change (d4PDF), using the Non-Hydrostatic Regional Climate Model (NHRCM) with 20 km grid spacing. Along with present climate, + 2 K and + 4 K experimental outputs from the d4PDF already available, we investigated responses of surface air temperature (SAT) and precipitation on regional scales over Japan to global warming. The reproducibility of the present climate experiment is satisfactory to investigate future changes in the Japanese climate, and dynamical downscaling from the global to the regional climate states improves the frequency of heavy daily precipitation. In the future, SAT over Japan rises linearly with and faster than the global mean SAT. The meridional contrast of SAT rises becomes more pronounced as global warming progresses. Winter precipitation decreases/increases linearly in the western/eastern Japan, reflected by weakening of future winter monsoons. Annual maximum daily precipitation (R1d) shows a closely linear increase with the global SAT rise, but annual precipitation is mostly unchanged. The global mean SAT change from + 1.5 to + 2 K enhances R1d by 2.7% over the Japanese Islands, although the increase of R1d varies by regions. The increase in R1d is 5% in northern Japan, where the SAT increases are greater than those in other regions. [ABSTRACT FROM AUTHOR]

Although many studies have analyzed the effects of urbanization on temperature changes, the urbanization's effect on temperature change remains controversial. Northeast China is the largest old industrial base in China, which experienced a rapid urbanization in the past decades. Under the background of climate change, understanding the changes of surface air temperature and urbanization effects on temperature changes in the Northeast China is important to predict climate change in China. By analyzing the historical climate data, our results suggested that minimum temperature (Tmin) over the Northeast China increased significantly (0.40℃ decade-1) from 1960 to 1989, but showed no significant change (-0.02℃ decade-1) during 1990 - 2016. Due to slight change of maximum temperature (Tmax), the diurnal temperature range (DTR) showed a significant decreasing trend before 1989 (-0.34℃ decade- 1), but reached a stable level after 1990 (-0.06℃ decade-1). In Northeast China, urbanization had a significant warming effect on Tmin during the night-time, but had different effects on Tmax during the daytime under the different changes of solar radiation before and after 1990. There were moderate warming effects of urbanization on Tmax during the solar dimming period of 1960 - 1989, but weak cooling effects on Tmax during the solar stable period after 1990. Due to obvious warming effect of urbanization on Tmin, urbanization in Northeast China tends to result in an increase of mean temperature but a decrease of DTR. [ABSTRACT FROM AUTHOR]

This study was undertaken to understand the impact of climate change on the ecology of infection of Clinostomum complanatum, a model trematode parasite. We analysed climate change data and data from infected fish over a period of seven years (2007–2013) from the Aligarh region (India) in this retrospective study. We show that infection of the trematode parasite Clinostomum complanatum (Rudolphi, 1814) in the forage fish Trichogaster facsiatus (Bloch & Schneider, 1801) is dependent on surface air temperature amongst the (ecologically) relevant climate change variables for both the parasite and its host. This study is the first to implicate surface air temperature as an environmental variable that may contribute towards parasitism, particularly for parasites with a piscine host. The biological relevance of changing climate on the ecology of this parasite is discussed. [ABSTRACT FROM AUTHOR]

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By separating variations on different time scales, the present study reveals important differences in the North Atlantic Oscillation (NAO)‐related sea ice concentration (SIC), surface air temperature (SAT), and sea surface temperature (SST) patterns for trend, interdecadal, and interannual variations. The SIC has a prominent decreasing trend in the Greenland Sea and the Barents Sea, collocating with an increasing SAT trend and a weak increasing SST trend in the high‐latitude North Atlantic. The wind trends display a weak NAO signal. Corresponding to the positive interdecadal NAO phase, the SIC shows a decreasing trend in the Greenland and Barents Seas. The SAT change features a west negative‐east positive pattern along with positive anomalies extending to the Greenland Sea. The SST change is very weak in the Greenland Sea. Corresponding to the positive interannual NAO phase, the SIC change is opposite between the Greenland/Barents Seas and the Labrador Sea. The SAT change is characterized by a broad west–east pattern over the mid‐high latitudes. The SST change features an east–west dipole pattern in the mid‐latitude North Atlantic Ocean. In both interdecadal and interannual variations, NAO‐related meridional wind anomalies induce anomalous advection that contributes to the SAT change together with upward long‐wave radiation. The SIC and SAT changes are coupled closely through surface heat fluxes in all the three time scales. The present results suggest that it is necessary to distinguish time scales in studying the relationship among SIC, SAT, and SST variations. [ABSTRACT FROM AUTHOR]

This study examines projected changes in surface air temperature (SAT) and precipitation over mid–high latitudes of Eurasia during boreal spring in a 1.5/2°C warmer world (relative to pre‐industrial level) using historical, RCP4.5 and RCP8.5 simulations from 16 climate models of the Coupled Model Intercomparison Project Phase 5. Results show that spatial patterns of spring SAT and precipitation changes over Eurasia in the 1.5/2°C warmer world relative to the current climate (1979–2005) are similar between the two RCPs, but with a slightly larger amplitude in the RCP8.5. Spring SAT warmings over Eurasia are faster than the global annual mean. Amplitudes of the spring Eurasian SAT warmings are larger over higher latitudes. An additional 0.5°C global warming leads to extra spring Eurasian SAT warmings, with the magnitude larger than 0.5°C. Changes in the spring maximum temperature are comparable to the mean temperature changes both in the 1.5 and 2°C warming targets. Significant increases in the mean and extreme precipitation are only seen over parts of Eurasia. An additional 0.5°C global warming does not lead to obvious changes in the mean and extreme spring precipitation. Further analyses show that there are large uncertainties in the projected changes in the inter‐annual variability of the Eurasian spring SAT and precipitation. [ABSTRACT FROM AUTHOR]

To simulate the current climate, a 20-year integration of a non-hydrostatic regional climate model (NHRCM) with grid spacing of 5 and 2 km (NHRCM05 and NHRCM02, respectively) was nested within the AGCM. The three models did a similarly good job of simulating surface air temperature, and the spatial horizontal resolution did not affect these statistics. NHRCM02 did a good job of reproducing seasonal variations in surface air temperature. NHRCM05 overestimated annual mean precipitation in the western part of Panama and eastern part of the Pacific Ocean. NHRCM05 is responsible for this overestimation because it is not seen in MRI-AGCM. NHRCM02 simulated annual mean precipitation better than NHRCM05, probably due to a convection-permitting model without a convection scheme, such as the Kain and Fritsch scheme. Therefore, the finer horizontal resolution of NHRCM02 did a better job of replicating the current climatological mean geographical distributions and seasonal changes of surface air temperature and precipitation.

Quality control can effectively improve the quality of surface meteorological observations. To ensure the stability and effectiveness of a quality control model under different terrain and climate conditions, it is necessary to structure a quality control model with strong generalization ability. Algorithms such as the Random Forest provide such generalization ability. However, machine learning algorithms are slower than traditional mathematical models. Therefore, a Random Forest quality control algorithm based on the Principal Component Analysis (PCA-RF) is proposed in this paper. Fifteen target stations under different climatic and geomorphological conditions were selected and tested using observations collected four times daily at neighboring stations from 2005-2014. The results show that using PCA to analyze the elemental composition and select elements with high correlation factors, as well as applying the Random Forest algorithm, can effectively reduce the run time and keep the accuracy of the model. The training sample dependence, model prediction accuracy and error detection rate of the PCA-RF model are superior to those of the Spatial Regression method. Therefore, the PCA-RF method is a better-quality control model for the spatial quality control of multiple elements of surface air temperature observations.

Increasing atmospheric carbon dioxide (CO2) is the main factor of global warming. Carbon satellites have proven that CO2 concentrations have nonuniform spatio‐temporal distributions. The relationship between unevenly distributed CO2 and global surface air temperature (SAT) is seldom known. The success of complex networks provides an opportunity to address this issue. This paper proposes a multilayer climate network approach to identify the impacts of nonuniform CO2 on SAT. The results show that the probability density function (PDF) of degrees, weighted degrees, and link lengths follows power‐law distributions. A large fraction of strong correlation links resides in proximal distance (smaller than 2,000 km), indicating that CO2 nodes are strongly connected to the surrounding SAT nodes. The enhanced distributions of large positive weights, time delays, and degree patterns are all consistent with the properties of Rossby waves. This framework can be useful for predicting future climate changes and policy‐making for carbon reduction. Plain Language Summary: Carbon dioxide (CO2) is a prominent anthropogenic greenhouse gas in the atmosphere, and the increasing CO2 is the main reason for global climate warming. The CO2 concentration from satellites has revealed that the spatial distribution of CO2 is uneven. In previous studies, the relationship between CO2 and temperature is based on global CO2 concentration mean assumptions. Here, we propose a multilayer climate network approach to identify their relations using satellite data. We find that the PDF of degrees, weighted degrees, and link lengths follows power‐law distributions. The most dominant links, with a geographical distance larger than 2,000 km, are found for distances of 7,000 km, coinciding with the wavelengths of atmospheric Rossby waves. The time delays associated with these distances are in agreement with the group velocity of the atmospheric Rossby waves. Moreover, the pronounced band over 50°S, the dominance in the Southern Hemisphere relative to the Northern Hemisphere, and the similar patterns to transient heat flux are consistent with the properties of Rossby waves. The proposed approach leads to a better understanding of the role played by CO2 in forcing global temperatures and future climate change. Key Points: There are short‐term correlations between global mid‐troposphere CO2 concentrations and global surface air temperatureThe degrees, weighted degrees, and link lengths of CO2 and SAT multilayer climate network are conformed to power‐law distributionsSignificant correlation links of the constructed climate network yield a clear association with Rossby waves [ABSTRACT FROM AUTHOR]

This study explores the near‐surface air temperature (TAS) prediction skills at different time scales via coupled climate models that were involved in the Coupled Model Intercomparison Project phase 5 (CMIP5). The simulation skills of the global mean TAS are first assessed between the observations and models; then, the temporal variability is separated into three parts (the linear trend, decadal variability and inter‐annual variability), and each part is compared with the observations. It is found that the global mean TAS anomaly and the decadal variability are well captured by the model, while the inter‐annual variability is poorly presented. In all the three parts of different time scales, there are larger differences among the 18 models, and the ensemble mean of CMIP5 is the closest to the observations. Besides, the TAS decadal variability is better presented for global ocean than for global land. In the assessment of climate oscillation, it is found that the models can well reproduce the TAS decadal variation patterns correlated with the Pacific Decadal Oscillation (PDO) and the inter‐annual variation patterns correlated with El Niño–Southern Oscillation (ENSO) but poor for the decadal variation patterns related to the Atlantic Multidecadal Oscillation (AMO). This study provides a reference assessing the simulation skills of climate models and an indicator evaluating the advantage of CMIP6 in comparison with CMIP5. [ABSTRACT FROM AUTHOR]

Surface air temperatures (SATs) derived from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim and CERA‐20C reanalysis data sets are compared with data from 43 observation stations in Sichuan for 1979–2010. The results show (a) the temperatures from the ERA‐Interim and CERA‐20C data sets are strongly correlated with those from the observation stations, although significant cold biases are seen on both annual and seasonal timescales. (b) The biases in SATs are predominately influenced by the differences between the actual topography and the topography used in the reanalysis models. Larger differences in temperature are observed in the plateau and mountainous regions of Sichuan. We confirmed larger SAT biases at high altitudes by categorizing the elevation into four bands, each with a spacing of 1,000 m. (c) We reduced the biases resulting from elevation by using an elevation correction method with internal lapse rates derived from different reanalysis pressure levels. The annual mean bias was reduced from −2.86 to −0.75°C for the ERA‐Interim data set and from −5.27 to −2.21°C for the CERA‐20C data set. After calibration, the correlation coefficients between the difference in SAT (observed minus reanalysis data) and the difference in elevation (station elevation minus model elevation) decreased from −0.97 and −0.91 to −0.29 and −0.30 for the ERA‐Interim and CERA‐20C data sets, respectively. These significant differences should not be ignored in the application of reanalysis data sets to climate research. The evaluation and calibration of reanalysis data sets are essential before making assessments of regional climate change, especially over regions with complex topography. [ABSTRACT FROM AUTHOR]

To solve the problem that the meteorological factors used in the interpretation of numerical forecast products are fixed and unsuitable for special turning of weather phenomenon, a recurrent wavelet neural network(RWNN) method based on dynamic factors was adopted to interpret the summer maximum temperature in Jiangsu cities and towns. This method could select meteorological factors automatically and with out establishing statistical equations, so could be widely used. Based on the data of five cities and towns of Jiangsu Province including Nanjing, Xuzhou, Sheyang, Changzhou, Suzhou during June-August of 2017-2018 from T639 numerical forecast products, the maximum temperature forecast and warning models were established with the method. Experimental results show that models’ TT2 scores have increased by 9% and HSS35 scores have increased 0.15 compared to back propagation neural network, and models’ TT2 scores have increased by 17% and HSS35 scores have increased 0.2 compared to Kalman filter on average in Nanjing, Xuzhou and Sheyang. [ABSTRACT FROM AUTHOR]

The ability of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to reproduce twentieth-century climate trends over the seven CONUS regions of the National Climate Assessment is evaluated. This evaluation is carried out for summer and winter for three time periods, 1895–1939, 1940–1979, and 1980–2005. The evaluation includes all 206 CMIP5 historical simulations from 48 unique models and their multi-model ensemble (MME), as well as a gridded in situ dataset of surface air temperature and precipitation. Analysis is performed on both individual members and the MME, and considers reproducing the correct sign of the trends by the members as well as reproducing the trend values. While the MME exhibits some trend bias in most cases, it reproduces historical temperature trends with reasonable fidelity for summer for all time periods and all regions, including at the CONUS scale, except the Northern Great Plains from 1895 to 1939 and Southeast during 1980–2005. Likewise, for DJF, the MME reproduces historical temperature trends across all time periods over all regions, including at the CONUS scale, except the Southeast from 1895 to 1939 and the Midwest during 1940–1979. Model skill was highest across all of the seven regions during JJA and DJF for the 1980–2005 period. The quantitatively best result is seen during DJF in the Southwest region with at least 74% of the ensemble members correctly reproducing the observed trend across all of the time periods. No clear trends in MME precipitation were identified at these scales due to high model precipitation variability. [ABSTRACT FROM AUTHOR]

The Mediterranean region is located over the entrance of the Asian upper level jet. The anomalous atmospheric circulation over the region can excite wave train pattern propagating eastward and impacting the climate over the Eurasian continent. In this study, the summer (June–September) sea level pressure (SLP) prediction skill over the Mediterranean region is investigated, based on the May-start hindcasts of five state-of-the-art coupled models from the ENSEMBLES over the period of 1960–2005. The result shows that there are three models that exhibit an increased prediction skill for the Mediterranean SLP after the late 1970s. Tropical sea surface temperature (SST) is generally recognized as the major source for dynamical seasonal climate prediction. The analysis of the three models shows that they can capture well the observed impact of tropical Atlantic and Pacific SSTs on the Mediterranean atmospheric circulation after the late 1970s. Consequently, the three models have an increased prediction skill for the Mediterranean SLP over the time. In contrast, the other two models cannot reproduce the observed relationship of the Mediterranean SLP with the tropical Atlantic and Pacific SSTs, thereby exhibiting low prediction skill for the Mediterranean SLP. Further analysis indicates that the increased prediction of the Mediterranean SLP can improve dynamical prediction of surface air temperature (SAT) over the eastern Mediterranean and central East Asia, which is valuable for current regional dynamical seasonal climate prediction. [ABSTRACT FROM AUTHOR]

This study discusses the interannual variability of the summer (June–August) season surface air temperature (SAT) over the Arabian Peninsula and its relationship to the global circulation patterns under the influence of El Nino Southern Oscillation (ENSO). A composite analysis based on the above (below) normal summer SAT over the Arabian Peninsula reveals a wavelike pattern resembling the mid-latitude Circumglobal Wave Train (CGT). The positive (negative) phase of the CGT is associated with above (below) normal SAT anomalies over the Arabian Peninsula. Further, the Arabian Peninsula summer SAT is also found to be associated with ENSO and this association has strengthened in recent decades as compared to before 1980s. The ENSO induced upper level circulation patterns resemble the CGT pattern. The ENSO warm phase over the central and eastern Pacific is associated with anomalous low pressure anomalies over the Eurasian region while the cold phase associated with anomalous high pressure. This favours the above normal SAT anomalies over the Eurasian region including the northern parts of the Peninsula during the cold phase of ENSO and below normal temperature during the warm phase. The weakening of upper level low-pressure anomalies over central Asia during the positive phase of CGT also favours the extension of the Eurasian high over the Arabian Peninsula. Additionally, the climate model simulations experiments performed with newly developed Saudi-KAU Atmospheric Global Climate Model (AGCM) also support the observed findings. [ABSTRACT FROM AUTHOR]

Historical temperature records are often partially biased by the urban heat island (UHI) effect. However, the exact magnitude of these biases is an ongoing, controversial scientific question, especially in regions like China where urbanization has greatly increased in recent decades. Previous studies have mainly used statistical information and selected static population targets, or urban areas in a particular year, to classify urban-rural stations and estimate the influence of urbanization on observed warming trends. However, there is a lack of consideration for the dynamic processes of urbanization. The Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are three major urban agglomerations in China which were selected to investigate the spatiotemporal heterogeneity of urban expansion effects on observed warming trends in this study. Based on remote sensing (RS) data, urban area expansion processes were taken into consideration and the relationship between urban expansion rates and warming trends was investigated using data from 975 meteorological stations throughout China. Although urban areas constitute less than 1% of land in China, more than 90% of the meteorological stations experienced urban land use change and the average urban expansion rate was 0.33%/a. There was also a significant positive relationship between observed warming trends and urban expansion rates. Background warming, without the influence of urbanization and extra warming induced by urbanization processes, was estimated using a linear regression model based on observed warming trends and urban expansion rates. On average, urbanization led to an additional annual warming of 0.034 ± 0.005 °C/10a. This urbanization warming effect was 0.050 ± 0.007 °C/10a for minimum temperatures and 0.008 ± 0.004 °C/10a for maximum temperatures. Moreover, it appeared that urbanization induced greater warming on the minimum temperature during the cold season and maximum temperature during the warm season. [ABSTRACT FROM AUTHOR]

Interannual variability of surface air temperature over South America is investigated and, based on previous studies, thought to be partly the consequence of soil–atmosphere interaction. Annual and monthly averages of surface air temperature, evapotranspiration, heat fluxes, surface radiation and cloud cover, simulated by two regional climate models, RCA4 and LMDZ, were analyzed. To fully reveal the role of soil as a driver of temperature variability, simulations were performed with and without soil moisture-atmosphere coupling (Control and Uncoupled). Zones of large variance in air temperature and strong soil moisture-atmosphere coupling are found in parts of La Plata Basin and in eastern Brazil. The two models show different behaviors in terms of coupling magnitude and its geographical distribution, being the coupling strength higher in RCA4 and weaker in LMDZ. RCA4 also shows greater amplitude of the annual cycle of the monthly surface air temperature compared to LMDZ. In both regions and for both models, the Uncoupled experiment tends to be colder and exhibits smaller amplitude of the interannual variability and larger evaporative fraction than the Control does. It is evidenced that variability of the land surface affects, and is affected by, variability of the surface energy balance and that interannual temperature variability is partly driven by land–atmosphere interaction. [ABSTRACT FROM AUTHOR]

This study analyzes the dominant modes of interannual variability of surface air temperature (SAT) during boreal autumn over the mid-high latitudes of Eurasia and investigates their associations with snow cover, atmospheric circulation and sea surface temperature (SST). The first, second and third empirical orthogonal function (EOF) mode of autumn SAT anomalies displays same-sign distribution, an east–west dipole pattern and a south–north dipole pattern, respectively. Analysis of surface heat fluxes indicates that snow changes explain only small patches of SAT anomalies related to the first two EOFs via altering surface shortwave radiation. Atmospheric circulation anomalies have important contributions to the formation of SAT anomalies. Southerly (northerly) wind anomalies generally favor positive (negative) SAT anomalies via bringing warmer (colder) air from lower (higher) latitudes. The atmospheric circulation anomalies related to the first EOF mode are attributed to a combination of the Arctic Oscillation (AO) and the Scandinavia pattern and those related to the second EOF mode have a close relation with the East Atlantic/West Russian (EAWR) and circumglobal teleconnection (CGT) patterns. Formation of the atmospheric circulation anomalies related to the third EOF mode is partly related to the Arctic sea ice change around the Barents–Kara Seas. SST anomalies have little contribution to the atmospheric circulation anomalies associated with the first three EOF modes of Eurasian autumn SAT interannual variations. Hindcast skill of the SAT anomalies related to the first (second) EOF mode is improved when taking both the AO and Scandinavia (EAWR and CGT) patterns into account. [ABSTRACT FROM AUTHOR]