Search

Interdecadal variations of the relationship between El Niño–Southern Oscillation (ENSO) and the Indo-China Peninsula (ICP) surface air temperature (SAT) in winter are investigated in the study. Generally, there exists a positive correlation between them during 1958–2015 because the ENSO-induced anomalous western North Pacific anticyclone (WNPAC) is conducive to pronounced temperature advection anomalies over the ICP. However, such correlation is unstable in time, having experienced a high-to-low transition around the mid-1970s and a recovery since the early 1990s. This oscillating relationship is owing to the anomalous WNPAC intensity in different decades. During the epoch of high correlation, the anomalous WNPAC and associated southwesterly winds over the ICP are stronger, which brings amounts of warm temperature advection and markedly heats the ICP. In contrast, a weaker WNPAC anomaly and insignificant ICP SAT anomalies are the circumstances for the epoch of low correlation. It is also found that substantial southwesterly wind anomalies over the ICP related to the anomalous WNPAC occur only when large sea surface temperature (SST) anomalies over the northwest Indian Ocean (NWIO) coincide with ENSO (viz., when the ENSO–NWIO SST connection is strong). The NWIO SST anomalies are capable of driving favorable atmospheric circulation that effectively alters ICP SAT and efficiently modulates the ENSO–ICP SAT correlation, which is further supported by numerical simulations utilizing the Community Atmospheric Model, version 4 (CAM4). This paper emphasizes the non-stationarity of the ENSO–ICP SAT relationship and also uncovers the underlying modulation factors, which has important implications for the seasonal prediction of the ICP temperature.

The reanalysis datasets (CRA40) have now been developed in China Meteorological Administration. We aim to compare the differences in surface air temperature (SAT) between observational that has been adjusted for urbanization bias and reanalysis data (NCEPV1, NCEPV2, ERA5, CFSR, MERRA, JRA55, 20CRV3 and CRA40) over mainland China during 1961–2015. The main results are presented as follows. The correlation and standard deviation between the reanalysis data and observations exhibit highly consistent interannual variability and dispersion, with the interannual SAT variability in JRA55 being closest to the observations for 1961–2015 and that of ERA5 for 1979–2015; the dispersions of 20CRV3 is most consistent with the observations for 1961–2015 and that of NCEPV1 for 1979–2015. Although annual mean SAT of the reanalysis is generally 0–2.0 °C lower than the observations, the bias in the SAT climatology of 20CRV3 is the least for 1961–2015 in all reanalysis datasets and that of CRA40 is the least for 1979–2015. The trends of NCEPV1 is closer to the observations than other reanalysis for 1961–2015 and that of 20CRV3 for 1979–2015. The biases in terms of interannual variability, dispersion, climatology, and linear trend are increase with altitude. Overall, in terms of the similarity of multiple measures to the urbanization bias-adjusted observations, JRA55 and CRA40 show the best performances for the periods 1961–2015 and 1979–2015 respectively in reproducing various aspects of climatological and climate change features in mainland China.

Coastal lagoons are particularly vulnerable to climate change, in particular, Sea Level Rise (SLR) due to their shallowness. Lake Burullus provides a variety of socio-economic services as the second largest coastal lagoon in Egypt. Recently, it has experienced significant ecological deterioration. Thus, its ecosystem is fragile in the face of anthropogenic induced changes. The main objective of the current study is to investigate the climate change impacts on characteristics of Lake Burullus. A depth averaged hydro-ecological modeling system, MIKE21, was applied to develop an eco - hydrodynamic model for the lake. The developed model was calibrated and verified for two successive years: July 2011–June 2012 and July 2012–June 2013. The model simulations exhibited good agreement with the measurements during the calibration and verification processes. Six different Regional Climate Models (RCMs) were compared, using six different statistical metrics, to determine the most accurate one for the study area. The required meteorological input, including surface air temperature, precipitation, and evaporation were derived from the selected RCM. The meteorological input was extracted for two different years in the 21 st century considering one Representative Concentration Pathways (RCPs) scenario, based on the Intergovernmental Panel on Climate Change (IPCC) 5th Report. Regional SLR projections for the Mediterranean Sea for the selected RCP scenario and the two studied years were obtained. These future climate change estimates were used to modify the validated model of the lake. A sensitivity analysis was applied to assess effect of future climatic conditions and SLR, separately. The results revealed that the lake water depths will increase and it will be warmer and more saline. Significant spatial variability of the studied parameters under climate change forcing is expected. Consequently, climate change is going to restrict the lake's ability to preserve the present-day species. An urgent management plan involving adaptation works, should be implemented to reduce such potential species losses in Egyptian lagoons.

The frequency and intensity of extreme events especially heat waves (HW) are growing all around the world which ultimately poses a serious threat to the health of individuals. To quantify the effects of extreme temperature, appropriate information, and the importance of HW and heat index (HI) are carefully discussed for different parts of the world. Varied definitions of the HW and HI formula proposed and used by different countries are carried out systematically continent-wise. Different studies highlighted the number of definitions of HW; however, mostly used Steadman’s formulae, which was developed in the late 1970s, for the calculation of HI that uses surface air temperature and relative humidity as climatic fields. Since then, dramatic changes in climatic conditions have been observed as evident from the ERA5 datasets which need to be addressed; likewise, the definition of HW, which is modified by the researchers as per the geographic conditions. It is evident from the ERA5 data that the temperature has increased by 1–2 °C as compared to the 1980s. There is a threefold increase in the number of heatwave days over most of the continents in the last 40 years. This study will help the researcher community to understand the importance of HW and HI. Furthermore, it opens the scope to develop an equation based on the present scenario keeping in mind the basics of an index as considered by Steadman.

Features of the dominant modes of surface air temperature (SAT) on the intraseasonal timescale over the mid–high-latitude Eurasia (MHE) during boreal summer (June–September) are investigated based on the ERA5 reanalysis data from 1979 to 2016. The intraseasonal variability (ISV) of SAT over MHE is primarily characterized by an eastward propagation along 60°N, which is found to impact the regional weather in China, including summertime extreme hot and cool events. The forecast skill and potential predictability of the ISV of SAT over MHE are assessed for 5 dynamical models that have participated in the subseasonal-to-seasonal (S2S) prediction project, by analyzing 12 years’ (1999–2010) model reforecast/hindcast data. By using the principal component (PC) index of the leading intraseasonal SAT modes as a predictand, we found that the forecast skill for ISV of SAT can reach out to 11–17 days, and the ECMWF model exhibits the best score. All the S2S models tend to show 1) a relatively higher skill for strong intraseasonal oscillation (ISO) cases, 2) a systematic underestimate of the amplitude of the SAT ISV signal, and 3) different skills during different phases of ISO cases. Analysis of potential predictability based on the perfect-model assumption reveals a 4–6-day skill gap for most models, and the skill gap also varies among different phases of ISO events. The results imply the need for continued development of operational forecasting systems to improve the actual prediction skills for the ISV of SAT over MHE.

Soil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

The extremely low value of the Northern Hemisphere annular mode (NAM) that occurred in the winter of 2009/2010 led to rare extreme low temperatures and heavy snow disasters over Northeast China. Studies on the effect of NAM on temperature often rely on correlation analysis, synthetic analysis, regression analysis, and other statistical methods. However, although the statistical method is instructive to the discussion of physical mechanisms, it cannot prove whether there is causality between variables. Here, we apply extended convergent cross mapping (ECCM) to detect the time-delayed causal interaction between NAM and winter surface air temperature (SAT) over Northeast China. Results indicate that the winter SAT information is encoded in the contemporaneous 1000 hPa NAM and vice versa, showing the contemporaneous bidirectional causality between them. In addition, we detected a unidirectional causality between 500 hPa NAM and winter SAT. NAM at 500 hPa can influence the SAT but not vice versa. Moreover, we examine the time-delayed causality between winter SAT and the stratospheric NAM at 50 hPa. Stratospheric NAM can influence the SAT approximately 10 days later. These causal relationships and feedback effects in dynamic systems have opened up the possibility of improving the prediction of winter SAT over Northeast China by using the NAM as an external factor.

In this paper, we investigate the influence of the winter NAO on the multidecadal variability of winter East Asian surface air temperature (EASAT) and EASAT decadal prediction. The observational analysis shows that the winter EASAT and East Asian minimum SAT (EAmSAT) display strong in-phase fluctuations and a significant 60–80-year multidecadal variability, apart from a long-term warming trend. The winter EASAT experienced a decreasing trend in the last two decades, which is consistent with the occurrence of extremely cold events in East Asia winters in recent years. The winter NAO leads the detrended winter EASAT by 12–18 years with the greatest significant positive correlation at the lead time of 15 years. Further analysis shows that ENSO may affect winter EASAT interannual variability, but does not affect the robust lead relationship between the winter NAO and EASAT. We present the coupled oceanic-atmospheric bridge (COAB) mechanism of the NAO influences on winter EASAT multidecadal variability through its accumulated delayed effect of ∼15 years on the Atlantic Multidecadal Oscillation (AMO) and Africa–Asia multidecadal teleconnection (AAMT) pattern. An NAO-based linear model for predicting winter decadal EASAT is constructed on the principle of the COAB mechanism, with good hindcast performance. The winter EASAT for 2020–34 is predicted to keep on fluctuating downward until ∼2025, implying a high probability of occurrence of extremely cold events in coming winters in East Asia, followed by a sudden turn towards sharp warming. The predicted 2020/21 winter EASAT is almost the same as the 2019/20 winter.

Electronic archives of data from standard meteorological observations (mean daily/monthly surface air temperatures - SAT) at the meteorological stations at Bukhta Tikhaya (Hooker Island, 1929-1960) and Krenkel Observatory (Hayes Island, 1957-2017) on Franz Josef Land (FJL) are presented. Parallel data series of SAT made in 1958 and 1959 on both meteorological stations were analyzed. Linear regression equations used for extrapolation of observational data representative for Krenkel Observatory for the period 1929-1957 are also presented. The assessment of long-term changes in SAT on FJL was carried out based on the analysis of the obtained series (1929-2017). The main conclusions that follow from our study are: (1) The total warming in the FJL archipelago was 1.6-1.8°C (0.2°C/decade) for the entire available period of instrumental observations (1929-2017); (2) The highest rates of warming were recorded in March-April and amounted to 0.6°C/decade; (3) A particular strong warming has been observed since the 1990s. The annual temperature increased by 6.3°C (2.2°C/decade) for the period 1990-2017 and 5.2°C (2.9°C/decade) for the period 2000-2017; (4) For the period 1990-2017 the maximum rate of warming occurred between October to February with 4.4°C/decade; (5) For the period 2000-2017 the maximum rate of warming occurred between January to April and from November to December with 5.6°C/decade; (6) The dominant seasons of the year are winter (November-April), spring (May), summer (June-September) and autumn (October); (7) Over the entire observation period the largest temperature increase was observed in the winter season. During the period of modern warming (1990-2017), the largest temperature increase was observed in winter and autumn.

The effect of Eurasian spring snowmelt on surface air temperature (SAT) in late spring (April–May) and early summer (June–July) and the relevant physical mechanisms during 1981–2016 are investigated. Results show that the first mode of the inter-annual Eurasian spring snowmelt represents an east–west dipole anomaly pattern, with an intense center over Siberia and another moderate center around eastern Europe. The European spring snowmelt shows an insignificant relation to the local SAT, whereas the Siberian spring snowmelt has a significant impact on the SAT in late spring and early summer. More Siberian spring snowmelt contributes to higher SAT in late spring and lower SAT in early summer via different mechanisms. In late spring, increased Siberian spring snowmelt corresponds to less local surface albedo and cloud cover, leading to the surface absorbing more shortwave radiation and thereby higher SAT. The sub-surface and deep soil moisture anomalies generated from Siberian spring snowmelt can persist into early summer. Excessive Siberian spring snowmelt corresponds to positive soil moisture anomalies, contributing to decreased sensible heat and increased cloud cover in early summer. Increased cloud cover leads to the surface receiving less shortwave radiation. Thus, lower SAT appears over Siberia in early summer due to reduced sensible heat and shortwave radiation. However, the simulation of Eurasian spring snowmelt variability and its influences on SAT via the snow hydrological effect is still a challenge for the climate models that participated in the Coupled Model Intercomparison Project phase 6.

We analyzed the spatiotemporal variations in surface air temperature and key climate change indicators over the Tibetan Plateau during a common valid period from 1979 to 2018 to evaluate the performance of different datasets on various timescales. We used observations from 22 in-situ observation sites, the CRA-40/Land (CRA) reanalysis dataset, the China Meteorological Forcing Dataset (CMFD), and the ERA-Interim (ERA) reanalysis dataset. The three datasets are spatially consistent with the in-situ observations, but slightly underestimate the annual mean surface air temperature. The daily mean surface air temperature estimated by the CRA, CMFD, and ERA datasets is closer to the in-situ observations after correction for elevation. The CMFD shows the best performance in simulating the annual mean surface air temperature over the Tibetan Plateau, followed by the CRA and ERA datasets with comparable performances. The CMFD is relatively accurate in simulating the daily mean surface air temperature over the Tibetan Plateau on an annual scale, whereas both the CRA and ERA datasets perform better in summer than in winter. The increasing trends in the annual mean surface air temperature over the Tibetan Plateau from 1979 to 2018 reflected by the CRA dataset and the CMFD are 0.5°C (10 yr)−1, similar to the in-situ observations, whereas the warming rate in the ERA dataset is only 0.3°C (10 yr)−1. The trends in the length of the growing season derived from the in-situ observations, the CRA, CMFD, and ERA datasets are 5.3, 4.8, 6.1, and 3.2 day (10 yr)−1, respectively. Our analyses suggest that both the CRA dataset and the CMFD perform better than the ERA dataset in modeling the changes in surface air temperature over the Tibetan Plateau.

Seasonal variability is the complex non-linear response of the physical climate system. There are two types of natural variability: those external and internal to the climate system. In any given season, natural variability may cause the climate to be different than its long-term average. This study examines with the seasonal variation of the maximum temperatures during the summer season. In addition, the maximum temperatures in May become close to the characteristics of the summer season. The monthly data for maximum temperature of May, June and July were used from Iraqi Meteorological Organization and Seismology (IMOS) for 47 years from 1970 to 2017 for Baghdad city. This period was long enough to estimate the range of approaching maximum temperature (Tmax) May to summer. Results revealed a significant Tmax for Baghdad during the second period (1992–2017) and ?shown similar behavior of Tmax in May to June and July; on the contrary that first period (1970–1991). In second period, two phases have been found out, positive phase and negative phase. The positive phase were happened in 1995, 1999, and 2006, and the negative phase was four cases (1992, 2004, 2013, and 2016), while a few cases recorded in first period. The amplitudes of monthly variability had same distance of leaner correlation especially in 1999 and 2013 that represent coherent wave with summer seasons. The variance difference for Tmax between May and June approximately was 2°C for second study’s period, while exceed this range in first period. This variance change to 7.5°C when found difference between July and May.

The leading modes of the North Pacific Jet (NPJ) variability include intensity changes and meridional shifts in jet position on the low frequency timescales. These leading modes of NPJ variability are closely associated with weather and climate conditions spanning from Asia to the United States (US). In this study, we investigated changes in the NPJ’s role as a conduit for US surface air temperature (SAT) anomalies during the boreal winter across the late 1990s. We found that the leading mode of NPJ variability changed from the NPJ intensity changes to meridional shifts in NPJ position across the late 1990s. It leads to the change in the role of NPJ as a conduit for US SAT anomalies. Before the late 1990s, the variability of NPJ’s intensity significantly impacted western US SAT anomalies related to the anomalous surface cyclonic circulation over the North Pacific. After the late 1990s, however, the variability of NPJ’s meridional shift significantly influenced on the eastern US SAT anomalies in association with the anomalous surface cyclonic circulation over the northern North Pacific. Further analysis and model experiments revealed that the western to central North Pacific Ocean has been warming since the late 1990s and modulates atmospheric baroclinicity. This phenomenon mainly leads to a northward NPJ shift and implies that the eddy driven mechanism on the NPJ’s formation, which acts to enhance the meridional variability of NPJ position, becomes dominant. We conclude that this northward shift of NPJ could have contributed to enhancing the NPJ’s meridional shift variability, significantly influencing the eastern US SAT anomalies since the late 1990s.

In this study, we reviewed the maximum and minimum temperature trends of the Kathmandu valley over the period of 2011-2017. In addition, the average monthly temperature trends were studied annually for the same period, with the data made available from the Department of Hydrology and Meteorology (DHM) of Nepal. The annual temperature trended in the same direction, with winter temperatures being lower and summer temperatures being higher. The annual average minimum and maximum air temperature trends were found to be slightly rising at 0.097˚C/year and 0.04˚C/year, respectively. The mean air temperature in Kathmandu valley is increasing at a rate of 0.06 degrees Celsius per year, with 2016 being the warmest year and 2012 being the least warm, with annual mean temperatures of 19.82˚C and 19.32˚C, respectively. The temperature difference is much smaller in the summer (less than ~12˚C) than in the winter. BIBECHANA 18 (2) (2021) 95-104

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.

A thorough understanding of regional differences in change patterns of surface air temperature (SAT) at various spatial scales can help people cope well with global warming. In this study, a SAT change pattern recognition method was firstly established based on k-mean++ algorithm. By creating a clustering effect evaluation index (DBWk) to select the optimal cluster number k, the pattern of SAT change in 1960–2016 of China was recognized at national and regional scales. Results showed that China’s SAT change patterns were grouped into 3 clusters, namely, Clusters I, II and III, at the national scale. These clusters were further divided into 3, 7, and 4 subclusters at the regional scale, respectively. The SAT change in Cluster I was intense, with a relatively cold period (1960–1987) and a relatively warm period (1988–2016). The SAT of Cluster II decreased slightly in the first phase (1960–1983), minimally increased in the third phase (1999–2016), but rose strongly in the second phase (1984–1998). The linear trend (LT) of SAT increase of Cluster III was high and statistically significant, especially in 1983–2016. The analysis of the SAT change pattern of subclusters showed that the SAT fluctuations of the Altai Mountains and Junggar Basin were the strongest. The Northern Qinghai–Tibet Plateau had the highest warming rate, and the LT of warming was statistically the most significant in China.

This study investigates the relationship between the central tropical Pacific (CTP) sea surface temperature (SST) and the surface air temperature (SAT) variability un-related to canonical El Niño-Southern Oscillation (ENSO) over mid-to-high latitude Eurasia during boreal summers over the past half-century. The results show that their relationship experienced a decadal shift around the early 1980s. Before the early 1980s, the Eurasian SAT-CTP SST connection was weak; after that time, the relationship became stronger, and the SAT anomalies exhibited a significant wave-like pattern over Eurasia. Such a decadal change in the Eurasian SAT-CTP SST relationship could be attributed to decadal changes in the mean state and variability of CTP SST. The warmer mean state and enhanced SST variability after the early 1980s reinforced the convective activities over the tropical Pacific, leading to significantly anomalous divergence/convergence and Rossby wave sources over the North Pacific. This outcome further excited the wave train propagating along the Northern Hemisphere zonal jet stream to northern Eurasia and then affected the surface heat fluxes and atmospheric circulations over the region, resulting in wave-like SATs over Eurasia. However, during the period before the early 1980s, the CTP SST had a weak impact on the North Pacific atmospheric circulation and was consequently not able to excite the wave train pattern to impact the Eurasian atmospheric circulation and SATs. The physical processes linking the CTP SST and Eurasian SAT are further confirmed by numerical simulations. The results of this study are valuable to understanding the variability of summer Eurasian SATs.

This study investigates the impact of sea ice and snow changes on surface air temperature (SAT) trends on the multidecadal time scale over the mid- and high-latitudes of Eurasia during boreal autumn, winter and spring based on a 30-member ensemble simulations of the Community Earth System Model (CESM). A dynamical adjustment method is used to remove the internal component of circulation-induced SAT trends. The leading mode of dynamically adjusted SAT trends is featured by same-sign anomalies extending from northern Europe to central Siberia and to the Russian Far East, respectively, during boreal spring and autumn, and confined to western Siberia during winter. The internally generated component of sea ice concentration trends over the Barents-Kara Seas contributes to the differences in the thermodynamic component of internal SAT trends across the ensemble over adjacent northern Siberia during all the three seasons. The sea ice effect is largest in autumn and smallest in winter. Eurasian snow changes contribute to the spread in dynamically adjusted SAT trends as well around the periphery of snow covered region by modulating surface heat flux changes. The snow effect is identified over northeast Europe-western Siberia in autumn, north of the Caspian Sea in winter, and over eastern Europe-northern Siberia in spring. The effects of sea ice and snow on the SAT trends are realized mainly by modulating upward shortwave and longwave radiation fluxes.

Commencing in 1956, observations made at Halley Research Station, Antarctica provide one of the longest continuous series of near-surface temperature observations from the Antarctic continent. Since few other records of comparable length are available, the Halley record has been used extensively in studies of long-term Antarctic climate variability and change. The record does not, however, come from a single location but is a composite of observations from a sequence of seven stations, all situated on the Brunt Ice Shelf, that range from around 10 km to 50 km distance from the coast. Until now, it has generally been assumed that temperature data from all of these stations could be combined into a single composite record with no adjustment. Here, we examine this assumption of homogeneity. Application of a statistical change point algorithm to the composite record detects a sudden cooling associated with the move from Halley IV to Halley V station in 1992. We show that this temperature step is consistent with local temperature gradients measured by a network of automatic weather stations and with those simulated by a high-resolution atmospheric model. These temperature gradients are strongest in the coastal region and result from the onshore advection of maritime air. The detected inhomogeneity could account for the weak cooling trend seen in the uncorrected composite record. In future, studies that make use of the Halley record will need to account for its inhomogeneity.

In recent years, climate change has attracted many researchers’ attention around the world, among which the analysis of long-term surface air temperature (SAT) changes is essential. In this paper, we analyze the spatial and temporal variations of the SAT in Tarim Basin from 1961 to 2015. Our results show that warming of the SAT is evident at most weather stations in the Taklamakan Desert, except for those at Aketao, Kuche, and Tazhong. The overall warming rate is 0.25 °C/decade in the Taklamakan Desert. Mann–Kendall tests are adopted to detect abrupt changes in the SATs. It is demonstrated that nine out of 39 stations experienced a simultaneous abrupt change in 1996, which is consistent with the abrupt changes seen in the local vegetation cover. The relationships between the SAT changes and the underlying landscape and vegetation cover are also discussed in this paper. A moderate negative correlation is discovered between the SAT and the normalized difference vegetation index (NDVI) at oases.

A 13-member ensemble from CMIP5 is used to analyze the Iran future climate in terms of surface air temperature (TAS), identifying when anomalies of 1.5 ℃, 2.0 ℃ and 2.5 ℃ would be achieved respect to the preindustrial period (1861–1900) using the time sampling method. The global warming targets ( $${D}_{1.5}, {D}_{2.0}, {D}_{2.5}$$ ) are attained by the 2024, 2040 and 2056 under the RCP4.5 scenario, on the other hand, the respectively, attained years for Iran are 2023, 2036 and 2051, an evidence of a higher mean increase in surface air temperature respect to the global behavior. We identified important aspects regarding to temperature anomalies in the Iran region: (1) A mean warming of 1.0 ℃ for the 2001–2010 decade respect to the preindustrial baseline. (2) A mean warming of 0.5 ℃ for the 1960–2005 period respect to the preindustrial period. (3). For the 2006–2100 period under RCP4.5 scenario, was possible to identify a warming trend ( $$0.028 {^\circ{\rm C} } {\mathrm{year}}^{-1}$$ ), which is above double the trend observed in the period 1960–2005 ( $$0.012 {^\circ{\rm C} } {\mathrm{year}}^{-1}$$ ). 4) Largest trends over Iran occur for the 2006–2050 period (2.75 ± 0.74 to 4.72 ± 0.82 °C/century) respect to the 2006–2100 period (2.28 ± 0.36 to 3.39 ± 0.37 °C/century) with the most possible reason associated with the fact that under the RCP4.5 scenario, the emission rate increases toward 2040 and then stabilizes to the end of the century. All of these indicators evidence an intensification of the warming over the Iran region respect to the global trends. From the spatial analysis of surface air temperature trends over 5 regions of Iran for the 1850–2005, 1960–2005, 2006–2050 and 2006–2100 periods was possible to identify a significant increase in all the trends over Iran, but specially in the Southwestern of the country with a warming rate higher in warm regions than the cold climate regions. The present study reveled that further research should be development to explore renewable energy and create mitigation plans to minimize greenhouse gas emissions to overcome the increased risk of climate change effects.

Bridging the gap between weather forecasting and climate prediction, subseasonal to seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality. Using the S2S Prediction Project database, the study evaluates products derived from four operational centers of CMA, KMA, NCEP, and UKMO, and superensemble experiments including the straightforward ensemble mean (EMN), bias-removed ensemble mean (BREM), error-based superensemble (ESUP), and Kalman filter superensemble (KF), in forecasts of surface air temperature with lead times of 6–30 days over northeast Asia in 2018. Validations after the preprocessing of a 5-day running mean suggest that the KMA model shows the highest skill for either the control run or the ensemble mean. The nonequal weighted ESUP is slightly superior to BREM, whereas they both show larger biases than EMN after a lead time of 22 days. The KF forecast constantly outperforms the others, decreasing mean absolute errors by 0.2°–0.5°C relative to EMN. Forecast experiments of the 2018 northeast Asia heat wave reveal that the superensembles remarkably improve the raw forecasts featuring biases of >4°C. The prominent advancement of KF is further confirmed, showing the regionally averaged bias of ≤2°C and the hit rate of 2°C reaching up to 60% at a lead time of 22 days. The superensemble techniques, particularly the KF method of dynamically adjusting the weights in accordance with the latest information available, are capable of improving forecasts of spatiotemporal patterns of surface air temperature on the subseasonal time scale, which could extend the skillful prediction lead time of extreme events such as heat waves to about 3 weeks.

The selective ensemble mean (SEM) technique is applied to the late spring and summer months (May–August) surface air temperature anomaly predictions of the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), coupled general circulation model over Japan. Using the Köppen–Geiger climatic classification we chose four regions over Japan for applying the SEM technique. The SINTEX-F2 ensemble members for the SEM are chosen based on the anomaly correlation coefficients (ACC) of the SINTEX-F2 predicted and observed surface air temperature anomalies. The SEM technique is applied to generate the forecasts of the surface air temperature anomalies for the period 1983–2018 using the selected members. Analysis shows the ACC skill score of the SEM prediction to be higher compared to the ACC skill score of predictions obtained by averaging all the 24 members of the SINTEX-F2 (ENSMEAN). The SEM predicted surface air temperature anomalies also have higher hit rate and lower false alarm rate compared to the ENSMEAN predicted anomalies over a range of temperature anomalies. The results indicate the SEM technique to be a simple and easy to apply method to improve the SINTEX-F2 predictions of surface air temperature anomalies over Japan. The better performance of the SEM in generating the surface air temperature anomalies can be partly attributed to realistic prediction of 850-hPa geopotential height anomalies over Japan.

Based on the latest series of homogenized ground surface temperature (GST) and surface air temperature (SAT) data for China, this study performed a detailed analysis of the trend of the differences between the two homogenized series in 1961–2016. The differences, referred to as surface–air temperature differences (SATDs) in this study, were separately averaged by month, season, and year. The long-term and spatial changes in the trends of SATDs were investigated. Moreover, interdecadal trend breakpoints were identified to understand the characteristics of trends in fluctuation. The possible influences of precipitation, Pacific Decadal Oscillation (PDO), and global warming on SATDs were also analyzed. The results showed that during the 12 months of a year, only three months, March, April, and May, exhibited increasing trends of station-averaged, monthly mean SATDs while the other nine months exhibited decreasing trends. In addition, the station-averaged annual and seasonal mean SATDs of summer, autumn, and winter all showed significant decreasing trends, while the spring mean SATDs showed a significant increasing trend. The spatial distribution pattern of the linear trends of monthly, seasonal, and annual SATDs in meteorological stations indicated that SATDs had more obviously increasing trends in the northern regions than the southern regions of China. The trends of station-averaged monthly mean SATDs (except for April) and station-averaged seasonal and annual mean SATDs experienced interdecadal breakpoints, fully indicative of obvious interdecadal fluctuations with temporal complexity among China’s SATD trends. By the regression analysis of monthly STADs against simultaneous precipitation, as well as the comparative analysis of their linear trends, we found that both amount of precipitation and the change of precipitation type have important influences on SATDs. The results of convergent cross mapping analysis also revealed the causal effect of precipitation on SATDs.

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, by using linear analysis and nonlinear causality analysis—convergent cross mapping (CCM) and its time-lagged version (time-lagged CCM), 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.

Aerosol effects on the forecast of surface temperature, as well as temperature at the levels of 850 and 500 hPa over Europe and the European part of Russia are studied using various aerosol climatologies: Tanre, Tegen, and MACv2. The numerical experiments with the COSMO-Ru model are performed for the central months of the seasons (January, April, July, and October) in 2017. It is found that a change in the simulated surface air temperature over land can reach 1°С when using Tegen and MACv2 data as compared to Tanre. At 850 and 500 hPa levels, the changes do not exceed 0.4°С. At the same time, it is shown that a decrease in the root-mean-square error of 2-m air temperature forecast at individual stations reaches 0.5°С when using Tegen and MACv2 data and 1°С for clear-sky conditions in Moscow.

The manifestations of the main large-scale atmospheric processes, typical for the Atlantic–European sector, in winter air temperature anomalies in the Black Sea–Caspian region (40–50 °N 28–50 °E) were clarified according to the atmospheric reanalysis NCEP 20CR V2c data at an isobaric surface of 1000 hPa for 1851–2014. After removing the linear trend, the air temperature anomalies in December-January were decomposed into empirical orthogonal functions (EOF). It is shown that the first three leading EOFs describe ~ 96% of the total air temperature variability. The first EOF is a manifestation of the East Atlantic – Western Russia mode. The second EOF is due to the North Atlantic Oscillation. The third EOF corresponds to the Scandinavian pattern.

The present study investigates different impacts of the East Asian winter monsoon (EAWM) on surface air temperature (Ts) in North America (NA) during ENSO and neutral ENSO episodes. In neutral ENSO years, the EAWM shows a direct impact on the Ts anomalies in NA on an interannual time scale. Two Rossby wave packets appear over the Eurasian–western Pacific (upstream) and North Pacific–NA (downstream) regions associated with a strong EAWM. Further analysis suggests that the downstream wave packet is caused by reflection of the upstream wave packet over the subtropical western Pacific and amplified over the North Pacific. Also, the East Asian subtropical westerly jet stream (EAJS) is intensified in the central and downstream region over the central North Pacific. Hence, increased barotropic kinetic energy conversion and the interaction between transient eddies and the EAJS tend to maintain the circulation anomaly over the North Pacific. Therefore, a strong EAWM tends to result in warm Ts anomalies in northwestern NA via the downstream wave packet emanating from the central North Pacific toward NA. A weak EAWM tends to induce cold Ts anomalies in western-central NA with a smaller magnitude. However, in ENSO years, an anomalous EAJS is mainly confined over East Asia and does not extend into the central North Pacific. The results confirm that the EAWM has an indirect impact on the Ts anomalies in NA via a modulation of the tropical convection anomalies associated with ENSO. Our results indicate that, for seasonal prediction of Ts anomalies in NA, the influence of the EAWM should be taken into account. It produces different responses in neutral ENSO and in ENSO years.

Slow-varying atmospheric boundaries are the main sources of seasonal climate predictions, and their footprints on climate variables may be captured as predictable patterns. Based on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The main findings of this study are as following:The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m pattern over the Pacific–North America region, which is rooted in the tropical forcing of the eastern Pacific-type El Niño–Southern Oscillation (ENSO). The MSN EOF3 shows a wavelike T2m pattern over the Pacific–North America region, which has an approximately 90° phase difference from that associated with MSN EOF2, and a loading center over midlatitude Eurasia. Its sources of predictability include the central Pacific-type ENSO and Eurasian snow cover. The MSN EOF4 reflects T2m variability surrounding the Tibetan Plateau, which is plausibly linked to the remote forcing of the Arctic sea ice. The information on the leading predictable patterns and their sources of predictability is further used to develop a calibration scheme to improve the prediction skill of T2m. The calibrated prediction skill in terms of the anomaly correlation coefficient improves significantly over midlatitude Eurasia in a leave-one-out cross-validation, implying a possible way to improve the wintertime T2m prediction in the SEAS5.

Reanalysis products are an invaluable tool for representing variability and long-term trends in regions with limited in situ data, and especially the Antarctic. A comparison of eight different reanalysis products shows large differences in sea level pressure and surface air temperature trends over the high-latitude Southern Ocean, with implications for studies of the atmosphere's role in driving ocean–sea ice changes. In this study, we use the established close coupling between sea ice cover and surface temperature to evaluate these reanalysis trends using the independent, 30-year sea ice record from 1980 to 2010. We demonstrate that sea ice trends are a reliable validation tool for most months of the year, although the sea ice–surface temperature coupling is weakest in summer when the surface energy budget is dominated by atmosphere-to-ocean heat fluxes. Based on our analysis, we find that surface air temperature trends in JRA55 are most consistent with satellite-observed sea ice trends over the polar waters of the Southern Ocean.

A possibility of using the one-dimensional Kalman filter to improve the forecast of surface air temperature at an irregular grid of point is studied. This mechanism is tested using the forecasts obtained from different configurations of two different numerical weather prediction models. An algorithm for the statistical correction of numerical forecasts of surface air temperature based on the one-dimensional Kalman filter is constructed. Two methods are proposed for estimating the bias noise dispersion. The series of experiments demonstrated the effectiveness of the algorithm for the bias compensation.The most significantresults are achieved for the models with large bias or for long-range forecasts. At the same time, the use of the algorithm has little effect on the root-meansquare error of the forecast. Keywords: hydrodynamic model of the atmosphere, numerical weather prediction, statistical correction of numerical forecasts, Kalman filter

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

The paper presents the results of the verification of ERA-Interim and ERA5 reanalyses data on surface air temperature obtained from drifting buoys, ground-based weather stations, and, for the first time, from measurements at the North Pole drifting stations. The North Pole station data were not assimilated in the reanalyses, which provides a rare opportunity for independent validation. The comparison with data of the North Pole drifting stations revealed that bias for the cold season in the Arctic basin is 2.25°С for ERA-Interim and 3.92°С for ERA5, respectively. The comparison with data of drifting buoys allows us to speculate about the cause of such large errors in the reanalyses. Some buoys were installed from the air-based platform. In this case, the temperature sensor of the buoy was potentially buried in the snow cover that shielded it from the cold atmosphere and contributed to the heating due to the heat flux from the underlying layer of sea water. The assimilation of such data could be one of the reasons for the overestimation of air temperature over drifting ice in the both reanalysis.

Evolution of the autumn snowpack has been considered as a potential source for the subseasonal predictability of winter surface air temperature, but its linkage to precipitation variability has been less well discussed. This study shows that the snow water equivalent (SWE) over the Urals region in early (1–14) November is positively associated with precipitation in southern China during 15–21 November and 6–15 January, based on the study period 1979/80–2016/17. In early November, a decreased Urals SWE warms the air locally via diabatic heating, indicative of significant land–atmosphere coupling over the Urals region. Meanwhile, a stationary Rossby wave train originates from the Urals and propagates along the polar-front jet stream. In mid (15–21) November, this Rossby wave train propagates downstream toward East Asia and, combined with the deepened East Asian trough, reduces the precipitation over southern China by lessening the water vapor transport. Thereafter, during 22 November to 5 January, there are barely any obvious circulation anomalies owing to the weak land–atmosphere coupling over the Urals. In early (6–15) January, the snowpack expands southward to the north of the Mediterranean Sea and cools the overlying atmosphere, suggestive of land–atmosphere coupling occurring over western Europe. A stationary Rossby wave train trapped in the subtropical westerly jet stream appears along with anomalous cyclonic circulation over Europe, and again with a deepened East Asian trough and less precipitation over southern China. The current findings have implications for winter precipitation prediction in southern China on the subseasonal timescale.

Based on the snowfall observations at 836 surface weather stations in China and the Daily Surface Climate Variables of China version 3.0 dataset for 1961–2013, capability of five methods with different objective criteria for identifying wintertime snowfall is evaluated, to provide reference for application of these methods in snowfall/rainfall discrimination. Methods I, II, III, IV, and V use the daily average surface air temperature (Ta), wet-bulb temperature (Tw), dynamic threshold Tw, 0-cm ground temperature, and 700–850-hPa thickness, respectively, to identify the snowfall. The results show that the climatological distribution of snowfall can be well produced by Methods I, II, and III. Method IV underestimates the snowfall days in eastern Tibetan Plateau (ETP), and Method V cannot yield the actual large numbers of snowfall days and amounts. Accordingly, the linear trends of snowfall days estimated from Methods I, II, and III largely agree with the observations, while a discrepancy is found in the linear trend of snowfall amounts over southeastern China (SEC). For interannual and decadal variations of snowfall, Method V shows the worst performance. It is more reasonable to use Tw to distinguish snowfall from rainfall instead of Ta, 0-cm ground temperature, and 700–850-hPa thickness; and the reference thresholds of Tw in northeastern China (NEC), northwestern China (NWC), ETP, and SEC are −1.5, −1.5, −0.4, and −0.3°C, respectively. The above results are beneficial to identifying snowfall in short-term climate prediction.

Accurate subseasonal-to-seasonal (S2S) atmospheric forecasts and hydrological forecasts have considerable socioeconomic value. This study conducts a multimodel comparison of the Tibetan Plateau snow cover (TPSC) prediction skill using three models (ECMWF, NCEP and CMA) selected from the S2S project database to understand their performance in capturing TPSC variability during wintertime. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Compared with the observational snow cover analysis, all three models tend to overestimate the area of TPSC. Another remarkable issue regarding the TPSC forecast is the increasing TPSC with forecast lead time, which further increases the systematic positive biases of TPSC in the S2S models at longer forecast lead times. All three S2S models consistently exaggerate the precipitation over the Tibetan Plateau. The exaggeration of precipitation is prominent and always exists throughout the model integration. Systematic bias of TPSC therefore occurs and accumulates with the model integration time. Such systematic biases of TPSC influence the forecasted surface air temperature in the S2S models. The surface air temperature over the Tibetan Plateau becomes colder with increasing forecast lead time in the S2S models. Numerical experiments further confirm the causality.