Surface Air Temperature Variability Research Articles

Probing the timescale dependency of local and global variations in surface air temperature from climate simulations and reconstructions of the last millennia.

Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. Essential climate variables, such as surface air temperature, describe this dynamics. Our current interglacial, the Holocene (11700 yr ago to today), has been characterized by small variations in global mean temperature prior to anthropogenic warming. However, the mechanisms and spatiotemporal patterns of fluctuations around this mean, called temperature variability, are poorly understood despite their socioeconomic relevance for climate change mitigation and adaptation. Here we examine discrepancies between temperature variability from model simulations and paleoclimate reconstructions by categorizing the scaling behavior of local and global surface air temperature on the timescale of years to centuries. To this end, we contrast power spectral densities (PSD) and their power-law scaling using simulated and observation-based temperature series of the last 6000 yr. We further introduce the spectral gain to disentangle the externally forced and internally generated variability as a function of timescale. It is based on our estimate of the joint PSD of radiative forcing, which exhibits a scale break around the period of 7 yr. We find that local temperature series from paleoclimate reconstructions show a different scaling behavior than simulated ones, with a tendency towards stronger persistence (i.e., correlation between successive values within a time series) on periods of 10 to 200 yr. Conversely, the PSD and spectral gain of global mean temperature are consistent across data sets. Our results point to the limitation of climate models to fully represent local temperature statistics over decades to centuries. By highlighting the key characteristics of temperature variability, we pave a way to better constrain possible changes in temperature variability with global warming and assess future climate risks.

Long-Term Spatial and Temporal Variation of Near Surface Air Temperature in Southwest China During 1969–2018

Near surface air temperature (NSAT) is one of the most important climatic parameters and its variability plays a vital role in natural processes associated with climate. Based on an improved ANUSPLIN (short for Australian National University Spline) model which considers more terrain-related factors, this study analyzed the trends, anomalies, change points, and variations of NSAT in Southwest China from 1969 to 2018. The results revealed that the improved approach performed the best in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R-squared (R2) comparing to the conventional ANUSPLIN and co-kriging methods. It has great potential for future meteorological and climatological research, especially in mountainous regions with diverse topography. In addition, Southwest China experienced an overall warming trend of 0.21°C/decade for annual mean NSAT in the period 1969–2018. The warming rate was much higher than mainland China and global averages, and statistically significant warming began in the late 1990s. Moreover, consistent warming and significant elevation-dependent warming (EDW) were observed in most parts of Southwest China, and the hiatus or slowdown phenomenon after the 1997/1998 EL Niño event was not observed as expected. Furthermore, the remarkable increase in winter and minimum NSATs contributed more to the whole warming than summer and maximum NSATs. These findings imply that Southwest China responds to global warming more sensitively than generally recognized, and climate change in mountainous regions like Southwest China should be of particular concern.

Panama’s Current Climate Replicability in a Non-Hydrostatic Regional Climate Model Nested in an Atmospheric General Circulation Model

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.

Evaluation of re-analyses over China based on the temporal asymmetry of daily temperature variability

As an intrinsic feature of daily surface air temperature (SAT) variability found in station measurements, temporal asymmetry (TA) can be taken as an evaluation metric to access the quality of SAT re-analysis product. In this study, TA calculated from four SAT variables, i.e., daily mean SAT (Tmean), daily maximum SAT (Tmax), daily minimum SAT (Tmin) and diurnal temperature range (TDTR = Tmax − Tmin), is applied to evaluate synoptic-scale performance of four reanalysis products (NCEP-2, JRA-55, ERA-I, and ERA-5) over China. The results show that four re-analyses overall overestimate the TA of daily Tmax and Tmin variability over China, but with a comparatively consistent estimated TA for Tmean. Moreover, the TA of Tmean variability for these four re-analyses shares high spatial consistency with those from the observation. However, four re-analyses own the similar region-dependent spatial patterns of overestimated TA for Tmax and Tmin variability, especially for Tmax. Since high TA is an indicator for strong nonlinear feature, only Tmean reanalysis is the most suitable to explore synoptic-scale extreme events, such as heat waves and cold waves, which are highly related to the strong nonlinear processes.

Future projection of extremely hot and precipitation events over Southeast Asian river basins under RCP8.5 scenario

AbstractSoutheast Asian (SEA) river basins, particularly Huang He, Yangtze and Mekong river basins, are home for large share of global population. The region is experiencing rapid socio‐economic development, urbanization and industrialization. Using Community Earth System Model large ensemble data, seasonal mean surface air temperature (SAT) in summer is projected to increase by ~6°C under Representative Concentration Pathway (RCP) 8.5 scenario. As a consequence, in the long term (2071–2100), record‐breaking extreme hot and precipitation events are projected to increase by ~90–100% and ~ 60–70%, respectively. The climate mean SAT will increase by 4 ~ 5.5°C by the end of 21st century. Extreme events occur due to the combined influence of external forcing factors and internal variability. The relative strength of both the factors are estimated by signal‐to‐noise ratio (SNR), that is, the ratio of external forcings and internal variability. Until 2000/1998/2000, SNR < 1 over Huang He/Yangtze/Mekong river basins, that is, internal variability dominates over external forcings, while from 2001/1999/2001 onwards, SNR > 1, that is, external forcings strongly override the internal climate variability. Interestingly, the SAT variability at 50th (mean state) and 95th (record‐breaking hot/wet extreme) percentile will decrease significantly as climate warms in the future. It contradicts the existing hypothesis that as the climate warms in the future, variability will increase and weather will get more volatile everywhere. Interestingly, in our analysis, SAT variability appears to decrease over SEA river basins, which is opposite to the current understanding. It makes the result a novel one. We analyse the temperature–precipitation relationship under RCP8.5 scenario, which displays a peak‐shaped structure, that is, precipitation is increasing at low–mid‐range of temperature rise, while it decreases at higher temperatures.

Reconstructing past thermal conditions in beach microclimates.

Reconstruction of past conditions provides important information on how ecosystems have been impacted by climate change, but generally for microhabitats worldwide there are no long-term empirical measurements. In these cases, there has been protracted debate about how various large-scale environmental proxies can best be used to reconstruct local temperatures. Here we help resolve this debate by examining how well environmental proxies hindcast sand temperatures at nest depths for five sea turtle nesting sites across the world. We link instrumental air temperature and sea surface temperature records with empirical sand temperature observations in the Atlantic (Ascension Island and Cape Verde), the Indian Ocean (Chagos Archipelago), the Caribbean (St Eustatius) and the Pacific (French Polynesia). We found strong correlations between sea surface temperatures, air temperatures and sand temperatures at all our study sites. Furthermore, Granger causality testing shows variations in sea surface temperature and air temperature precede variations in sand temperatures. We found that different proxies (air or sea temperature or a combination of both) predicted mean monthly sand temperatures within<0.5°C of empirical observations. Reconstructions of sand temperatures over the last 170years reveal a slight warming of temperatures (maximum 0.5°C per century). An analysis of 36 published datasets revealed that the gradient of the relationship between sand temperature and air temperature is relatively constant, suggesting long-term changes in sand temperature could be extended around the world to include nesting sites where there are no empirical measurements of sand temperature. Our approaches are likely to have utility for a range of microhabitats where there is an interest in long-term changes in temperature.

Comprehensive evaluation of surface air temperature reanalysis over China against urbanization-bias-adjusted observations

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.

Spatial patterns of multi–centennial surface air temperature trends in Antarctica over 1–1000 CE: Insights from ice core records and modeling

The spatial pattern of Antarctic surface air temperature variability on multi–decadal to multi–centennial time scales is poorly known because of the short instrumental records, the relatively small number of high–resolution paleoclimate observations, and biases in climate models. Here, changes in surface air temperature over Antarctica are reconstructed over the past two millennia using data assimilation constrained by different ice core water isotope records in order to identify robust signals. The comparison between previous statistically based temperature reconstructions and simulations covering the full Common Era driven by natural and anthropogenic forcings shows major discrepancies occurring in the period 1–1000 CE over East Antarctica, with the reconstructions displaying a warming over 1–500 CE that is not reproduced by the simulations. This suggests that the trends in the first millennium deduced from the statistically based reconstructions are unlikely to be entirely forced by external forcings. Our reconstructions show the high sensitivity of the 500-year temperature trend in Antarctica and its spatial distribution to selection of the records for the reconstructions, especially during 1–500 CE. A robust cooling over Antarctica during 501–1000 CE has been obtained in three data assimilation–based reconstructions with a larger magnitude in the WAIS than elsewhere over Antarctica, in agreement with previous estimates with the larger changes than simulated in climate models. The reconstructions for atmospheric circulation indicate that the pattern of temperature changes over 501–1000 CE is related to the positive trend of Southern Annular Mode and a deepening of Amundsen Sea Low. This confirms the role of internal variability in the temperature trends on multi–centennial scales.

Influence of the NAO on Wintertime Surface Air Temperature over East Asia: Multidecadal Variability and Decadal Prediction

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.

Weakened Antarctic Dipole Under Global Warming in CMIP6 Models

AbstractThe Antarctic dipole (ADP) depicts the leading mode of interannual variability over the Antarctic region in both sea ice and surface air temperature (SAT) fields. The ADP is superimposed on the long‐term climatic trends, modulating the response of the mean climate to external forcing by modifying the signal‐to‐noise ratio. The response of the ADP to greenhouse warming is unknown. Based on the Coupled Model Intercomparison Project Phase 6 models that capture main characteristics of the ADP, we find a robust reduction in variability of ADP under greenhouse warming. The warming‐induced sea ice loss limits the magnitude of ADP SAT variability through surface albedo feedback. The El Niño‐Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) are two main triggering mechanisms of the ADP on interannual time scale. In a warmer climate, decreased variability of the SAM contributes to weakened ADP but increased ENSO variability plays an offsetting role.

The influence of the wind field and stratification on the nocturnal surface air temperature over modest topography

AbstractOur study examines the horizontal variation of the nocturnal surface air temperature by analyzing measurements from four contrasting networks of stations with generally modest topography. The horizontal extent of the networks ranges from 1 to 23 km. For each network, we investigate the general relationship of the horizontal variation of temperature to the wind speed, wind direction, near-surface stratification, and turbulence. As an example, the horizontal variation of temperature generally increases with increasing stratification and decreases with increasing wind speed. However, quantitative details vary significantly between the networks. Needed changes of the observational strategy are discussed.

Diverse Responses of Global‐Mean Surface Temperature to External Forcings and Internal Climate Variability in Observations and CMIP6 Models

AbstractWe investigate the impacts of external forcings and internal climate variability (ICV) on global‐mean surface air temperature (GMST) variations (1850–2014) in observation and an ensemble of sixth Coupled Model Intercomparison Project (CMIP6) historical simulations. Using multiple regression models, we estimate that 83% of the observed GMST variance is explained by global‐scale anthropogenic forcing. Most CMIP6 models underestimate this anthropogenic contribution (median explained variance is 67%), but compare better with observation when the North Atlantic regional contribution is included in the anthropogenic forcing (median 77%). The ICV contributions to the GMST variance are ∼8% for observation and ∼7% for the models (median). The models' GMST responses to the external forcings and ICV vary widely, with the two responses being oppositely related. The North Atlantic predominantly drives the free GMST variability in observations, whereas the Pacific drives this in most CMIP6 models due to stronger than observed simulated GMST‐Pacific ICV relationships.

Relative contributions of internal atmospheric variability and surface processes to the interannual variations in wintertime Arctic surface air temperatures

AbstractSuperimposed on a warming trend, Arctic winter surface air temperature (SAT) exhibits substantial interannual variability, whose underlying mechanisms are unclear, especially regarding the role of sea-ice variations and atmospheric processes. Here, atmospheric reanalysis data and idealized atmospheric model simulations are used to reveal the mechanisms by which sea-ice variations and atmospheric anomalous conditions affect interannual variations in wintertime Arctic SAT. Results show that near-surface interannual warming in the Arctic is accompanied by comparable warming throughout large parts of the Arctic troposphere and large-scale anomalous atmospheric circulation patterns. Within the Arctic, changes in large-scale atmospheric circulations due to internal atmospheric variability explain a substantial fraction of interannual variation in SAT and tropospheric temperatures, which lead to an increase in moisture and downward longwave radiation, with the rest likely coming from sea ice-related and other surface processes. Arctic winter sea-ice loss allows the ocean to release more heat and moisture, which enhances Arctic warming; however, this effect on SAT is confined to the ice-retreat area and has a limited influence on large-scale atmospheric circulations.

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

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

Influence of North Atlantic sea surface temperature anomalies on springtime surface air temperature variation over Eurasia in CMIP5 models

Influence of North Atlantic sea surface temperature anomalies on springtime surface air temperature variation over Eurasia in CMIP5 models

Impacts of sunspot number and Geomagnetic aa-index on climate of Wet Zone West Africa during solar cycles 22\u201324

Using the facilities at Heliophysics Science Division of NASA Goddard Space Flight Center, Greenbelt, MD, USA, we attempted to investigate the impact of solar magnetic activities on the climate of Wet Zone West Africa. The solar activity data namely, Sunspot Number (SSN) was obtained from the Royal Observatory of Belgium, Brussels; and Geomagnetic aa-index was obtained from World Data Center, Kyoto, Japan. Surface Air Temperature (SAT) and Rainfall data [for Port Harcourt in Nigeria and Abidjan in Cote D’Ivoire] were obtained from the HadCRUT-4 project of Climate Research Unit of University of East Anglia, United Kingdom. Firstly, we carried out Time Series Analysis of SSN and Geomagnetic aa-index spanning from 1950 to 2016. Secondly, we performed Regression Analysis on both solar activity data and climate variables to estimate the impact of solar magnetic activity on the Wet Zone West African climate. The Time Series Analysis showed that SSN variation was in-phase with Geomagnetic aa-index in all the solar cycles studied. Thus, Geomagnetic aa-index can be used as a proxy for studying solar magnetic activities. Performance of Regression Analysis showed that SSN regressed on SAT and Rainfall amounted to an average of 0.49 and 0.02% respectively throughout Solar Cycles 22–24. Furthermore, a regression of Geomagnetic aa-index on SAT and Rainfall yielded an average of 0.145 and 0.125% respectively. Our models showed that the variability of SAT and Rainfall in Wet Zone West Africa during Solar Cycles 22–24 are far less than 1%. Hence, the influence of SSN and Geomagnetic aa-index on SAT and Rainfall is less than 1%; and could cause ‘very small’ effect. These weak impacts are proofs that the variability of SAT and Rainfall were most probably not effected by SSN and Geomagnetic aa-index. Consequently, the variability of SAT and Rainfall in Wet Zone West Africa could not be attributed to SSN and Geomagnetic aa-index. We therefore, attempt to conclude that climate variability in Wet Zone West Africa is most probably not driven by solar magnetic activity, but could be attributed to anthropogenic activities.

The Atlantic Multidecadal Variability Phase Dependence of Teleconnection between the North Atlantic Oscillation in February and the Tibetan Plateau in March

AbstractThe Tibetan Plateau (TP), referred to as the “Asian water tower,” contains one of the largest land ice masses on Earth. The local glacier shrinkage and frozen-water storage are strongly affected by variations in surface air temperature over the TP (TPSAT), especially in springtime. This study reveals that the relationship between the February North Atlantic Oscillation (NAO) and March TPSAT is unstable with time and regulated by the phase of the Atlantic multidecadal variability (AMV). The significant out-of-phase connection occurs only during the warm phase of AMV (AMV+). The results show that during the AMV+, the negative phase of the NAO persists from February to March, and is accompanied by a quasi-stationary Rossby wave train trapped along a northward-shifted subtropical westerly jet stream across Eurasia, inducing an anomalous adiabatic descent that warms the TP. However, during the cold phase of the AMV, the negative NAO cannot persist into March. The Rossby wave train propagates along the well-separated polar and subtropical westerly jets, and the NAO–TPSAT connection is broken. Further investigation suggests that the enhanced synoptic eddy and low-frequency flow (SELF) interaction over the North Atlantic in February and March during the AMV+, caused by the southward-shifted storm track, helps maintain the NAO pattern via positive eddy feedback. This study provides a new detailed perspective on the decadal variability of the North Atlantic–TP connection in late winter to early spring.

Role of the warming trend in global land surface air temperature variations

Role of the warming trend in global land surface air temperature variations

Spatio‐temporal variability of summer monsoon surface air temperature over India and its regions using Regional Climate Model

AbstractIn this study, a dynamically downscaled regional climate model (RegCM4.3) is used to study the Indian summer monsoon (ISM) surface air temperature over the South‐Asia CORDEX domain using six convection schemes during 1986–2010. The spatial and temporal variability of mean surface air temperature has been analysed with reference to the India Meteorological Department (IMD) analysis data using various statistical scores. The sensitivity experiments in selecting the best convective parameterized schemes have been performed in simulating the surface air temperature during the summer monsoon season (June–September) over India and its five sub‐regions such as Northwest India, Northcentral India, West Peninsular India, Eastern Peninsular India, and Southern Peninsular India. The model results show the tendency of overestimation of surface air temperature mainly in four cumulus parameterization schemes (CPSs) that is, Tiedtke, Emanuel, Mix98, and Mix99 of RegCM4.3 during the JJAS, where Grell and Kuo CPSs show better agreement with the IMD data. Overall, Grell CPS has a close resemblance to the observation data with a minimum root mean square error, mean absolute error, lowest mean absolute percentage error (MAPE), and higher correlation coefficient. The model simulated results have also been investigated further using modified Nash Sutcliffe efficiency and modified Willmott's degree of index. These analyses confirm the potentiality of the Grell CPS followed by the Kuo CPS in simulating interannual variability of the surface air temperature over Indian and its five sub‐regions. The MAPE in Grell and Kuo CPSs are 0.004 and 0.013°C during monsoon season over India, respectively. The inter‐scheme difference in simulating surface air temperature is linked with the generation of low cloud convection and warming‐induced atmospheric moisture advection in the schemes. Therefore, Emanuel, Tiedtke, and Mix98 CPSs have shown a persistent nature of overestimation in surface air temperature variability during JJAS. It is also inferred that after removing the systematic mean bias from the RegCM4.3 model simulated outputs; the skill of Emanuel, Mix98, and Mix99 could be useful over the Indian subcontinent except for the southern peninsular region.

Enhanced Relationship between Central Tropical Pacific Sea Surface Temperature and Eurasian Surface Air Temperature during Boreal Summers

AbstractThis 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.