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Accurate estimates of essential climate variables, including ocean surface specific humidity (qa) and air temperature (Ta), at the air–sea interface are critical in understanding many atmospheric and oceanic processes and impacts and can be used to inform data-assimilative forecast systems about the coupled atmosphere-ocean state. To aid in addressing this scientific community need, the data processing component of the Naval Research Laboratory (NRL) Ocean Surface Flux System (NFLUX) uses multiple polynomial regression algorithms to produce single-sensor satellite-based swath-level estimates of surface state parameters and heat fluxes over the global ocean. This study extends the current NFLUX processing to include data processing from multiple sensors onboard the same platform. Specifically, we are combining satellite brightness temperature data from the Compact Ocean Wind Vector Radiometer (COWVR) and the Temporal Experiment for Storms and Tropical Systems (TEMPEST) to produce qa and Ta estimates. These combined surface estimates have error statistics comparable to current NFLUX satellite-based swath-level estimates from single sensors.

Abstract This study examines the multi-decadal variability and trends of surface air temperature and precipitation in the Indian Cardamom Hills (ICH), a degraded tropical rainforest area unique for cardamom cultivation. Utilizing observed long-term climatic data (1958–2017), statistical methods such as the Mann–Kendall test (MKT), Sen’s Slope Estimator (SSE), and Incremental Trend Analysis (ITA) were applied to assess the impact of surface air temperature, rainfall, and the number of rainy days on cardamom yield. The analysis revealed a significant decline in annual rainfall by approximately 13.62 mm per year, with pronounced seasonal declines 0.87 mm for winter, 12.33 mm for pre-monsoon, 24.93 mm for southwest monsoon, and 18.10 mm for post-monsoon. Simultaneously, the number of rainy days dropped by nearly 19.75 days over the 40-year period. A noticeable increase in decadal minimum and average temperatures was observed, highlighting potential adverse effects on cardamom yield and irrigation water resources. The findings suggest that excessive rainfall during the southwest monsoon negatively correlates with cardamom yield, while slightly warmer temperatures show a weak positive correlation. The study also emphasizes the need for adaptive agricultural practices and climate-resilient policies to mitigate the effects of changing climatic conditions on cardamom production. This research contributes valuable insights for farmers and other stakeholders as well as policymakers aiming to ensure sustainable cardamom cultivation amidst climate change.

Abstract We examine daily surface air temperatures (SAT) in the Arctic under global warming, synthesizing changes in mean temperature, variability, seasonality, and extremes based on five Earth system model large ensembles from the Coupled Model Intercomparison Project Phase 6. Our analysis shows that the distribution of daily Arctic SAT changes substantially, with Arctic mean temperatures being distinguishable from pre‐industrial levels on 84% and 97% of days at 1.5 and 2°C of global warming, respectively, and on virtually every day at 3°C of global warming. This shift is primarily due to the rapid rise in average temperature resulting from Arctic amplification and is exacerbated by a decrease in the variability of daily Arctic SAT of approximately 8.5% per degree of global warming. The changes in mean temperature and variability are more pronounced in the cold seasons than in summer, resulting in a weakened and shifted seasonal cycle of Arctic SAT. Moreover, the intensity and frequency of warm and cold extreme events change to varying degrees. The hottest days warm slightly more, while the coldest days warm 4–5 times more than the global average temperature, making extreme cold events rare. Changes in local SAT vary regionally across the Arctic and are most significant in areas of sea‐ice loss. Our findings underscore the Arctic's amplified sensitivity to global warming and emphasize the urgent need to limit global warming to mitigate impacts on human and natural systems.

The variation of global radiation from 1950–2020 and its impact on the terrestrial surface air warming was investigated based on measurements of sunshine duration, global radiation and temperature from six measuring stations from the national weather services in Austria, Switzerland and Germany.A quantitative estimation of that impact was conducted with the help of a multiple linear regression model based on monthly values from the period 1950–2020. The applicability of this approach was ensured by a set of five statistical standard tests.The overall temperature increase from 1950–2020 (about +2.1 °C in the annual and summer semi-annual average at the six measuring stations) is dominated by the temporally continuous temperature increase (global warming). However, a fraction of the increase (+0.3 °C corresponding to about 14% and +0.5 °C corresponding to about 25% for the entire year and the summer half-year, respectively) is attributed to the increase of global radiation.With regard to the brightening period in the last four decades (ca. 1982–2020), the fraction of the increase of global radiation within the total temperature increase is substantial: about one third for the entire year (ca. +0.6 °C), even half for the summer half-year (ca. +1.1 °C). The model results are reasonable in view of the comparison with the temperature measurements at the six stations.Increased air pollution by aerosols led to a continuous weakening of global radiation at the earth's surface (global dimming) in Europe from about 1950–1980, but with the subsequent improvement of air quality, an increase occurred again (global brightening). Global brightening, which has continued until today, has more than compensated for global dimming of 1950–1980. From 1982–2020, direct solar radiation increased by about one-third in both the summer and winter months. This increase is about twice as large as the decrease during the dimming phase, except for the case of nearly overcast sky.The phases of global dimming and brightening are mainly caused on one hand by increasing and decreasing opacity of the atmosphere by aerosols, the direct aerosol effect. On the other hand, indirect radiative aerosol effects of cloud droplets, including effects of cloud properties, have had an increasing impact on global radiation.

This study used 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze the mean and extreme projection precipitation during the Indian summer monsoon (ISM) season, i.e., June to September (JJAS). A continuous rise in precipitation and its associated unpredictability were predicted by global climate models using the CMIP6. The extreme values are estimated on the basis of generalized extreme value (GEV) distribution. Four socioeconomic paths are used as SSP126, SSP245, SSP370 and SSP585 to understand the impact of low-emission to high-emission scenarios. In all the scenarios, it is seen that all the models predict a significant rise in JJAS mean and extreme precipitation. Moreover, a strong correlation is obtained between the historical (1995–2014) and near-emission future simulation (2021–2040), mid-emission future simulation (2041–2060), high-emission future simulations (2081–2100) of air temperature and precipitation. In extreme emission scenarios, ensemble mean of CMIP6 models shows an increasing amplitude in surface air temperature and precipitation over India in the near future (2021–2040), mid future (2041–2060) and the end of the century (2081–2100). The CMIP6 simulations mainly support the results of all models, but they also demonstrate improved robustness across models. HIGHLIGHTS Projection of mean and extreme precipitation over India using CMIP6 models during JJAS season.; A generalized extreme values distribution is utilized to estimate the climate extreme.; Four socioeconomic paths are used as SSP126, SSP245, SSP370, and SSP585 to understand the impact of low-emission to high-emission scenarios over precipitation.; A strong association is observed between surface air temperature and precipitation over India.; Extreme precipitation may rise up to 80% over India in the SSP585 scenario.;

Recognizing the relationship between gross primary production (GPP) and precipitation in eastern China, the East Asian Summer Monsoon (EASM) plays a crucial role in shaping GPP. Despite confirmation of the strong link between EASM and GPP, there remains a notable research gap in understanding the specific impact of the EASM on GPP in different regions of eastern China. Here we used simulations from Trends in Net Land–Atmosphere Carbon Exchanges (TRENDY) models from 1951 to 2010 and divided eastern China into five subregions for the study. We also used the New East Asian Summer Monsoon Index (NEWI) as a quantitative metric to distinguish between periods of strong and weak EASM. Building on this, this study aims to investigate the response of GPP in different subregions of eastern China. Regionally, under strengthened EASM years (1954, 1957, 1965, 1969, 1977, 1980, 1983, 1987, 1993 and 1998), East China experienced the most pronounced increase in GPP at 12 ± 21 (mean ± 1 sigma) gC m−2 mon−1 compared to the weak EASM years (1958, 1961, 1972, 1973,1978, 1981, 1985, 1994, 1997 and 2004). In contrast, Southwest China showed a decline in GPP at −4 ± 10 gC m−2 mon−1. Moreover, GPP also increased in Northeast and North China when EASM strengthened, while South China showed a decline in GPP. This indicated that GPP changed with monsoon intensity. According to the mechanism analysis, during strong EASM, there was intense moisture convergence through alterations in the atmospheric circulation field over East China and abundant precipitation, which further contributed to the increase in GPP. Downward solar radiation in Southwest China decreased with EASM enhancement, which suppressed GPP and hindered vegetation growth. Overall, the results highlight the importance of accurately predicting the impact of different EASM intensities of regional carbon fluxes.

Assumptions of linear lapse rates in regions prone to surface-based inversions (SBIs) can generate biases in the prediction of surface air temperature. Although studies of Arctic inversions are common, few regional studies of their characteristics exist in high-latitude regions with mountainous topography. To address this gap, vertical atmospheric temperature profiles for five sites in northwestern Canada were analysed using archived radiosonde data from 1990 to 2016. We present monthly, seasonal, and annual SBI characteristics including the occurrence of transient and persistent SBIs. A novel metric, surface-based inversion impact (SBIimp), was developed by combining the traditional inversion characteristics of depth, strength, and frequency, and was used to quantify the impact of SBIs on cooling the surface air temperature. SBIimp values of >5 °C yr−1 and ∼10 °C winter−1 occur locally. A weak linear relationship between sea ice coverage in the Beaufort Sea and SBIimp manifests across parts of the study area, though this relationship does not persist after detrending the datasets. Topographic analysis of areas surrounding each radiosonde location reveals highly variable SBIimp in complex mountain areas and more consistent SBIimp across areas of low relief. Our results can help interpret the role of inversions in climatic conditions maintaining cryospheric elements such as permafrost.

Permafrost distribution in high-latitude continental mountains is a product of both latitudinal and elevationally controlled attributes. Frequently occurring surface-based temperature inversions (SBIs) significantly modify surface lapse rates (SLRs) annually. We aim to identify and quantify patterns of SBI characteristics in two proximal yet morphologically and vegetatively dissimilar central Yukon valleys. Elevational transect analysis is applied by using sensors in valley bottoms and 100 m upslope to determine in situ SLRs for the study period (August 2017 – August 2021). SLRs were shown to vary significantly between these dissimilar valleys. Climate reanalysis products (ClimateNA and Globsim) underestimated or almost entirely missed the presence of strong SBIs which produce annual average SLRs that range from 0.46 to 1.2 °C 100 m−1. The magnitude of these hyper-inversions was grossly underpredicted by previous surface air temperature modelling that attempted to account for SBIs across Yukon. Our results support the previously conceptualized framework that strong SBIs influence surface air temperatures and the pattern of permafrost distribution.

In order to know how surface air temperature (SAT) changes in East Asia under different emission scenarios after the COVID-19 outbreak, we investigated the impacts of greenhouse gases (GHGs) and anthropogenic aerosols changes on SAT in East Asia by using the aerosol-climate coupled model BCC-AGCM 2.0_CUACE/Aero, combining with the post-pandemic emission scenarios proposed by Covid multi-Earth system model intercomparison project (CovidMIP scenarios for short, including fossil-fueled recovery, moderate green stimulus, strong green stimulus, hereinafter as FFF, MGG, SGG, respectively). We assessed the impacts of changes in GHGs and anthropogenic aerosols together and separately on SAT in East Asia and its typical subregions during 2020–2050. The results show that by mid-21st-century, SAT in East Asia will increase by 0.81 ± 0.083 °C under Baseline (same as SSP2-4.5 scenario), i.e., SAT difference between 2045–2050 and 2020–2025), and there will be more intense warming in all the three scenarios in East Asia, in which the largest SAT difference (SAT-d) compared to Baseline is 0.33 ± 0.11 °C under SGG and the smallest SAT-d is 0.07 ± 0.14 °C under FFF. To further explore the mechanism of these SAT-d, we analyzed the trend of surface longwave and shortwave net radiation flux driven by GHGs and anthropogenic aerosols there. It is found that in early period (2020–2035), the role of aerosol changes is bigger than that of GHG changes in dominating SAT-d, particularly sulfate, whose reduction will become the main contributor to SAT-d by affecting the net solar flux at surface. In later period (2036–2050), because of GHGs’ longer atmospheric lifetime than aerosols, the role of decreasing GHGs concentrations will determine the drop in SAT-d through affecting the net longwave flux at surface.

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation.

This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test were employed to identify the change cycle and abrupt change year of the boundary layer height. The Empirical Orthogonal Function (EOF) method was utilized to determine the spatial distribution of the boundary layer height, and the RF method was used to establish the relationship between the ABLH and influencing factors. The results demonstrated that the highest values of ABLH (over 1900 m) were observed in the middle parts of the study area in June, and the ABLH exhibited a significant increase over the TB throughout the study period. Abrupt changes in the ABLH were also identified in 2004, as well as in 2-, 5-, 9-, and 15-year changing cycles. The first EOF ABLH mode indicated that the middle and northeast regions are relatively high ABLH areas within the study area. Additionally, the monthly variations in ABLH show a moderately positive correlation with air temperature, while exhibiting a negative correlation with air pressure and relative humidity.

In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring.

Abstract Quantitative assessment of urbanization effect on surface air temperature (SAT) change provides crucial basis for formal detection and attribution analyses of climate change. However, debates about urbanization‐related warming bias in documented regional SAT trend still persist, mainly due to different determination of rural stations. Here the urbanization effect on SAT change over the Beijing–Tianjin–Hebei region in China during 1980–2019 is estimated through three kinds of ways (i.e., comparisons between urban and rural stations [arithmetically station‐averaged], urban‐dominated and rural‐dominated patches [patch‐weighted mean], and realistic urban and rural areas [area‐weighted mean]). The last method explicitly takes urban and rural land cover fractions into account when calculating urban/rural and regional mean SAT trends. Urbanization‐induced warming in the annual mean SAT change of urban stations (areas) through the three ways are estimated as 0.159°C, 0.195°C, and 0.138°C per decade, respectively. And urbanization effect on regional averaged annual mean SAT calculated by patch‐weighted and area‐weighted methods are 0.113°C and 0.050°C per decade, respectively, which account for 33.8% and 14.8% of the total regional warming. The urbanization effect on observed SAT change estimated by considering realistic urban/rural land cover proportions is much lower than traditional station‐unweighted way.

The Arctic climate is changing rapidly, along with intensified melting of sea ice, which has significant impacts on surface air temperature (SAT) in Eurasia. This study reveals that the subseasonal response of SAT to the autumn Kara–Laptev Sea ice (KLSIC) differs significantly between early and late winter. The response of SAT to KLSIC forms a warm Arctic–cold Eurasia pattern in early winter. Conversely, the negative anomaly response of SAT to KLSIC in late winter is only distributed in the band range of Eurasia, without significant positive SAT anomaly over the Arctic Ocean. After further examination of the separate physical mechanisms involved in early and late winter, it is found that a decrease in KLSIC in autumn can lead to a strengthened Ural high and Siberian high in the Arctic–Eurasia region, which is conducive to cold events in the mid-latitudes of Eurasia in early winter. For late winter, a westward shift in the response of atmospheric circulation to KLSIC leads to a negative anomaly feedback of North Sea surface temperature, which triggers the propagation of Rossby waves to the Sea of Japan through the wave activity flux. Meanwhile, the deep trough of East Asia is strengthened and extends to the southeast, guiding northern cold air to the western Pacific. Our results highlight that different subseasonal effects of sea ice should be considered in Eurasian climate prediction, rather than only consider the effects in winter mean.

In the 20th century, with the intensification of human activities, the Earth is experiencing unprecedented warming. However, there are certain differences in the sensitivity of temperature changes to anthropogenic forcings in different regions and at different altitudes of the troposphere. The time of emergence (TOE) is the key point at which the anthropogenic climate change signal exceeds from the internal climate variability serving as a noise. It is a crucial variable for climate change detection, climate prediction and risk assessment. Here, we systematically analyzed the spatiotemporal characteristics of the TOE of temperature changes over the past century by calculating the SNR based on the selected CMIP6 multi-model outputs. The results show that the temperature TOE, particularly in the lower and middle troposphere, shows distinct latitude dependence, displaying an ‘M-type’ distribution from the Antarctic to the Arctic: it first appears in low-latitudes, followed by high-latitudes, and last appears in the two mid latitude bands. For the tropics, the TOE of tropospheric temperatures becomes earlier with increasing altitude: the TOE of air temperatures at the surface, mid-tropospheric 500 hPa and upper-tropospheric 200 hPa occurs in 1980 ± 15, 1965 ± 20, and 1930 ± 30, respectively. The TOEs of tropospheric temperatures in eastern equatorial Pacific are 10–30 years later than those in the western equatorial Pacific. For the regional TOEs of surface air temperature diverse differences exist on land and ocean in various latitudes of two hemispheres.

Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from observations, which limits seasonal prediction skill of climate models. In this study, we attempted to improve prediction skill of surface air temperature (SAT) with two post-processing on dynamical model’s seasonal forecast: (1) correction of the AO impact on SAT pattern, and (2) correction of AO index (AOI). The first correction involved replacing the inaccurately simulated impact of AO on SAT with that observed. For the second correction, we employed a empirical prediction model of AOI based on multiple linear regression model based on three precursors: summer sea surface temperature, autumn sea-ice concentration, and autumn snow cover extent. The application of the first correction led to a decrease in prediction skills. However, a significant improvement in SAT prediction skills is achieved when both corrections are applied. The average correlation coefficients for the North America and Eurasian regions increased from 0.23 and 0.06 to 0.28 and 0.30, respectively.

The rapid warming of the Tibetan Plateau (TP) in recent decades has led to severe consequences, including the melting of glaciers and snow cover, which further accelerates warming. Accurately projecting the magnitude of future warming is crucial for effective climate change adaptation. However, the projection of future temperature change is model dependent. In this study, we demonstrate a significant correlation between the historical inter-model warming trend and future temperature change, suggesting this relationship could be used to calibrate the best estimate of projections and reduce the uncertainty by observations. For a high emission scenario, the constraint helps to narrow down the uncertainty range of annual and summer temperature change on the western TP by up to 2 °C and 4 °C, respectively, in the end of this century. The most substantial calibrated increase of future change is in winter by up to 2 °C, followed by autumn with an increase by about 1 °C. Discrepancies of historical warming trend among different observation datasets expose the largest impact on the constrained best estimate compared with emergent relationship derived from different climate models and warming trend in different historical periods.

Surface Air Temperature (SAT) is an important weather element affecting the climate and its nature, as it greatly affects the nature of the atmosphere prevailing in the city of Baghdad, as well as the main influence on the pressure systems affecting Iraq and thus the city of Baghdad. In this study, the annual SAT for three aspects of temperatures (mean, maximum, and minimum) for Baghdad city of approved 11 years had studied, analyzed and discussed. The sources of data came from two sources. First, Iraqi Meteorological Organization and Seismology (IMOS), which represented the arithmetic average annually of the SAT, maximum and minimum temperature for the period (2009 – 2019). Second source of data was from Mustansiriyah University, atmospheric sciences department of Atmospheric Sciences - college of Science, the data are supported as monthly average for SAT for Baghdad. Results shown Basrah was high annual of SAT than four selective cities in Iraq. The variation of annual SAT was in the end study period where reach to 7 oC in mean of SAT for Basrah city. The trend of SAT for Baghdad city was positive for Mean and maximum, while negative for minimum case. Mustansiriyah station was high than IMOS station by 2 oC for mean of SAT, and 5 oC for maximum SAT.

The surface air temperature (SAT) over the Tibetan Plateau (TP) not only affects the physical processes such as local evaporation, snow melting, and glacier ablation, but also has a great impact on the downstream regions and even the global climate change. The development of reanalysis data has gradually overcome the problem of sparse stations over the TP, but there are still some deficiencies. Therefore, the distance between indices of simulation and observation (DISO) method is used to calculate the distance between five reanalysis datasets (ERA5, JRA-55, ERA-Interim, MERRA2, NCEP2) and the CMFD to evaluate the abilities of different reanalysis datasets to capture the SAT over the TP in different seasons. The results indicate that ERA-Interim has a higher ability to reproduce the SAT over the TP in spring and summer, while it is ERA5 in autumn and winter. It should be noted that although the optimal reanalysis has a better performance in capturing the SAT of the TP, there are still a certain degree of deviations in their spatial fields. We further show the spatial deviation fields of SAT over the TP corresponding to the optimal reanalysis data in different seasons, and analyze the possible reasons. The result implies that the SAT deviation field is mainly related to the snow in winter and spring, while the summer SAT deviation field is mainly related to the water vapor, and the autumn is related to both the snow and the water vapor fields. Overall, the quality of reanalysis data needs to be further improved in the future.

The increase in the average surface air temperature anomaly (SATA) over China is higher than the global average. However, the accurate simulation and attribution of regional SATA evolution remain challenging for current global climate models. This study simulates historical SATA variations over China using the coupled (FGOALS-g3) and uncoupled (atmospheric component, GAMIL3) models and examines their possible causes. Results show that both models reproduce the historical SATA variation with higher correlation coefficients (0.735 and 0.782) than many global climate models (0.25–0.56), although they overestimate or underestimate the changes of SATA to some extent in different periods. The results show that the cooling trend during 1941–1970 is well simulated with the coupled model while poorly presented with the uncoupled model; the coupled simulations particularly produce stronger long-term trends than the uncoupled ones during 1870–2014 considering full interaction among the atmosphere, ocean, and sea ice. In contrast, the uncoupled simulations reproduce better decadal and multi-decadal SATA variations owing to the constraints of the observed sea surface temperature (SST), such as the Atlantic multidecadal oscillation, and sea ice cover. Using Detection and Attribution Model Intercomparison Project (DAMIP) experiments, we found that the warming in the early 20th century and the recent 50 years is mainly driven by natural forcings and greenhouse gases (GHGs), whereas the cooling during 1941–1970 is caused by natural factors and anthropogenic aerosols. The cooling effects of anthropogenic aerosols are mainly attributed from the indirect SST-mediated responses through the atmosphere-ocean interactions in the coupled model.

Abstract Globally, a variety of numerical weather prediction (NWP) models are employed by national agencies. However, the derived outputs from these models are generally inadequate for precise cultivation management due to bias error. This study uses observational data acquired over several months to develop an equation for correcting surface air temperature (SAT) error in NWP model outputs. With the proposed method, model outputs are converted into potential temperature to cancel the influence of differences between computational elevation and real elevation. The potential temperature errors in the NWP model outputs are corrected using a linear regression equation in which a radiative cooling scale (RCS) serves as the independent variable. The RCS is computed using air temperature observed at a nearby meteorological observation station, together with upper‐level pressure data from the NWP model. Versions of the correction models are developed for specific situations: day, night, and upper‐level meteorology, classified into six groups. In an application of the proposed method, the root mean square error (RMSE) of the corrected SAT output from a global spectral model operated by the Japan Meteorological Agency (JMA) was 1.2–1.6 K; the RMSE of the corrected SAT output from a nonhydrostatic model operated by the JMA was 1.2–1.5 K. The proposed method corrected the NWP model SAT output errors to a level of accuracy similar to that of data corrected using the Kalman filtering technique without the requirement for constant observation at the target site.

Sea ice conditions in the Canadian Arctic Archipelago (CAA) play a key role in the navigation of the Northwest Passage (NWP). Limited by observed sea ice thickness data, the research of temporal and spatial variation of sea ice in the NWP is insufficient. Based on the observed sea ice concentration and simulated thickness data, the temporal and spatial characteristics of sea ice concentration, extent and thickness from 1979 to 2017 in the NWP of the CAA were studied. The more specific pathways of the northern and southern routes of the NWP were evaluated. Against the background of the rapid retreat of Arctic sea ice, the 39-year observed sea ice concentration and extent of the NWP exhibited a relatively large decreasing trend in summer and fall, while heavy sea ice conditions were maintained in winter and spring, with slightly increasing trend in some subregions. The sea ice thickness in most subregions of the NWP showed a decreasing trend, with exception of Lancaster Sound. The sea ice thickness was larger along the northern route than the southern routes. The significant correlation (p

Suhu udara permukaan (SUP) merupakan salah satu indikator penting dalam konteks terjadinya perubahan iklim. Penelitian ini bertujuan untuk mengkaji variasi temporal dan spasial beserta trend SUP di Pulau Jawa pada tahun 1990-2019. Data yang digunakan diperoleh dari observasi Badan Meteorologi Klimatologi dan Geofisika (BMKG) dan data pemodelan reanalisis (GHCN_CAMS, ERA-5, JRA-55, dan GISTEMP-v4). Data observasi BMKG diperoleh berdasarkan data bulanan dari stasiun BMKG yang tersebar di seluruh Pulau Jawa (16 stasiun). Hasilnya menunjukkan bahwa terjadi peningkatan SUP dalam 30 tahun sebesar 0,11-1,24℃. Variasi spasial menunjukkan bahwa daerah dengan SUP tertinggi terdapat di Pulau Jawa sebelah utara bagian timur dan terendah di dataran tinggi Pulau Jawa bagian barat. SUP di Pulau Jawa tertinggi terjadi pada periode bulan September-Oktober-November (SON) dan terendah pada periode bulan Juni-Juli-Agustus (JJA). Hasil uji statistik menggunakan metode Mann Kendall test dengan significance level 1% menunjukkan bahwa semua data observasi BMKG mengalami trend peningkatan kecuali untuk Banyuwangi. Trend peningkatan SUP per tahun berkisar antara 0,017-0,048℃. Hal ini menunjukkan bahwa perubahan iklim secara regional telah terjadi di Pulau Jawa selama 30 tahun terakhir berdasarkan trend perubahan SUP.

Examining large-scale characteristics of the difference between ground surface temperature (GST) and surface air temperature (SAT) and its long-term trend will help understand land surface energy exchange and the effect of land-atmosphere interaction on climate change and variability. Based on a homogenized monthly dataset of GST and SAT from 1961 to 2018, this study analyzes the spatial distribution and long-term trend of the difference between ground surface temperature and surface air temperature (GST–SAT) in the warm season (April to October) over China mainland. The results show that the warm-season mean GST–SAT in the Qinghai-Tibet Plateau and the northwestern deserts have the largest GST–SAT. On average, the GST–SAT in China is the greatest in summer, with the maximum monthly value occurring in July. During 1961–2018, the warm-season mean GST–SAT undergoes a significant increasing trend (0.04 °C/10yr, p < 0.01), with the largest increase seen in mid-late spring (April and May), and the smallest increase in August. Spatially, the GST–SAT increases significantly in the northern region, decreases slightly in the southern region, and remains unchanged in the Qinghai-Tibet Plateau. The warm-season mean GST–SAT is significantly positively correlated with altitude and sunshine duration (R = 0.50, 0.40; p < 0.05), and significantly negatively correlated with relative humidity and precipitation (R = 0.48, −0.42; p < 0.05), in the country on a whole in the analysis period.

Climate change has caused many changes in hydrologic processes and climatic conditions globally, while extreme events are likely to occur more frequently at a global scale with continued warming. Given the importance of general circulation models (GCMs) as an essential tool for climate studies at global/regional scales, together with the wide range of GCMs available, selecting appropriate models is of great importance. In this study, six synoptic weather stations were selected as representative of different climatic zones over Iran. Utilizing monthly data for 20 years (1981–2000), the outputs of 25 GCMs for surface air temperature (SAT) and precipitation were evaluated for the historical period. The root-mean-square error and skill score were chosen to evaluate the performance of GCMs in capturing observed seasonal climate. Finally, the outputs of selected GCMs for the three Representative Concentration Pathways emission scenarios (RCPs), namely RCP2.6, RCP4.5, and RCP8.5, were downscaled using the change factor method for each station for the period 2046–2065. Results indicate that SAT in all months is likely to increase for each region, while for precipitation, large uncertainties emerge, despite the selection of climate models that best capture the observed seasonal cycle. These results highlight the importance of selecting a representative ensemble of GCMs for assessing future hydro-climatic changes for Iran.

Abstract We investigated interdecadal relationship changes between the Arctic oscillation (AO) and Aleutian low (AL) for the period 1980–2021 along with the linkage on surface air temperature (SAT) over Eastern Eurasia during the boreal winter (December–January–February). The AO–AL relationship has weakened since the mid‐1990s. To explore a possible explanation, we compared atmospheric fields for two contrasting periods, one when the AO–AL relationship is the strongest (1986–2006) and one when it is the weakest (2000–2020). In both periods, atmospheric circulation over the Arctic region for the two AO–AL in‐phase relationships (i.e., either +AO/+AL or –AO/−AL) evolves with negative AO‐like patterns, contributing to a weakening AO–AL relationship in concurrent with a cool SAT in Eastern Eurasia. We found that the correlation between AL and SAT over Eastern Eurasia decreases, while the AO and SAT correlation remains unchanged. This implies that low‐frequency atmospheric circulation changes such as those of the AO and AL should be considered when studying SAT changes in Eastern Eurasia.

El Niño is the long-lasting anomalous warming of sea surface temperature (SST) and surface air temperature (SAT) over the tropical Pacific. Each El Niño event has its unique impact on the overlaying atmosphere, where the warming exhibits diversity in spatiotemporal patterns. It still remains an open question for discriminating the El Niño diversity, since the single area-averaging SST index often fails to distinguish the impact of the event diversity, which is partially due to the nonlinear and non-uniform variations of the warming patterns. Here, we introduced the Dynamical Systems metrics (DSMs) to measure instantaneous dimensions and persistence of the SAT warming patterns over the tropical Pacific. Our results show that different SAT warming patterns can be discriminated by their corresponding values of dimension and persistence, then the central Pacific and eastern Pacific El Niño events can be discriminated by DSM. Particularly, through the analyses of El Niño events, we can interpret the physical meaning of DSM parameters applied to the space-time SAT field: an instantaneous dimension reflects whether the sub-regions of the SAT field are consistently varying and to what degree the spatial pattern of anomalies is homogeneous, while the instantaneous persistence indicates how long an anomalous SAT pattern can be maintained. This work analyzes the spatiotemporal variability of El Niño from a dynamical system perspective, and DSM may also serve as a useful tool to study extreme events related to SST anomalies.

The longest instrumental meteorological observations on the Svalbard archipelago were obtained in the Russian town Barentsburg, where the first meteorological station (MS) appeared back in 1932, however, the use of these data in its original form is complicated by the interruption in observations related to World War II. In the paper a critical analysis of metadata (changing the location of meteorological observations within the town Barentsburg) was carried out, as well as checking the climatic uniformity of the series. Missing data on surface air temperature (SAT) were restored for the period 1941-1947 based on the regression analysis with the Norwegian MS “Isfjord Radio” and “Longyearbyen”. For periods when observations on the archipelago were not carried out at all (the period of World War II), the data were restored using NOAA-CIRES-DOE 20CRv3 reanalysis. Data recovery from December 1911 was carried out on the basis of measurements made at the MS “Spitsbergen Radio”. In 2013 - 14 parallel measurements were carried out by two automatic weather stations at the historical site of the weather site MS “Spitsbergen Radio” and at the modern weather site GMO “Barentsburg”, which made it possible to establish the statistical relationship between the SAT values ​​in these two points. As a result, composite series of average monthly SAT ​​have been obtained since 1911 for the MS “Barentsburg”, and it can be used for correct estimates of long-term climate changes in the region in different seasons of the year. Moreover, for the period from 1911 to 2018 the proportion of original (measured) SAT values ​​exceeds 75%, which is the maximum possible number among any other reconstruction of such series on Svalbard.

Abstract 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.

The linear relationship between average monthly anomalies of the ice coverage in the Arctic seas and the surface air temperature over the land in the Northern hemisphere in March and September was analyzed for the purpose of finding regions with statistically significant correlations. Possible mechanisms of the revealed interrelations are discussed. Data on the surface temperature and the ice concentration from Met Office Hadley Centre were used in this study. A negative correlation of the sea ice with the temperature in the land regions adjacent to the seas, as well as a number of remote relations was revealed. Specifically, statistically significant relations were found between anomalies of the ice area in the Laptev Sea in September with the temperature anomalies in the Mediterranean region, as well as with the temperature anomalies in Central Asia. In most cases, such relationships may be explained by the influence of atmospheric circulation, including the North Atlantic Oscillation, the Arctic Oscillation, the Pacific Decadal Oscillation, and variability in the intensity of the atmospheric centers of action. Characteristics of seasonal variations of the sea ice coverage and climatic trends together with variability and autocorrelation of the coverage anomalies are considered. The largest reduction in the ice area is observed for the recent decades in the Barents Sea in winter while in the Kara, Laptev and East Siberian seas - in summer.

Fast quality control (FQC) is important to deal with high-frequency observation records at meteorological station networks in time, and may check whether the records fall within a range of acceptable values. Threshold tests in the previous quality control methods for monthly, daily, or hourly observation data do not work well for 0.5 Hz data at a single station. In this study, we develop an algorithm for the automatic determination of maximum and minimum minute thresholds for 0.5 Hz temperature data in the data collection phase of the newly built stations. The fast threshold test based on the percentile threshold (0.1–99.9%) and standard deviation scheme is able to efficiently identify the incorrect data in the current minute. A visual graph is generated every minute, and the time series of the data records and the thresholds are displayed by the automated graphical procedures. The observations falling outside the thresholds are flagged and then a manual check is performed. This algorithm has the higher efficiency and lower computational requirement in identifying out the obvious outliers of 0.5 Hz data in real or near-real time observation. Meanwhile, this algorithm can also find problems in observation instruments. This method is applied to the quality control of 0.5 Hz data at two Tianjin experiment stations and hourly data at one Shenyang experiment station. The results show that this fast threshold test may be a viable option in the data collection phase. The advantage of this method is that the computation requires less memory and the computational burden is reduced for real or near-real time observations, so it may be extended to test other meteorological variables measured by high-frequency measurement systems.

The utility of a forecast depends on its skill as demonstrated by past performance. For most forecasts errors rapidly become large compared to uncertainties in the observation-based state of the system and, for this reason, it is usually deemed adequate to assess predictions against a single verification dataset. Eleven reanalyses and station-based analyses of annual mean surface air temperature are compared as are basic skill measures obtained when using them to verify decadal prediction hindcasts from the Canadian Centre for Climate Modelling and Analysis forecasting system. There are differences between reanalysis and station-based analyses which translate also into differences in basic skill scores. In an average sense, using station-based verification data results in somewhat better correlation skill. The spread between the locally best and worst scores is obtained for individual forecast ensemble members and for ensemble mean forecasts compared to individual analyses. The comparison of ensemble mean forecasts against different analyses can result in apparent skill differences, and using a “favorable” analysis for verification can improve apparent forecast skill. These differences may be more pertinent for longer time averages and should be considered when verifying decadal predictions and when comparing the skill of decadal prediction systems as part of a model intercomparison project. Either a particular analysis could be recommended by the decadal prediction community, if such could be agreed on, or the ensemble average of a subset of recent analyses could be used, assuming that ensemble averaging will act to average out errors.

Based on the China Meteorological Administration Land Data Assimilation System (CLDAS) reanalysis data and 12–72 h forecasts of the surface (2-m) air temperature (SAT) from the European Centre for Medium-Range Weather Forecasts (ECMWF) and three numerical weather prediction (NWP) models of the China Meteorological Administration (CMA-GFS, CMA-SH, and CMA-MESO), multi-model ensemble forecasts are conducted with a convolutional neural network (CNN) and a feed-forward neural network (FNN) to improve the SAT forecast in Henan Province, China. The results show that there are large errors in the 12–72 h forecasts of SAT from the CMA, while the ECMWF outperforms the other raw NWP models, especially in eastern and southern Henan. The CNN has the best short-term forecasting skills. The difference in the geographical distribution of the CNN forecast errors is small, without any apparent large-value areas. The CNN shows its advantages in its bias correction in the mountainous region (western Henan), indicating that the CNN can capture the spatial features of the atmospheric fields and is therefore more robust in regions with varied topography. In addition, the CNN can extract data features through the convolution kernel and focus on local features; it can assimilate the local features at a higher level and obtain global features. Therefore, the CNN takes advantage of the four models in the SAT forecast and further improves the forecast skill.

The spatiotemporal distributions of wildfire areas and FRP values for the territory of Russia and its large regions (the European part of Russia, as well as the Ural, Siberian, and Far Eastern Federal Districts) during 2001–2022 were analyzed using satellite data. For the territory of Russia, there was a decreasing trend in annual burned areas and a small increase in average hotspot FRP. At the same time, the largest annual burned areas in the territory of Russia were recorded in 2008 (295.2 thous. km2), 2002 (272.4 thous. km2), 2006 (261.2 thous. km2), and in 2012 (258.4 thous. km2). It was found that during the studied period, 90% of fire hotspots in Russia had a maximum FRP < 100 MW. The most intense wildfires (FRP > 1500 MW) amounted to only 0.1% and were detected mainly in the Siberian and Far Eastern Federal Districts. Interconnections between large wildfires and meteorological factors, including blocking activity in the atmosphere, were revealed.

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth’s surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China’s new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets.

Reversals of surface air temperature anomalies over mid-latitude Asia (SAT _MA ) have become more frequent. Six winters have been reversed over the last decade, which had serious negative consequences for agricultural production, ecological systems, and human health. This subseasonal reversal can be observed in the second mode of season-reliant empirical orthogonal function analysis, showing a persistent opposite anomaly between early and late winter. The difference in SAT _MA between early and late winter exceeds 3 °C when the reversal occurs. At the time of the reversal, the North Atlantic jet stream extends eastward in early winter and recedes westward in late winter. The main influencing system changes from the Scandinavia teleconnection to the Ural blocking high, accompanied by a change from strong southerly to northerly winds. These factors jointly lead to the reversal of SAT _MA between early and late winter. More importantly, the North Atlantic Oscillation (NAO) has been shown to affect SAT _MA variability in a variety of different ways. Changes in SAT _MA are influenced not only by the intensity of the NAO, but also by its spatial distribution. When the southern center of the NAO shifts westward and is located over the North Atlantic, atmospheric circulation and SAT _MA in early and late winter show similar changes to those seen in SAT _MA reversal winters. In contrast, when the NAO southern center shifts eastward and is located over Western Europe, persistent anomalies are observed in atmospheric circulation and SAT _MA throughout the winter. Therefore, when studying the relationship between SAT _MA and the NAO, attention should be paid to the different responses of SAT _MA to the spatial distribution of the NAO at subseasonal timescales.

A regional potential vorticity (PV) intrusion (PVI) (RPVI) index, defined as the sum of the numbers of grids containing PVI within a certain area for each day, is used to reflect the day-to-day PVI variability over northeastern China during winter from 1979 to 2016. The synoptic-scale PVI variations and resultant surface air temperature (SAT) anomalies are identified by comparing the high and low RPVI index cases. In high (low) RPVI cases, significantly strong positive (negative) PV anomalies are found in the stratospheric midlatitudes, which intrude downward mostly within 90°–110°E into the upper troposphere to reach around 300 hPa and extend eastward to the east of 120°E, forcing an anomalous cyclonic (anticyclonic) circulation in the middle and lower troposphere over East Asia, with the anomalous northerlies (southerlies) of the forced lower-tropospheric cyclone (anticyclone) leading to significant negative (positive) SAT anomalies of less (greater) than −0.9°C (0.9°C), especially over northeastern China. In the stratosphere, the positive (negative) midlatitude PV anomalies over northern China are actually associated with a weakening (strengthening) of the polar vortex over the Eurasian continent for the high (low) RPVI cases, resulting mostly from positive (negative) barotropic vorticity anomalies associated with static stability due to the meridional shear of anomalous zonal winds on the southern side of the anomalous Eurasian anticyclone (cyclone).

The combined effect of the Pacific–Japan (PJ) pattern and Mediterranean–northern Eurasia (MnE) pattern on East Asian surface air temperature (SAT) during summer is investigated using the Japanese 55-year reanalysis and Climatic Research Unit SAT data over the period of 1958–2016. The results show that the combination of the two patterns in different phases can result in different SAT anomalies. During the in-phase PJ-MnE years, the overlapping of opposite signs of the atmospheric circulations associated with the PJ and MnE patterns results in weak atmospheric circulation and SAT anomalies in central East Asia; during these years, the significant SAT anomalies are over northern East Asia. In contrast, during the out-of-phase PJ-MnE years, the overlapping of the same signs of the atmospheric circulations associated with the PJ and MnE patterns leads to significant atmospheric circulation and SAT anomalies in central East Asia and northern Asia. The analysis in this study indicates that to better understand and predict the variability of East Asian summer SATs, the combined effect of the PJ and MnE patterns should be taken into account.

Abstract Urban development exacerbates urban heat island effects of cities and thermal discomfort of the residents worldwide. However, such urban effects vary with the geographical and climatic conditions of the respective cities. This study is the first to quantitatively estimate the impact of past (1878), present (2012), and future (2050) urbanization on surface air temperatures and human thermal comfort in Sofia, the largest city of Bulgaria. We used the state‐of‐the‐art Weather Research and Forecasting (WRF) climate model, with a 1‐km horizontal resolution for three cases: LU1878, LU2012, and LU2050. We first validated the results of the control simulation (case LU2012) against actual observations. The model satisfactorily reproduced the diurnal variation of the observed monthly mean surface air temperatures for July (2011–2013) with a mean bias of ±0.6°C. We then estimated the potential impacts of past and future urbanization on surface temperature. Our results suggest that because of urbanization of Sofia, the surface air temperature at 0600 Local Standard Time (LST) has increased approximately 4.0°C in the past (1878–2012) and approximately 0.4°C in future (2012–2050). We also evaluated the impact of the urban thermal environment on human comfort levels. Considering the LU2050 case in the central part of Sofia, the temperature–humidity index during mid‐afternoon (1400–1700 LST) is estimated to be 24–26, indicating that approximately 50% of the residents could feel perceptibly uncomfortable.

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.

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.

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.

It has been hypothesised that the Arctic amplification of temperature changes causes a decrease in the northward temperature gradient in the troposphere, thereby enhancing the oscillation of planetary waves leading to extreme weather in mid-latitudes. To test this hypothesis, we study the response of the atmosphere to Arctic amplification for a projected summer sea-ice-free period using an atmospheric model with prescribed surface boundary conditions from a state-of-the-art Earth system model. Besides a standard global warming simulation, we also conducted a sensitivity experiment with sea ice and sea surface temperature anomalies in the Arctic. We show that when global climate warms, enhancement of the northward heat transport provides the major contribution to decrease the northward temperature gradient in the polar troposphere in cold seasons, causing more oscillation of the planetary waves. However, while Arctic amplification significantly enhances near-surface air temperature in the polar region, it is not large enough to invoke an increased oscillation of the planetary waves.

Most peripheral seas of the Arctic Ocean have seen a pronounced rise in sea surface temperatures in the past century, and this signature of Arctic amplification in proximity to the land suggests that the observed marine and terrestrial changes might be connected to each other. Using in situ observations of temperature from nine coastal meteorological stations around Greenland (GrSTs) and remotely sensed fields of sea ice extent (SIE), we examine the interannual variations of surface air temperature (T2m) and sea level pressure (SLP) anomalies associated with the GrSTs and SIEs surrounding Greenland, specifically within Baffin Bay, the Greenland Sea and Kara-Barents Seas. During winter, the interannual variation in T2m and SLP of the west and south coasts of GrSTs and the Baffin Bay SIE are different from that of the east coast of GrSTs and the SIEs in the Greenland Sea and Kara-Barents Seas. The GrSTs on the west and south coasts of Greenland and the Baffin Bay SIE are associated with the T2m anomalies over Baffin Bay and Davis Strait. The winter SLP patterns associated with these GrSTs and SIEs show positive anomalies over the Arctic and negative anomalies over the North Atlantic with a large-scale atmospheric circulation such as the winter NAO. On the contrary, the east coast of GrSTs and the SIEs in the Greenland Sea and Kara-Barents Seas are correlated with the T2m anomalies over the Greenland Sea and Barents Sea. The surface wind pattern associated with the SIEs in the Greenland Sea and Kara-Barents Seas has a cyclonic circulation in the Greenland Sea and Barents Sea. At the local scale the cyclonic circulation could induce negative SIE anomalies and contribute to increasing open water in the Greenland Sea and Barents Sea. The effect of the loss of sea ice and the heat from the open ocean warming to the atmosphere may influence the GrSTs in the east coast of Greenland. As a result, the T2m pattern associated with the GrSTs in the east coast of Greenland is similar to the pattern of the SIEs in the Greenland Sea and Kara-Barents Seas. During summer, the T2m anomalies associated with all GrSTs and SIEs have positive anomalies over mid-latitudes. The two times series of all GrSTs and SIEs fluctuate quickly and display large trends towards warming temperatures and decreasing SIE. The summer SLP associated with all GrSTs and SIEs are characterised by a seesaw pattern between positive anomalies over the Arctic and negative anomalies over mid-latitudes. The summer SLP anomalies are similar to the summer AO pattern, and it is noteworthy that the summer anticyclonic circulation over the Arctic and Greenland has contributed to the variability and trends in both summer GrSTs and SIEs.