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The efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere. Plain Language Summary: The temporal and spatial distribution mechanism of physical parameters in near space is very complex. It is of great significance to carry out fine modeling and forecasting of atmospheric temperature profiles for spacecrafts in near space. In this study, taking a single‐station temperature profile in the stratosphere as the object, the mathematical mapping problem of temperature operator is realized by a new deep learning method (deep operator networks). This provides a new research perspective for fine spatiotemporal modeling of temperature profiles. The proposed temperature operator shows low cumulative error performance in forecasting low latitude temperature profiles, and is also suitable for high latitude temperature profiles and mid latitude wind profiles in the stratosphere. Key Points: A deep neural operator method is utilized to forecast hourly atmospheric temperature profiles in the stratosphereThe temperature profile operator network based on BiLSTM denotes lower cumulative error in multi‐step iterative forecastingThe proposed method exhibits potential performance in different locations and atmospheric variables [ABSTRACT FROM AUTHOR]

Rotational temperatures in the Mesosphere‐Lower Thermosphere region are estimated by utilizing the OH(6,2) Meinel band nightglow data obtained with an Ebert‐Fastie spectrometer (EFS) operated at Arecibo Observatory (AO), Puerto Rico (18.35°N, 66.75°W) during February‐April 2005. To validate the estimated rotational temperatures, a comparison with temperatures obtained from a co‐located Potassium Temperature Lidar (K‐Lidar) and overhead passes of the Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) instrument onboard NASA's Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite is performed. Two types of weighting functions are applied to the K‐Lidar temperature profiles to compare them with EFS temperatures. The first type has a fixed peak altitude and a fixed full width at half maximum (FWHM) for the whole night. In the second type, the peak altitude and FWHM vary with the local time. SABER measurements are utilized to estimate the OH(6,2) band peak altitudes and FWHMs as a function of local time and considerable temporal variations are observed in both the parameters. The average temperature differences between the EFS and K‐Lidar obtained with both types of weighting functions are comparable with previously published results from different latitude‐longitude sectors. We found that the temperature comparison improves when the time‐varying weighting functions are considered. Comparison between EFS, K‐Lidar, and SABER temperatures reveal that on average, SABER temperatures are lower than the other two instruments and K‐Lidar temperatures compare better with SABER in comparison to EFS. Such a detailed study using the AO EFS data has not been carried out previously. Plain Language Summary: In the Earth's atmosphere, a strong band of near‐infrared (NIR) emissions exists at ∼90 km altitude due to the vibrational‐rotational excitation of the Hydroxyl (OH) molecules. The intensity of these OH emissions depends on the temperatures of their rotational states. Under the assumption of thermodynamic equilibrium, the rotational temperatures should represent the background atmospheric temperatures. A scanning spectrometer of the Ebert‐Fastie configuration (EFS) was operated on the nights of February‐April 2005 at Arecibo Observatory (AO), Puerto Rico (18.35°N, 66.75°W) to record the intensity of the OH(6,2) band which is used to estimate rotational temperatures in this work. During the same period, a co‐located Potassium temperature Lidar (K‐Lidar) utilized resonance scattering from the atmospheric K‐atoms to measure temperatures in the 80–100 km altitude range. By nature, the EFS‐derived temperatures are an altitude‐integrated quantity while the K‐Lidar provides altitude‐resolved temperatures. First, we followed a commonly used methodology to compare the EFS and K‐Lidar temperatures by using the SABER data onboard NASA Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. We found good agreement between our results and previously published studies. Then, we propose a modified methodology with the help of SABER data to show that it provides a better comparison between the EFS and K‐Lidar temperatures. Key Points: Good inter‐comparison is obtained between Arecibo Observatory spectrometer and Lidar temperatures, and results are similar to previous studiesThe temperature comparison improves when time‐varying weighting functions are used instead of a single weighting functionRotational temperature estimation from the spectrometer data depends strongly on the choice of Einstein coefficients for the P1 line transitions [ABSTRACT FROM AUTHOR]

Mountains near the equator have very unique temperature regimes due to the tropical latitude and alpine altitude. How climate change will impact these temperature regimes is not clear as there are so few temperature records in these environments. This study attempts to characterize the near‐surface air and ground temperature regime for the Teleki Valley (3,200–4,200 m. a.s.l) on the western slope of Mount Kenya using a set of six temperature loggers placed at 200 m elevation intervals. Temperature was recorded at 30 min intervals from September 2021 to October 2022. The mean diurnal temperature range varied from 6°C to 14°C, with maximum daily swings in excess of 25°C. There was a distinct seasonality in temperature, with a hot season during the months of January‐March, where it was roughly 3°C warmer than the coolest months. Diurnal temperature swings were also highest in March. This seasonality is driven by radiation and moisture: higher radiation during the vernal equinox combined with clear skies during the dry season made for higher temperatures. Elevation was the dominant spatial gradient determining temperature regimes, but rock and vegetation cover played a large role in modifying near‐surface air and ground temperature. The highest elevation logger (4,200 m. a.s.l), measuring ground temperature, showed muted seasonal and diurnal swings, creating for warmer temperatures than lower elevation loggers. This demonstrates the importance of thermal refugia at these high elevations. This also makes predictions of plant and animal responses to climate change difficult, as uniform upward shifts would not necessarily maintain thermal niches. Plain Language Summary: Tropical alpine areas have few detailed records of climate particularly in the zone near the surface where most organisms live. This study investigated the near ground temperature regime of the Teleki Valley of Mount Kenya using loggers placed at 200 m elevation intervals. The results showed that temperature can vary considerably over short distances and over time, with marked daily and seasonal temperature patterns. Elevation, topography and cover drive these differences as do macro‐climactic factors of wind patterns and solar radiation. The implication is that biotic responses to climate change will not be straight‐forward but may be site‐ and species‐specific. Key Points: Near‐surface temperature regime of the alpine zone of Mount Kenya has a seasonal element that is driven in part by solar radiation changesElevation and topography drive spatial differences in temperature in the alpine zoneThere is a diversity of microclimates which may play a crucial role in survival of biotic communities under climate change [ABSTRACT FROM AUTHOR]

Gridded air temperature data are required in various fields such as ecological modeling, weather forecasting, and surface energy balance assessment. In this work, a piecewise multiple linear regression model is used to produce high‐resolution (250 m) daily maximum (Tmax), minimum (Tmin), and mean (Tmean) near‐surface air temperature maps for the State of Hawaiʻi for a 32‐year period (1990–2021). Multiple meteorological and geographical variables such as the elevation, daily rainfall, coastal distance index, leaf area index, albedo, topographic position index, and wind speed are independently tested to determine the most well‐suited predictor variables for optimal model performance. During the mapping process, input data scarcity is addressed first by gap‐filling critical stations at high elevation using a predetermined linear relationship with other strongly‐correlated stations, and second, by supplementing the training dataset with station data from neighboring islands. Despite the numerous covariates physically linked to temperature, the most parsimonious model selection uses elevation as its sole predictor, and the inclusion of the additional variables results in increased cross‐validation errors. The mean absolute error of resultant estimated Tmax and Tmin maps over the Hawaiian Islands from 1990 to 2021 is 1.7°C and 1.3°C, respectively. Corresponding bias values are 0.01°C and −0.13°C, respectively for the same variables. Overall, the results show the proposed methodology can robustly generate daily air temperature maps from point‐scale measurements over complex topography. Plain Language Summary: Uniformly gridded maps of daily air temperature are a valuable resource for scientific research, resource management, and environmental education and awareness. However, there are numerous challenges to producing these maps in the State of Hawai'i, including the steep changes in terrain, and the relatively limited availability of weather data measurements on which the maps are based. To account for these challenges, the daily air temperature maps are modeled based on other variables physically linked to temperature, such as elevation or rainfall, which are used as independent variables to predict the temperature values. After testing numerous combinations of variables, it was found that the simplest model using only elevation as the predictor is the most efficient combination with respect to balancing lower prediction errors with model complexity. Excluding outliers and filling in missing data gaps in the input data is also found to be critical to producing quality temperature maps. This modeling method is used to create a series of daily maximum, minimum, and mean temperature maps from 1990 to 2021, making it the most up‐to‐date record of gridded temperature for Hawai'i. Key Points: Numerous unique challenges exist for estimating gridded air temperature in the State of HawaiiDaily temperature maps from 1990 to 2021 created for State of Hawaii using a piecewise linear modelElevation covariate alone is sufficient to estimate temperature and quality control of input data are critical to the modeling process [ABSTRACT FROM AUTHOR]

The Greenland Ice Sheet is a leading contributor to global sea‐level rise because climate warming has enhanced surface meltwater runoff. Melt rates are particularly sensitive to air temperatures due to feedbacks with albedo. The primary melt‐albedo feedback, fluctuation of seasonal snowlines, however, is determined not only by melt but also by antecedent snowfall which could delay the onset of dark glacier ice exposure. Here we investigate the role of snowfall versus air temperatures on ice sheet melt‐albedo feedbacks using satellite remote sensing and atmospheric reanalysis data. We find several lines of evidence that snowline fluctuations are driven primarily by air temperatures and that snowfall is a secondary control. First, standardized linear regressions indicate that the timing of glacier ice exposure is nearly twice as sensitive to air temperatures than antecedent snowfall. Second, in 74% of the ablation zone by area, winter snowfall rates are not significantly correlated with winter air temperatures. This relationship implies that ice sheet melt due to climate warming is unlikely to be compensated by higher snowfall rates in the ablation zone. Third, we find no significant change in snowfall rates in the ablation zone during our 1981–2021 study period. Our findings demonstrate that snowfall is unlikely to reduce future ice sheet melt and that ice sheet meltwater runoff should be accurately predicted by air temperatures. Although given the importance of melt‐albedo feedbacks, ice sheet models that parameterize albedo or are coupled with regional climate models are likely to provide the most accurate projections of mass loss. Plain Language Summary: The Greenland Ice Sheet is currently losing mass because rates of mass loss (mainly due to surface meltwater runoff and iceberg discharge into the ocean) are higher than rates of mass gain (mainly due to snowfall). As the climate warms, rates of mass loss are expected to increase non‐linearly because rising air temperatures darken the surface, leading to more solar energy absorption, and more melting. The main process responsible for surface darkening is the exposure of dark glacier ice due to Greenland's seasonally evolving snowline. The position of the snowline is determined not only by summer melt but also by the thickness of the snowpack that accumulates during winter. In this study, we investigated whether summer melt or snowpack thickness was more important for determining the timing of glacier ice exposure using satellite remote sensing and climate model outputs. We found that the glacier ice exposure is much more sensitive to air temperatures than snowfall and that snowfall did not change in the ablation zone during the 1981–2021 study period. Our findings imply that snowfall is unlikely to reduce ice sheet mass loss and that meltwater runoff from the ice sheet should be accurately predicted by air temperatures. Key Points: We investigate the extent to which antecedent snowfall versus summer air temperatures controls melt‐albedo feedbacksWe find that melt‐albedo feedbacks are primarily driven by air temperatures and that antecedent snowfall is of secondary importanceWe find little evidence that snowfall will compensate enhanced ice sheet melt due to climate warming [ABSTRACT FROM AUTHOR]

Accurate estimation of near‐surface air temperature (TsurfAir) at fine spatiotemporal resolution is crucial for environmental applications, data assimilation into land surface models, and climate change studies. The Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite measures TsurfAir twice a day at the global scale. In this study, the AIRS TsurfAir is validated using ground‐based weather stations in North America. Owing to the high density of weather stations, spatial heterogeneity (<1°) of TsurfAir is modeled using semi‐variograms and further used to calculate the validation reference via ordinary kriging at AIRS retrieval footprints. This analysis intends to reveal error characteristics in cold and hot seasons by selecting six months of data in January and July of three years, along with a case study on two extreme events. Furthermore, MODIS‐enabled land surface temperature and land cover are used to attribute the observed error in TsurfAir. Results show an overall warm bias of 0.11 K from AIRS TsurfAir and conditional bias over‐ and underestimating cold and hot temperatures, respectively. Spatial heterogeneity of TsurfAir is found to influence the variance of AIRS TsurfAir error. As a possible source of TsurfAir error, land surface temperatures (Tskin) from AIRS and MODIS are compared. Close match between the two Tskin products indicates a lesser influence of the Tskin difference on TsurfAir error. Overall, the ground validation of AIRS TsurfAir along with the error attribution can facilitate post‐processing of AIRS products and uncertainty analysis for the wide range of applications of AIRS TsurfAir. Key Points: Near‐surface air temperature retrievals from Atmospheric Infrared Sounder are validated using ground truth from a dense network of weather stations in North AmericaHigh density of ground truth reveals fine‐scale heterogeneity and representativeness error of TsurfAir via ordinary KrigingMODIS‐based land surface temperatures and land cover types are used to attribute errors in TsurfAir [ABSTRACT FROM AUTHOR]

The Community Long‐term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) spans more than two decades of soundings from infrared and microwave instrument pairs on Aqua, Suomi‐NPP, and NOAA‐20. CLIMCAPS builds on three decades of research within the National Aeronautics and Space Administration Atmospheric Infrared Sounder Science Team (AST) to retrieve profiles of atmospheric temperature, water vapor, ozone, and many other trace gases as well as cloud and surface properties. In this paper, we highlight four innovative aspects of the AST retrieval methodology that allows the CLIMCAPS record (2002 to present) to bridge technology differences, maintain global coverage and stay updated with fast reprocessing times. CLIMCAPS improves on the AST method with rigorous optimal estimation uncertainty quantification for improved understanding of the signal‐to‐noise in sounding retrievals to allow their use in scientific research. Plain Language Summary: The Atmospheric Infrared Sounder (AIRS) was the first instrument of its kind in space. It has been making measurements daily at ∼1:30 a.m./p.m. since 2002. The Community Long‐term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) uses AIRS measurements to retrieve variables that characterize the thermodynamic and chemical state of the atmosphere. National Aeronautics and Space Administration is funding CLIMCAPS to continue the AIRS record with retrievals from the Cross‐track Infrared Sounder launched on three satellites in similar orbits since 2014 and scheduled for launch on two additional satellites in future. Accurate, consistent, long‐term records of observations are critically important for our understanding of an evolving Earth system. Key Points: Community Long‐term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) extends the Atmospheric Infrared Sounder (AIRS) + Atmospheric Microwave Sounding Unit record with sounding retrievals from Cross‐track Infrared Sounder + Advanced Technology Microwave SounderKey aspects of the AIRS Science Team retrieval methodology are embedded in the CLIMCAPS algorithm to help bridge technology differencesThe CLIMCAPS algorithm design enables an atmospheric sounding product with global coverage in clear and partly cloudy conditions [ABSTRACT FROM AUTHOR]

We report the development of a temperature profile data set from the Ozone Mapping and Profiler Suite Limb Profiler (OMPS‐LP) instrument on the Suomi NPP satellite. The data set covers a roughly 10 year period from 2012 to 2022, and temperatures are provided in the altitude range between 35 and 70 km. The algorithm uses daytime Rayleigh scattered radiation near 350 nm to estimate atmospheric density profiles, which are vertically integrated using the hydrostatic equation to estimate atmospheric pressure. Temperature profiles are then derived using the ideal gas law. Spectral structures in the OMPS‐LP radiances are a source of systematic errors that limit the absolute accuracy to ±2 K. However, since the systematic errors do not change significantly over time, the relative accuracy is better than ±1 K. Our temperature data set has been designed to supplement global temperature maps produced by assimilation of data from traditional meteorological sensors, including GNSS radio occultation sensors, that provide high quality atmospheric temperature profiles up to 40–55 km. We show comparisons to other co‐located temperature datasets to validate our product. Key Points: A retrieval algorithm based on daytime Rayleigh scattered radiation near 350 nm is proposedThe algorithm retrieves temperature profile from Ozone Mapping and Profiler Suite Limb Profiler in the 35–70 km altitude rangeWe compare the new temperature profiles to other measurements over the same region [ABSTRACT FROM AUTHOR]

Atmosphere and surface properties are routinely retrieved from satellite measurements and extensively used in weather forecast and climate analysis. Satellite missions dedicated to fill the gap of far‐infrared (far‐IR) observations are scheduled to be launched this decade. To explore mid‐infrared (mid‐IR) and far‐IR joint retrievals for the future far‐IR satellite missions, this study uses an optimal‐estimation‐based method to retrieve atmospheric specific humidity and temperature profiles, surface skin temperature, and surface spectral emissivity from the Infrared Interferometer Sounder‐D (IRIS‐D) measurements in 1970, the only existing spaceborne far‐IR spectral observations with global coverage. Based on a set of criteria, two cases in the Arctic, which are most likely under clear‐sky conditions, are chosen for the retrieval experiments. Information content analysis suggests that the retrieved surface skin temperature and the mid‐IR surface spectral emissivity are highly sensitive to the true values while the retrieval estimates of far‐IR surface emissivity are subject to the variation of water vapor abundance. Results show that radiances based on the retrieved state variables are more consistent with the IRIS‐D observations compared to those based on the reanalysis data. Retrieval estimates of the state variables along with retrieval uncertainties generally fall within reasonable ranges. The relative uncertainties of retrieved state variables decrease compared to the a priori relative uncertainties. In addition, the necessity to retrieve surface emissivity is corroborated by a parallel retrieval experiment assuming a blackbody surface emissivity that has revealed significant distortions of retrieved specific humidity and temperature profiles in the Arctic lower troposphere. Key Points: Atmospheric profiles and surface properties are simultaneously retrieved from satellite observations made 50 years agoCompared to reanalysis data, the retrieval estimates produce radiances which are more consistent with the observationsRetrievals of humidity and temperature profiles in the lower troposphere can be considerably affected by the surface spectral emissivity [ABSTRACT FROM AUTHOR]

Siliceous spicules are micro‐laminated geyserite deposits, or micro‐stromatolites, found around high temperature hot‐springs and geysers, associated with intermittent splashes of water. Understanding conditions of formation of these structures will give insights of critical parameters involved in prebiotic environments and early life. We provide a new data set around two geysers from the El Tatio hydrothermal field, Atacama Desert, Chile, a modern analogue for habitable environments on early Earth and Mars. Small spicules (<5 mm diameter) dominate in the near vent areas, whereas larger spicules (>5 mm diameter) form complex columnar structures in the distal vent (>0.3 m distance from the vent). Hydrodynamics, eruption periodicity, and environmental conditions modulate the temperature and cooling patterns around vents. During geyser eruptions, the temperature of water reaching the ground decreases with the distance from the vent. Cooling is stronger during the evenings, due to strong winds and cold air temperature. Rapid water cooling drives supersaturation of amorphous silica, and therefore precipitation rates increase significantly with distance from the vent. Cooling also controls changes in the bacterial communities, from thin filaments <1 μm diameter predominating closer to the vent, to 1–2 μm diameter filaments in the distal vent. Changes in the silica precipitation rate and biota may control the growth of spicules and the transition to columns. Estimated precipitation rates of silica provide a first approximation of the longevity of these structures and the timing for the formation of internal laminations. Microlamination will form in year time‐scales closer to the vent, and weeks farther from the vent. Key Points: Precipitation rates of silica around the geyser increase with the distance from the ventStrong daily changes in air temperature and wind speed modulate the precipitation rates of silica at El TatioSpicule size and internal lamination increase farther away from the vent due to change in cooling and precipitation rates [ABSTRACT FROM AUTHOR]

Day-to-day (DTD) temperature variability is an important characteristic of air temperature and is closely related to the economy, ecology, and human health. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, the air temperature has increased particularly during the most recent decades. However, the temporal and spatial variations of DTD temperature variability have not been fully addressed. In this study, the DTD temperature variability is calculated using observed air temperature from meteorological stations in China. The regional and seasonal performances are evaluated among different climate regions and half-year periods. In addition, the relationship between DTD temperature variability and the Arctic Oscillation (AO) is evaluated. The national mean DTD temperature variability in 1961--2020 during the warming (cooling) half-year was 1.8°C (1.5°C), with minimum (maximum) values occurring in the southwestern (northeastern) regions of mainland China. Negative correlation was found between DTD temperature variability and the AO, particularly during the warming half-year in the northern region. The national mean trends of DTD temperature variability were -0.003°C and -0.007°C per decade during warming and cooling half-years, respectively, indicating that the inter-day variation is decreasing under the background of global warming, which may be ecologically beneficial. These results could be of great significance in understanding temperature change and can be used to quantify land--atmosphere interaction and climate change. [ABSTRACT FROM AUTHOR]

In this study, a new lightning data assimilation (LDA) scheme using Geostationary Lightning Mapper (GLM) flash extent density (FED) is developed and implemented in the National Severe Storms Laboratory Warn-on-Forecast System (WoFS). The new LDA scheme first assigns a pseudo relative humidity between the cloud base and a specific layer based on the FED value. Then at each model layer, the pseudo relative humidity is converted to pseudo dewpoint temperature according to the corresponding air temperature. Some sensitivity experiments are performed to investigate how to assign and use GLM/FED in an optimum way. The impact of assimilating this pseudo dewpoint temperature on a short-term severe weather forecast is preliminarily assessed in this proof-of-concept study. A high-impact weather event in Kansas on 24 May 2021 is used to evaluate the performance of the new scheme on analyses and subsequent short-term forecasts. The results show that the assimilation of additional FED-based dewpoint temperature observations along with radar, satellite radiance, and cloud water can improve short-term (3-hr) forecast skill in terms of quantitative and qualitative verifications against the observations. The improvement is primarily due to the direct and indirect adjustment of dynamic and thermodynamic conditions through the LDA process. More specifically, the assimilation of FED-based dewpoint temperature, in addition to the other observations currently used in WoFS, tends to enhance the ingredients required for thunderstorm formation, namely moisture, instability, and lifting mechanism. [ABSTRACT FROM AUTHOR]

Long‐term variations in maximum gust speeds have been analyzed using wind measurements recorded at St Mary's, Isles of Scilly, UK, and possible drivers of recent changes have been investigated. A rigorous homogenization procedure has been applied to the wind records to remove discontinuities mainly caused by non‐climatic changes. Trends in magnitude of the homogenized annual and monthly maximum gust speeds have been subsequently estimated. A significant (p < 0.10) decreasing trend of −0.102 m s−1/10 years is exhibited by the annual maximum gust speeds over the period 1928–2011, while a non‐significant (p > 0.10) positive trend characterizes the entire period 1928–2020 of wind measurements. A turning point has been detected in the monthly maximum gust speed series in 2012, followed by a trend reversal. Distinctive differences in the gust trend pattern have been observed among the seasons, with increasing trend in summer, and winter gust speeds exhibiting decline in 1969–2011 and strengthening since 2012. Variations in the monthly maximum gust speeds appear closely related to changes in air temperature and latitudinal pressure gradients. Furthermore, the monthly maximum gust speeds show significant correlation with the main oceanic‐atmospheric circulation patterns in the North Atlantic region. A synergistic effect among the phases of the dominant patterns of climate variability appears to explain the observed decline and recovery trends in winter gusts. Summer gust speeds show a positive correlation with the global land‐ocean temperature deviations from the corresponding 1951–1980 base period mean. Key Points: Homogenization of historical gust speeds at St Mary's, Isles of Scilly, has been performed to remove discontinuities caused by non‐climatic changesSummer gust speeds have significantly increased, while winter gust speeds have declined in 1969–2011 and strengthened since 2012Statistically significant correlations are found between monthly maximum gust speeds and main climate indices which might explain the observed variability [ABSTRACT FROM AUTHOR]

We present a near surface air temperature (NSAT) fused data product over the contiguous United States using Level 2 data from the Atmospheric Infrared Sounder, on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar‐orbiting Partnership satellite. We create the fused product using Spatial Statistical Data Fusion, a procedure for fusing multiple data sets by modeling spatial dependence in the data, along with ground station data from NOAA's Integrated Surface Database (ISD) which is used to estimate bias and variance in the input satellite data sets. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We provide detailed validation using withheld ISD data and comparison with ERA5‐Land reanalysis. The fused gridded product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. Over the domain of our study, the fused product decreases daytime bias magnitude by 1.7 and 0.5 K, nighttime bias magnitude by 1.5 and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input data sets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple data sets estimating the same quantity. Finally, because our product reduces bias, it produces long‐term data sets across multi‐instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end. Plain Language Summary: We have used a data fusion technique called spatial statistical data fusion (SSDF) to create an improved near surface air temperature (NSAT) data set by fusing two separate satellite data sets. NSAT is important for a variety of applications, such as drought, wildfire, and extreme heat research and prediction. The two input NSAT data sets come from the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding suite on the Suomi National Polar‐orbiting Partnership satellite. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We also performed a detailed validation using withheld reference data (which was not included in the bias‐correction data) and comparison with ERA5‐Land reanalysis. The new fused product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. SSDF is computationally fast and generalizable, capable of data fusion from multiple data sets so long as they estimate the same quantity. Finally, because our product reduces bias, it provides a means of creating high‐quality continuous long‐term data sets across the years, as individual satellite missions and their data records begin and end. Key Points: We demonstrate spatial statistical fusion for Level 2 remote sensing data sets which estimate the same observableWe introduce a new daily and nightly fused near‐surface air temperature product from satellite hyperspectral sounders over contiguous United StatesThe fused product decreases bias and RMSE by 1 K and 25% respectively relative to input data sets, averaged over the domain of the study [ABSTRACT FROM AUTHOR]

Long‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured. Plain Language Summary: Long‐term changes in surface temperature due to climate change are usually studied using near‐surface air temperatures (T2m) from weather stations. However, these data are not available everywhere and changes in T2m are poorly understood in some regions (e.g., parts of Africa). This study assesses the potential for satellite‐observed land surface temperatures (LST) to help fill these gaps. The study finds that LST and T2m provide very similar information on surface temperatures and therefore LST can provide useful information where T2m data are unavailable. However, some of the LST data sets are found to be "unstable" and suffer from jumps and changes over time that result from for example, changes in satellite instrument or a change in the orbit of the satellite platform. These unstable LST data sets cannot be used to measure changes in surface temperature. However, the study finds that for "stable" LST data sets, which do not suffer from these non‐climatic jumps and changes, the changes in LST over time are very similar to, or even the same as, the changes in T2m. Therefore, this work suggests that satellite LST data could be used to measure changes in surface temperature where there are not enough stations to make these measurements using T2m. Key Points: The stability of six satellite land surface temperature data sets is assessed using homogenized station air temperatures over EuropeOnly two data sets are stable; the other four show non‐climatic discontinuities associated with a change in sensor and/or drift over timeSignificant trends in land surface temperature of ∼0.65 K/decade over Europe are found for one of the stable data sets over 2002–2018 [ABSTRACT FROM AUTHOR]

This study investigated the impacts of horizontal resolution of lateral boundary condition (LBC) and downscaling method on the performance of RegCM4.6 in simulating the 2 m air temperature and precipitation over central eastern China. Two groups of experiments (direct downscaling experiments using LBC derived from the same reanalysis data but with different horizontal resolutions [0.75°, 1.5°, and 2.5°] and double‐nested downscaling experiment) with the model horizontal resolution of 20 km over central eastern China have been carried out. Results show that the direct downscaling experiments consistently outperform the double‐nested experiment to simulate the 2 m air temperature and precipitation over central eastern China, suggesting that adopting the direct downscaling method is appropriate to simulate the surface climate over central eastern China. Meanwhile, the direct downscaling experiments driven by the LBC derived from the reanalysis data with the horizontal resolution ranging from 0.75° to 2.5° display very comparable performance with a little better performance by adopting the LBC derived from the reanalysis data of much finer resolution. Further mechanism analysis indicates that much larger errors of LBC produced by the double‐nested downscaling method lead to the positive pressure biases and negative biases of water vapor convergence flux over Jianghuai Valley, which contribute to the much larger biases of precipitation and 2 m air temperature in the double‐nested downscaling experiment compared to the direct downscaling experiments. This study emphasizes that the direct downscaling method is superior to the double‐nested downscaling method for the simulations of 2 m air temperature and precipitation over central eastern China. Key Points: Direct downscaling method is more suitable to simulate the surface climate over central eastern China than double‐nested approachLimited impact of the horizontal resolution of reanalysis data for lateral boundary condition generation on the model performance by adopting direct downscaling [ABSTRACT FROM AUTHOR]

Radio occultation (RO) measurement is an established technique for profiling planetary atmosphere/ionosphere. The accuracy of the derived physical parameters (atmospheric temperature, pressure, and density in the lower atmosphere, and electron density in the ionosphere) however depends on uncertainty in the observed frequency and robustness of the retrieval algorithm. The objective of this paper is to identify all possible sources of errors in RO experiments and their propagation to the retrieved atmospheric profiles. Quantification of errors due to assumptions used in the retrieval algorithm has also been attempted. We used the data from one‐way downlink single‐frequency (X‐band) radio signals, transmitted from the radio science payload onboard Akatsuki spacecraft orbiting Venus and received at Indian Deep Space Network, Bengaluru, India for this study. We followed the standard signal processing technique to analyze the raw data. Atmospheric profiles were retrieved using the basic concept of geometrical optics. Possible sources of errors in RO experiments are identified and their magnitude range has been quantified. The propagation of errors in obtained atmospheric profiles is also discussed. These results would be useful while devising RO experiments for future planetary explorations. Key Points: Identification of error sources in the planetary radio occultation experimentsQuantification of errors in the Doppler residualEstimation of propagation of errors in the retrieved atmospheric profiles [ABSTRACT FROM AUTHOR]

Climate reconstructions by data assimilation need to accommodate for sensitivities to proxies and prior estimates because models are uncertain and proxies are spatiotemporally limited. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (i.e., corals, ice cores, and tree‐ring cellulose). Experiments were conducted using an offline data assimilation approach; the results showed annual variations in the global distribution of surface air temperature and precipitation amount from 850 to 2000. Standard deviations of surface air temperature and precipitation amount during the entire period differed by up to 36% due to prior estimates. Experiments with different types of proxies showed that the El Niño‐like distribution of positive anomalies in the central to eastern tropical Pacific may only be adequately reproduced in experiments with corals, and not experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55‐year Reanalysis (JRA‐55) was 0.81 at maximum. By contrast, the correlation coefficient between experiments without corals and JRA‐55 was a maximum of 0.19. Key Points: Climate reconstruction by data assimilation illustrates annual variations in surface air temperature and precipitation from 850 to 2,000Spatiotemporal differences in climate reconstruction were compared based on prior estimatesThe impact of proxies on the results of past reproduced El Niño cases from 1971 to 2000 was validated [ABSTRACT FROM AUTHOR]

A knowledge of melting layer (ML) is crucial for the accurate estimation of rainfall and the study of microphysical characterization of the cloud. Based on 5 years (2016–2020) of measurements of cyclones over the Western North Pacific from the dual‐frequency precipitation radar on board the Global Precipitation Measurement satellite, the characteristics and microphysical processes of ML are investigated. Our research shows that the height of the top boundary of ML is higher than the freezing level (0 degrees centigrade level, FL), and the Ze of freezing level is larger than that of the top of ML. The height of ML is the result of the combined effect of latitude and stage of cyclone. When the ice particles pass through the ML, the mass‐weighted mean diameter (Dm), the generalized intercept parameter (Nw), and the distributions of the reflectivity factor (Ze) increase, indicating that growth mechanism and breakup process. Key Points: The height of melting layer in the cyclons region is higher than the 0°C isotherm heightThe melting layer in the cyclons region is different in different stages and latitudeThe microphyscics process in near the melting layer [ABSTRACT FROM AUTHOR]

A priori, cosmic‐ray measurements offer a unique capability to determine the vertical profile of atmospheric temperatures directly from ground. However, despite the increased understanding of the impact of the atmosphere on cosmic‐ray rates, attempts to explore the technological potential of the latter for atmospheric physics remain very limited. In this paper, we examine the intrinsic limits of the process of cosmic‐ray data inversion for atmospheric temperature retrieval, by combining a detection station at ground with another one placed at an optimal depth, and making full use of the angular information. With that aim, the temperature‐induced variations in cosmic rays (c.r.) rates have been simulated resorting to the theoretical temperature coefficients WT(h, θ, Eth) and the temperature profiles obtained from the ERA5 atmospheric reanalysis. Muon absorption and Poisson statistics have been included to increase realism. The resulting c.r. sample has been used as input for the inverse problem and the obtained temperatures compared to the input temperature data. Relative to early simulation works, performed without using angular information and relying on underground temperature coefficients from a suboptimal depth, our analysis shows a strong improvement in temperature predictability for all atmospheric layers up to 50 hPa, nearing a factor 2 error reduction. Furthermore, the temperature predictability on 6‐h intervals stays well within the range 0.8–2.2 K. Most remarkably, we show that it can be achieved with small‐area m2‐scale muon hodoscopes, amenable nowadays to a large variety of technologies. For mid‐latitude locations, the optimal depth of the underground station is around 20 m. Plain Language Summary: Cosmic rays (c.r.) are a form of natural radiation that comes from outer space and traverses the atmosphere. Analogously to X‐ray radiation, we can extract information from the object they pass through, the atmosphere in this case. Cosmic radiation can be measured at the Earth's surface using sophisticated instruments. In our work, we examine in detail the possibility of retrieving the vertical profile of temperatures from c.r. measurements, by using two detection stations, one placed at the surface and another one at an optimal depth. We make full use of a feature which is characteristic of modern c.r. detectors, the angular information. This refers to the capability of measuring c.r. from different directions. To analyze the limits of the suggested approach, we estimate temperatures from simulated c.r. data that would be measured under realistic atmospheric conditions and compare them with the original ones. Relative to early simulation works, our method estimates temperatures with a greater accuracy at higher temporal resolutions and for atmospheric layers up to 20 km. Most remarkably, we show that this performance can be achieved with small and affordable detectors, bringing the possibility of complementing satellite‐borne temperature retrieval with a technology cheaper to assemble and maintain. Key Points: Improved approach to obtain atmospheric temperature from cosmic‐ray data by introducing a pair of detection stations and angular informationResults show a significant retrieval improvement over earlier methods with high accuracy up to 20 km of heightThe use of affordable and small‐scale cosmic‐ray telescopes is suggested as a possible complement to satellite temperature measurements [ABSTRACT FROM AUTHOR]

High‐latitude lakes are warming faster than the global average with deep implications for life on Earth. Using an approximately six‐decade long in situ data set, we explored the changes in lake surface‐water temperature (LST), lake deep‐water temperature (LDT), lake depth‐weighted mean water temperature (LDMT), and ice‐free days in Lake Kallavesi, a boreal lake in central Finland, when the lake was stratified (June–August). Our results suggest that the LST is warming faster than the local air temperature (AT). As for the LST, fast warming was also observed in the LDT and LDMT, but at rates slower than those in the LST. The number of ice‐free days also shows an upward trend, with a rate of about 7 days per decade during the study period. The corresponding local AT is the main driver of the LST, followed by the ice‐free days and annual mean AT. Air temperature and ice‐free days also mainly contribute to the changes in the LDMT. The LDT is affected more by the North Atlantic Oscillation signals in this freshwater lake. The AT in the prior months does not affect the LDT in Lake Kallavesi although the AT during the prior season, that is, spring, is the main driver of summer LDT. This highlights the local AT impact on the LDT at time scales longer than a month. The warming rates in the lake water are at a minimum in June because the lake is not yet strongly stratified in this month when compared to July and August. These findings improve our knowledge of long‐term changes in the lake water temperature in a high‐latitude lake, a region with severe environmental consequences due to fast changes in the AT and lake ice phenology. Key Points: Our study presents the results from six decades of water temperature measurements in the surface and bottom layers of a boreal lakeUsing Mann‐Kendall and Theil‐Sen estimator methods we found strong warming rates in both surface and bottom layers of the lakeThe strong warming rate in the lake bottom temperature is not in agreement with null trend reported for deepwater temperature at global scale [ABSTRACT FROM AUTHOR]

GLDAS2.0 provides long‐term fine resolution gridded hydrometeorological data sets, which are necessary for water‐related studies, particularly in some transboundary rivers that are partially without observation. Yet, GLDAS2.0 has only been validated at limited locations, and few studies have been conducted to develop approaches to correct the GLDAS2.0 data for transboundary rivers. This work assessed the GLDAS2.0 data and developed approaches to correct their uncertainties for studies in large transboundary rivers in the Tibetan Plateau and Northeast China (NC). To achieve these goals, observational data from 1982 to 2010 and a water and energy budget‐based distributed hydrological model including biosphere after calibration and validation were employed. We find that the GLDAS2.0 data (except for wind speed) can reasonably replicate observed seasonal variations. However, its specific humidity and wind speed have large uncertainty, and precipitation has large uncertainty in summer. In NC, the trends of its precipitation, air temperature, downward longwave radiation, and wind speed are consistent with the observations. In the Yarlung Tsangpo, Lancang, and Nu Rivers, the trends of all GLDAS2.0 data reproduce the observation very well, that is, wetting, warming, and dimming trends. Validations show that the corrections are effective and the corrected forcing data can be successfully used in hydrological simulation with improved performance than the raw GLDAS2.0 data, which demonstrates the usefulness of the methodology and corrected forcing data to hydrometeorological studies in transboundary rivers in China as well as in other nearby regions/countries. Plain Language Summary: GLDAS2.0 provides long‐term hydrometeorological data sets, which are necessary for water‐related studies, particularly in some transboundary rivers that are partially without observation. This work assessed GLDAS2.0 data and developed approaches to correct their uncertainties for studies in large transboundary rivers in the Tibetan Plateau (TP) and Northeast China (NC). In NC, the trends of GLDAS2.0 precipitation, air temperature, downward longwave radiation, and wind speed are consistent with observations. In TP, the trends of all the GLDAS2.0 data reproduce observation very well. Validations show that the corrections are effective and the corrected data have improved performance than raw GLDAS2.0 data in hydrological simulation. Key Points: Specific humidity and wind speed have large uncertainty, and precipitation has large uncertainty in summerTrends of precipitation, air temperature, downward longwave radiation, and wind speed are consistent with observationEquations are developed to reduce forcing data uncertainty, and validations show that the equations are effective [ABSTRACT FROM AUTHOR]

Water surface‐atmosphere interactions play an important role in the climate system on regional and even global scale. Yet, it remains unclear whether radiative or aerodynamic forces contribute more to water surface evaporation, which may lead to different strategies in model parameterization. This study compared the eddy covariance observations taken at two contrasting lakes in China: Poyang Lake (subtropical, plain, and warm) in 2018 and Yamzhog Yum Co (alpine, plateau, and cold) in 2016. Both lakes of similar latitude have similar solar radiation, and Yamzhog Yum Co has a lower atmospheric pressure and air density and a larger temperature difference between water surface and air. Poyang Lake had a total evaporation of 741 mm from 6 May to 31 December 2018, relatively higher than that of Yamzhog Yum Co (677 mm) for the same season in 2016. Evaporation peaked in August for Poyang Lake but in October for Yamzhog Yum Co. The Penman equation disclosed that radiative forces contributed more than aerodynamic forces in both lakes. Evaporation was found to be most sensitive to air temperature in Poyang Lake but to surface net radiation in Yamzhog Yum Co, attributable to their sensitivity differences in iso‐radiative and isothermal conditions. Higher elevation reduced the aerodynamic contribution with lower air temperature and atmospheric pressure in Yamzhog Yum Co. The findings should be useful for understanding different behaviors of lake evaporation under common mechanisms. Key Points: Higher evaporation in Poyang Lake (plain, subtropical, and warm) than Yamzhog Yum Co (plateau, alpine, and cold) lags behind surface net radiationAerodynamic contribution to evaporation is lower than radiative one, and less in Yamzhog Yum Co (YL) due to low air temperature and pressureEvaporation is most sensitive to air temperature in Poyang Lake, different from YL in their iso‐radiative and isothermal sensitivity [ABSTRACT FROM AUTHOR]

This study presents a comprehensive evaluation of the mean and extreme surface air temperature and precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel ensemble simulations over the Tibetan Plateau. Simulations from 29 climate models are compared against gridded observations for the period 1961–2012. Results show that models reasonably reproduce the overall spatial patterns of the 1961–2012 averages of seasonal mean temperature and precipitation. Nevertheless, models tend to underestimate mean temperature and overestimate precipitation accumulations. To be specific, the multimodel mean estimates of seasonal average temperatures over the plateau are 0.5°C–2.6°C colder than observed, while the corresponding estimates of seasonal precipitation accumulations are 218% (spring: March–May), 76% (summer: June–August), 129% (autumn: September–November), and 533% (winter: December–February) of those observed. As in observations, models also well capture increasing trend of mean temperature and precipitation in all seasons but underestimate the rates of increasing trends for both variables in all seasons. Models' ability to simulate temperature and precipitation extremes is also evaluated in terms of a set of chosen extreme indices defined by the Expert Team on Climate Change Detection and Indices. On average, models tend to underestimate the averages of annual maximum daily maximum temperature, annual minimum daily minimum temperature, frost days, warm nights, and consecutive dry days but overestimate the corresponding averages of annual maximum 5‐day precipitation accumulations and simply daily intensity. Generally, models are able to simulate the signs of the trends in extreme indices but underestimate their magnitudes and misrepresent spatial patterns. Key Points: CMIP6 models reasonably reproduce the spatial patterns and temporal variations of mean and extreme temperature and precipitationCMIP6 models continue to suffer from cold bias in temperature and wet bias in precipitation similar as its predecessor CMIP5Most models underestimate the observed trends in mean and extreme temperature and precipitation [ABSTRACT FROM AUTHOR]

Change in global mean surface temperature (ΔGMST), based on a blend of land air and ocean water temperatures, is a widely cited climate change indicator that informs the Paris Agreement goal to limit global warming since preindustrial to "well below" 2°C. Assessment of current ΔGMST enables determination of remaining target‐consistent warming and therefore a relevant remaining carbon budget. In recent IPCC reports, ΔGMST was estimated via linear regression or differences between decade‐plus period means. We propose nonlinear continuous local regression (LOESS) using ±20 year windows to derive ΔGMST across all periods of interest. Using the three observational GMST data sets with almost complete interpolated spatial coverage since the 1950s, we evaluate 1850–1900 to 2019 ΔGMST as 1.14°C with a likely (17%–83%) range of 1.05°C–1.25°C, based on combined statistical and observational uncertainty, compared with linear regression of 1.05°C over 1880–2019. Performance tests in observational data sets and two model large ensembles demonstrate that LOESS, like period mean differences, is unbiased. However, LOESS also provides a statistical uncertainty estimate and gives warming through 2019, rather than the 1850–1900 to 2010–2019 period mean difference centered at the end of 2014. We derive historical global near‐surface air temperature change (ΔGSAT), using a subset of CMIP6 climate models to estimate the adjustment required to account for the difference between ocean water and ocean air temperatures. We find ΔGSAT of 1.21°C (1.11°C–1.32°C) and calculate remaining carbon budgets. We argue that continuous nonlinear trend estimation offers substantial advantages for assessment of long‐term observational ΔGMST. Key Points: Continuous local regression is an alternative to traditional IPCC temperature change estimation methodsGlobal warming, estimated from combined land and sea‐surface temperature observational series with enhanced surface coverage, reached 1.14°C in 2019 relative to 1850–1900 (likely range 1.05°C–1.25°C)Global surface air temperature anomalies reached 1.21°C in 2019 relative to 1850–1900 (1.11°C–1.32°C), implying a remaining carbon budget of ∼220 GtCO2 to limit warming to 1.5°C [ABSTRACT FROM AUTHOR]

Reconstructing past climates remains a difficult task because pre‐instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi‐model ensembles produce lower error than reconstructions from single‐model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid‐ and high‐latitude ocean and sea‐ice regions. Furthermore, we find that multi‐model ensemble reconstructions outperform single‐model reconstructions that use covariance localization. We propose that multi‐model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables. Plain Language Summary: Understanding past climate variability is important for contextualizing climate change as well as for testing the ability of climate models to simulate the climate system before global warming. However, reconstructing past climate variability remains a complex task because pre‐instrumental paleoclimate proxy records, such as tree rings, corals, ice cores, and sediment cores, are geographically sparse and are not perfect recorders of climate information. Exactly how to extrapolate information from paleoclimate proxies to other locations and climate variables remains an outstanding issue. Traditionally, information about how one location varies with another location or variable (covariance) is derived from one climate model or from one instrumental data source. Here we find that reconstructions using covariance estimated from combinations of multiple climate models produce less error than reconstructions that use just one climate model. Key Points: Ensembles drawn from single climate models and combinations of multiple models are used to reconstruct surface air temperature variabilityReconstructions from multi‐model ensembles produce lower error than reconstructions from single‐model ensemblesReconstructions from multi‐model ensembles show the largest decreases in error in regions with few observations such as high‐latitude oceans [ABSTRACT FROM AUTHOR]

Surface air temperatures affect a diverse set of physical and biological systems in many parts of the world. For regional‐scale studies, gridded surface air temperature data sets are frequently used as input variables. Here we evaluate 10 commonly used gridded air temperature products with spatial resolutions ranging from 0.1° × 0.1° to 5.0° × 5.0°, relative to observations from in situ weather stations on the Tibetan Plateau. Gridded temperatures are consistently lower, with mean annual air temperature biases ranging from −4.68 °C to −1.72 °C, and root mean square error (RMSE) from 3.24 °C to 6.11 °C. The mean biases of mean seasonal air temperatures for spring, summer, autumn, and winter are −3.21 °C, −2.98 °C, −2.89 °C, and −2.98 °C, respectively, and RMSEs of 4.81 °C, 4.39 °C, 4.3 °C, and 4.7 °C. Of all 10 products, the China meteorological forcing data set compares best with observations. Annual and season temperatures all increase significantly during 1980–2018 on the Tibetan Plateau, with winter dominating the warming rate. Key Points: Out of 10 assessed gridded products, the China meteorological forcing data set compares best with temperature observationsTibetan Plateau temperatures increase significantly during 1980–2018, with winter dominating the warming rateThe limitations related to the irregular spatial distribution of in‐situ sites in this complex terrain, especially elevation biases result in uncertainties. [ABSTRACT FROM AUTHOR]

In the upper troposphere (UT), Global Navigation Satellite System (GNSS) Radio Occultations (ROs) provide accurate air temperatures (<0.5 K) every ∼200 m vertically. We use RO observations from 01/2002 until 12/2018 at 300, 250, and 200 hPa to quantify the tropical upper tropospheric amplification (defined as the ratio of temperature trends in the UT relative to the surface). We compare the RO‐derived results against Atmospheric Infrared Sounder (AIRS), Coupled Model Intercomparison Project Phase 6 (CMIP6) Atmospheric Model Intercomparison Project (AMIP) models, Modern‐Era Retrospective Analysis for Research and Applications version 2 (MERRA‐2), and European Center for Medium‐range Weather Forecasts Re‐Analysis Interim (ERA‐Interim) data. We find that CMIP6 AMIP models show excellent agreement with independent AIRS and RO observations on the magnitude of the UT warming and show warming that is significantly faster than both reanalyses above 250 hPa. Additionally, AIRS and CMIP6 AMIP present excellent agreement with the RO‐measured tropical tropospheric amplification. Plain Language Summary: In the upper troposphere, Global Navigation Satellite System Radio Occultations (GNSS‐RO) measure the air's refractive index, which depends primarily on the air's temperature. We use these GNSS‐RO temperatures from 01/2002 until 12/2018 at 200, 250, and 300 hPa in the tropics (where the atmosphere experiences the largest amplification in response to surface warming). Routinely, climate models are used to simulate how much the upper atmosphere has warmed in response to surface warming. However, there appears to be a disagreement between observations and models, which today we try to understand. The GNSS‐RO observations have a unique feature of being able to measure the air temperature every 200 m, and show us that that climate models are in close agreement with the GNSS‐RO and other infrared observations. In other words, GNSS‐RO observations show that the upper atmosphere warms faster and the amplification due to surface warming is stronger than what current reanalysis estimate. Independent infrared observations of the Earth's upper troposphere also support the GNSS‐RO findings and are also in very close agreement with the climate models. Interestingly, the reanalysis indicates that at 200 hPa there is no warming observed, contrary to what GNSS‐RO, infrared, and climate models reveal. Key points: Coupled model intercomparison project phase 6 (cmip6) atmospheric model intercomparison project (AMIP) models agree with independent radio occultation and infrared data on the magnitude of the tropical upper tropospheric temperature trendsRadio occultations reveal faster tropical upper tropospheric warming than both modern‐era retrospective analysis for research and applications version 2 (MERRA‐2) and European Center for Medium‐Range Weather Forecasts Re‐Analysis Interim (ERA‐Interim) (above 250 hPa) reanalysesERA‐Interim presents decreasing tropical upper troposphere amplification with altitude, unlike the amplification shown by radio occultations [ABSTRACT FROM AUTHOR]

The Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) Version 2.0 (V2.0) monthly mean tropospheric air temperature, specific humidity, and relative humidity profile data were designed for climate model evaluation in the context of the Coupled Model Intercomparison Project (CMIP). Due to the limitations of the Aqua satellite orbit and the AIRS retrieval algorithm, the sampling biases of the AIRS Obs4MIPs V2.0 data can be large for certain cases and must be considered when the AIRS Obs4MIPs V2.0 data are used for climate model evaluation. In this study, we estimate the sampling biases of the AIRS Obs4MIPs V2.0 data based on the fifth generation of the European Centre for Medium‐Range Weather Forecasts (ECMWF) (ERA5) reanalysis and cross‐check them using the Modern‐Era Retrospective Analysis for Research and Application, Version 2 (MERRA‐2) reanalysis. We then remove the estimated sampling biases from the AIRS Obs4MIPs V2.0 data and produce the sampling‐bias‐corrected AIRS Obs4MIPs V2.1 data that have been published at the Earth System Grid Federation (ESGF) data centers and should be used in the future for climate model evaluation. Plain Language Summary: We have estimated and cross‐checked the sampling biases of the Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) V2.0 data and produced the sampling‐bias‐corrected AIRS Obs4MIPs V2.1 data that should be used in the future for climate model evaluation. Key Points: The sampling biases of the AIRS Obs4MIPs V2.0 data are estimated based on ERA5 reanalysis and cross‐checked based on MERRA‐2 reanalysisThe sampling‐bias‐corrected AIRS Obs4MIPs V2.1 data are produced by removing the sampling bias estimates based on ERA5The new AIRS Obs4MIPs V2.1 data have been published on ESGF and should be used in the future for climate model evaluation [ABSTRACT FROM AUTHOR]

As a microwave radiometer seeing through clouds, Advanced Technology Microwave Sounder (ATMS) observations play a critical role in visually monitoring hurricane warm core structures. However, the presence of orbit gaps within the ATMS observations at low latitudes regions, where hurricanes frequently develop, raises concerns in monitoring hurricanes' spatial variability. To resolve this issue, this study generates gap‐filled ATMS brightness temperature data by using the smoothing algorithm of Penalized Least Square Discrete Cosine Transform. The accuracies of the missing‐filled brightness temperatures for temperature sounding channels are approximately within 1 K. Furthermore, the gap‐filled brightness temperature data from channels 5–12 are utilized to establish a three‐dimensional Hurricane Warm Core Animation System (HWCAS) in near real time (NRT), which helps to visually observe realistic warm core structures of a hurricane system. The information of hurricane warm core over open oceans and coastal areas is derived using a combination of three new regression‐based atmospheric temperature retrieval algorithms, with the averaged error typically within ±1 K at the vertical levels a warm core could occur. Each animation consists of 97 two‐dimensional atmospheric temperature anomaly images at different cross‐sections through hurricane core regions. The retrieved maximum temperature anomalies show well the formation, intensification, weakening, reintensification, and dissipation stages of Hurricane Florence that are similar to those from the European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis. They also show similar weakening and reintensifying stages to those of the maximum sustained winds by the best track data, albeit with some temporal lead/lag. With its strength in NRT hurricane monitoring, the HWCAS demonstrate its great potential in providing meteorologists with timely information of temperature anomaly fields in the inner‐core regions of a hurricane. Key Points: This study fills orbital gaps within ATMS Sensor Data Record (SDR) data to visually observe whole hurricane warm core structuresThis study establishes a 3‐D Hurricane Warm Core Animation System (HWCAS) from near real‐time ATMS observationsEach 3‐D animation contains 97 2‐D atmospheric temperature anomaly images at different cross‐sections through hurricane core regions [ABSTRACT FROM AUTHOR]

Recent studies have used satellite data to estimate the response of top‐of‐atmosphere (TOA) radiative fluxes to surface temperature changes in the Arctic. The satellite‐observed radiative response is indicative of Arctic climate sensitivity that determines future Arctic warming. However, it remains ambiguous whether the satellite‐observed radiative response is invariable because the time period covered by satellite data reflects a rapidly changing transient Arctic climate state with considerable sea ice loss. Using NASA's Clouds and Earth's Radiant Energy System (CERES) observations from 2000 to 2018, this study evaluates the invariability of the radiative response by comparing the radiative response of the high sea ice concentration (SIC) period to that of the low SIC period. The results show that the net radiative response remains approximately unchanged regardless of the SIC (−0.19 ± 0.44 and 0.15 ± 0.16 W m−2 K−1 for high and low SIC periods, respectively). In addition, seven of the 11 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) demonstrated that the modeled radiative responses are stable. The ERA‐interim reanalysis estimates show that regionally confined changes in individual radiative feedbacks such as albedo, lapse rate, water vapor, and clouds do not vary considerably. Consequently, we infer that the radiative response in the Arctic may remain stable even under rapid Arctic climate change. Hence, the Arctic climate sensitivity can be quantified with present satellite observations. Key Points: Satellite‐observed radiative response to surface air temperature change remains unchanged under sea ice retreat over the Arctic regionRegionally confined changes in individual radiative feedbacks cannot considerably change the radiative response in the ArcticThe majority of the climate models demonstrated that the Arctic radiative response is insensitive to sea ice loss in the near future [ABSTRACT FROM AUTHOR]

Analyses of near‐surface air temperature T in Poland for 1781–2016 and in Tbilisi (Georgia) for 1881–2016 have been carried out. We show that the centenary warming effect in Poland and in Tbilisi has almost the same peculiarities. An average centenary warming effect ΔT = (1.08 ± 0.29)°C is observed in Poland and in Tbilisi for 1881–2016. A warming effect is larger in winter season (ΔT = ~1.15°C) than in other seasons (average warming effect for these seasons ΔT = ~0.95°C). We show that a centenary warming is mainly related to the change of solar activity (estimated by sunspot number [SSN] and total solar irradiance [TSI]), particularly, a time interval about ~70 years (1890–1960), when correlation coefficients between 11‐year smoothed SSN and T, and TSI and T are high, r = 0.66 ± 0.07 and r = 0.73 ± 0.07 for Poland and r = 0.82 ± 0.05 and r = 0.90 ± 0.05 for Tbilisi, respectively; in this period solar activity contributes decisively in the global warming. We show that a global warming effect equals zero based on the temperature T data in Poland for period 1781–1880, when human activities were relatively less than in 1881–2016. We recognize a few feeble ~20 ± 3 years of disturbances in the temperature changes for period 1885–1980, most likely related with the fluctuations of solar magnetic cycles. We distinguish the fluctuations of ~7–8 years in Poland's T data, possibly connected with local effects of the North Atlantic Oscillation. Key Points: We analyze temporal changes of the near‐surface air temperature in Poland and in Tbilisi (Georgia) in relation to solar activityIn global warming decisively contributes the period 1890–1960 when correlation between solar activity and the air temperature is highWe identify disturbances in the near‐surface air temperature data: ~20 ± 3 years and ~7–8 years [ABSTRACT FROM AUTHOR]

A high time resolution 2 m2 tracking detector, based on timing Resistive Plate Chamber (tRPC) cells, has been installed at the Faculty of Physics of the University of Santiago de Compostela (Spain) in order to improve our understanding of the cosmic rays arriving at the Earth's surface. Following a short commissioning of the detector, a study of the atmospheric temperature effect of the secondary cosmic ray component was carried out. To take into account this effect, temperature coefficients, WT(h), were obtained from cosmic ray data using a method based on Principal Component Analysis (PCA). The results obtained show good agreement with the theoretical expectation. The method successfully removes the correlation present between the different atmospheric layers, which would be dominant otherwise. We briefly describe the initial calibration and pressure correction procedures, essential to isolate the temperature effect. Overall, the measured cosmic ray rate displays the expected anticorrelation with the effective atmospheric temperature, through the coefficient αT=−0.279±0.051%/K. Rates follow the seasonal variations, and unusual short‐term events are clearly identified too. Key Points: Atmospheric temperature effect isolated with timing resistive plate chambers for the first timeExtraction of temperature coefficients versus atmosphere depth through principal component regression and comparison with theoretical expectationSeasonal and occasional variations for the cosmic ray flux observed show consistency with earlier Global Muon Detector Network results [ABSTRACT FROM AUTHOR]

In this study, two machine learning (ML) models (support vector regression (SVR) and extreme gradient boosting (XGBoost)) are developed to perform seasonal forecasts of the surface air temperature (SAT) in winter (December‐January‐February, DJF) in North America (NA). The seasonal forecast skills of the two ML models are evaluated via cross validation. The forecast results from one linear regression (LR) model, and two dynamic climate models are used for comparison. In the take‐one‐out hindcast experiment, the two ML models and the LR model show reasonable seasonal forecast skills for winter SAT in NA. Compared to the two dynamic models, the two ML models and the LR model have better forecast skill for the winter SAT over central NA, which is mainly derived from a skillful forecast of the second empirical orthogonal function (EOF) mode of winter SAT over NA. In general, the SVR model and XGBoost model hindcasts show better forecast performances than the LR model. However, the LR model shows less dependence on the size of the training data set than the SVR and XGBoost models. In the real forecast experiments during the period of 2011–2017, the two ML models exhibit better forecasting skills for the winter SAT over northern and central NA than do the two dynamic models. The results of this study suggest that the ML models may provide improved forecasting skill for seasonal forecasts of the winter climate in NA. Key Points: Machine learning (ML) models show considerable seasonal forecast skill for winter surface air temperature (SAT) in North America (NA)ML models exhibit better seasonal forecast skill for winter SAT in NA than a linear regression modelThe multimodel ensemble mean of the ML and dynamic models can significantly improve the forecast skill of winter SAT over central NA [ABSTRACT FROM AUTHOR]

We used the mean air temperature and particulate matter concentration at northern and southern rural stations as rural background values to calculate the urban heat island intensity (UHII) and urban pollution island intensity (UPII) for Beijing. The correlation between UHII and UPII is significantly negative in winter during the daytime and nighttime when selecting southern rural background stations but significantly positive in spring during both daytime and nighttime and in winter during the nighttime when selecting northern rural background stations. The downward longwave radiation (DLR) is highly correlated with surface air temperature and water vapor, and with particulate matter concentration in winter and summer. Water vapor also has a high correlation with particulate matter concentration in winter and summer. Winter data were used to investigate the particulate matter contribution to DLR to minimize the effect of humidity. The results indicate that in winter the urban area DLR and net radiation increased more than rural area under polluted conditions compared with clean conditions, which may lead to an increase in UHII. But in other seasons with more moisture, the aerosol effect on DLR is smaller than water vapor. Our results imply that the contribution of air pollutants to DLR had been overestimated in recent studies without removing water vapor effects on the longwave radiation. We suggest that the interaction between the urban heat island and the urban pollution island and related mitigation strategies needs to be carefully studied in the future by considering different climate zone and seasons. Key Points: The urban pollution island intensity is more sensitive to the selection of rural background stations than the urban heat island intensityThe downward longwave radiation is highly correlated with temperature and water vapor and with particulate matter in winterPollutants can enhance downward longwave radiation and net radiation distributions in urban areas more than in rural areas only in winter [ABSTRACT FROM AUTHOR]

Global mean surface air temperature (Tglobal) variability on subdecadal timescales can be of substantial magnitude relative to the long‐term global warming signal, and such variability has been associated with considerable environmental and societal impacts. Therefore, probabilistic foreknowledge of short‐term Tglobal evolution may be of value for anticipating and mitigating some course‐resolution climate‐related risks. Here we present a simple, empirically based methodology that utilizes only global spatial patterns of annual mean surface air temperature anomalies to predict subsequent annual Tglobal anomalies via partial least squares regression. The method's skill is primarily achieved via information on the state of long‐term global warming as well as the state and recent evolution of the El Niño–Southern Oscillation and the Interdecadal Pacific Oscillation. We test the out‐of‐sample skill of the methodology using cross validation and in a forecast mode where statistical predictions are made precisely as they would have been if the procedure had been operationalized starting in the year 2000. The average forecast errors for lead times of 1 to 4 years are smaller than naïve benchmarks on average, and they perform favorably relative to most dynamical Global Climate Models retrospectively initialized to the observed state of the climate system. Thus, this method can be used as a computationally efficient benchmark for dynamical model forecast systems. Plain Language Summary: Year‐to‐year global temperature variability can be large compared to the long‐term progression of global warming, and such year‐to‐year variability has been shown to have considerable environmental and societal effects. Thus, approximate foreknowledge of yearly global temperature deviations should be of value for anticipating some climate impacts. This study presents a relatively simple, empirical method for predicting year‐to‐year global temperature. We show that information on the global spatial patterns of surface air temperature alone can be used to skillfully predict global average temperature 1 to 4 years ahead of time. We find that the method performs favorably compared to predictions from much more computationally expensive Global Climate Models. Key Points: Global patterns of annual surface air temperature can be used to predict subsequent annual global mean temperature deviationsThe state of Global Warming, El Niño, and the IPO constrains subsequent annual global mean temperature to within ~0.45°C (95% confidence)The method is skillful relative to naïve benchmarks, and it is competitive with hindcasts from the best initialized Global Climate Models [ABSTRACT FROM AUTHOR]

In this study we derive the environmental lapse rate (ELR) from vertical profiles of temperature in the lower troposphere, applying it to downscale air temperature of the new European Centre For Medium‐Range Weather Forecasts (ECMWF) reanalysis ERA5, which replaces ERA‐Interim (ERAI). We focus over the western U.S. region, a data‐rich area with observations of daily maximum and minimum temperature (Global Historical Climatology Network) and snow depth and soil temperature. Observations indicate an ELR of −4.5 K·km−1 in the region, lower than the commonly used −6.5 K·km−1. ERA5 ELR agrees with the observational estimates, with some overestimation in winter and limitations in the diurnal variability. The elevation correction of ERA5 temperature using different ELR showed the benefits of deriving ELR fields from ERA5 vertical profiles, when compared with a constant ELR. Simulations with the ECMWF land surface model, at 9‐km resolution, driven by ERA5 using different ELR corrections showed the added value of the methodology, but the impact of different ELR corrections is limited. However, the validity of the downscaling method in reducing temperature to station altitude suggests that there is sufficient generality for application at kilometer and subkilometer resolutions. By comparing the estimated representativity errors of observations with reanalysis, the improvements from ERAI to ERA5 are mainly visible in the random component of the error. Large systematic biases remain, which require further attention from the modeling and data assimilation, and limit the potential benefits of ELR corrections. Key Points: Environmental lapse rate derived from atmospheric reanalysis vertical profiles agrees with observational estimatesSurface downscaling outperforms ERA5, but the impact of different ELR corrections to the driving data is reducedSystematic biases in ERA5 near‐surface temperature require further efforts from modeling and data assimilation [ABSTRACT FROM AUTHOR]

Surface emissivity has a significant impact on atmospheric parameter retrievals from microwave sounding instruments. To reduce the dependence of retrievals on surface emissivity, a double channel differences equation is deduced, and a corresponding retrieval scheme is constructed. Retrieval experiments are performed using Advanced Microwave Sounding Unit‐A (AMSU‐A) and Microwave Humidity Sounder (MHS) simulations and global measurements. Simulation experiments show that the double channel differences scheme can reduce the root mean square errors (RMSE) of the temperature and humidity profiles in the middle and lower atmosphere. Retrieval experiments based on AMSU‐A and MHS global measurements show that the proposed scheme can significantly reduce the RMSE of temperature profiles in the lower atmosphere and humidity profiles in the middle and lower atmosphere for cloudy and cloudless conditions, different surface types, and different scan angles, with maximum reduction values of 0.64 K and 9.03%, respectively. Regarding RMSE improvement, that of the cloudy condition is greater than that of the cloudless condition, that of the land is greater than that of the coast and the sea, and there is no significant dependence on the scan angles. The double channel differences scheme is very sensitive to initial near‐surface temperatures. Reducing the initial near‐surface temperature error can significantly improve the temperature retrieval accuracy below 900 hPa, with maximum reduction value of 3.25 K. Plain language summary: Microwave sounding instruments such as AMSU‐A/MHS provide an unique ability to acquire global atmospheric temperature and humidity profiles. However, at present, their ability to acquire temperature and humidity information in the lower atmosphere has not been fully exploited, mainly due to the impact of surface emissivity. Therefore, reducing the impact of surface emissivity is a key issue. In this paper, a temperature and humidity profiles retrieval scheme is constructed by using the relationship between the surface emissivity of adjacent channels. The research shows that the scheme can significantly improve temperature and humidity retrieval accuracy for cloudy and cloudless conditions, different surfaces types, different scan angles. Reducing initial values error in the scheme can further improve retrieval accuracy. Key Points: We propose a microwave temperature and humidity retrieval scheme based on double channel differencesWe show the accuracy performance of the double channel differences retrieval scheme for AMSU‐A/MHS measurementsResults demonstrate a significant retrieval improvement over a traditional AMSU‐A/MHS neural network retrieval scheme [ABSTRACT FROM AUTHOR]

Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi‐model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next‐day maximum and minimum air temperatures (Tmaxt+1 and Tmint+1) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in‐situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R2 of 0.69, a bias of −0.85 °C and an RMSE of 2.08 °C for Tmaxt+1 forecast, whereas the proposed models resulted in the improvement with R2 from 0.75 to 0.78, bias from −0.16 to −0.07 °C and RMSE from 1.55 to 1.66 °C by hindcast validation. For forecasting Tmint+1, the LDAPS model had an R2 of 0.77, a bias of 0.51 °C and an RMSE of 1.43 °C by hindcast, while the bias correction models showed R2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03 °C, and RMSEs from 0.98 to 1.02 °C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave‐one‐station‐out cross‐validation. Key Points: Machine learning based bias correction of air temperature forecasts of a numerical modelAll machine learning models improved prediction skills of air temperatureAn ensemble of three machine learning models resulted in more robust bias correction than individual machine learning models [ABSTRACT FROM AUTHOR]

A new technique is advanced to obtain tropospheric temperature profile using vibrational Raman (VR) and rotational Raman (RR) backscattering. The proposed technique can obtain tropospheric temperature and decrease retrieval errors caused by insufficient suppression of the Mie backscattering in the RR detection channels. The retrieval equations are obtained using VR and RR lidar equations. The feasibility of this technique is demonstrated by applying the algorithm to the Hampton University VR and RR lidar signals. Intercomparisons of these atmospheric temperature retrievals from the Hampton University lidar show good agreement with local radiosonde measurements. Moreover, the proposed technique is applied to a cloudy case and is further validated by showing the good agreements with radiosonde measurements of cloud temperature. Key Points: A new technique is proposed to retrieve tropospheric temperature profile using vibrational and rotational Raman backscatteringThe new technique can decrease retrieval errors caused by insufficient suppression of the Mie backscattering in the rotational Raman detection channelsThe new technique is validated by showing the good agreements between HU lidar and radiosonde measurements of temperature [ABSTRACT FROM AUTHOR]

The main features of the urban boundary layer in the metropolitan region of São Paulo are estimated based on rawinsondes carried out (a) every 3 hr in two 10‐day field campaigns of the MCITY BRAZIL Project during the summer and winter of 2013 and (b) regularly once per day and continuously for 4 years from 2009 to 2013. On average, the boundary layer height showed a daytime maximum of 1476 ± 149 m in summer and 1122 ± 168 m in winter campaigns. The differences are related to seasonal variations in the (a) buoyancy flux at the surface, which was 30% larger in summer (4.7 ± 0.6 MJ m−2 day−1), and the (b) static stability of the free atmosphere, which was 15% smaller in summer (3.3 ± 0.1 K km−1). The average nighttime boundary layer height, estimated from equilibrium empirical expression, indicated maximum of 126 ± 13 m in summer and 122 ± 10 m in winter campaigns. The presence of a low‐level jet was identified in 80% of the field campaign nights, with intensity varying from 2.7 to 14 m s−1 and height ranging from 95 to 962 m. The nighttime residual‐mixing layer is well estimated using the air temperature gradient method and is a good indication of the maximum vertical evolution of the previous daytime boundary layer. The monthly average maximum boundary layer height varied from a minimum of 1,061 ± 77 m in September to maximum of 1,632 ± 96 m in May. Key Points: Main urban boundary layer features over the São Paulo megacity are estimated using 160 rawinsonde released in 2013 field campaignsSurface layer and entrainment layer thickness correspond to 10% (12%) and 16% (17%) of the urban boundary layer height in summer (winter)Four‐year climatology of boundary layer height estimated objectively yield a maximum (1,632 m) in May and a minimum (1,061 m) in September [ABSTRACT FROM AUTHOR]

A number of previous studies show that the long‐term Arctic winter warming causes severe cold over East Asia and North America. We use the atmospheric data from National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis to define the Arctic rapid tropospheric daily warming (RTDW) events that in one day, Arctic average 1,000‐hPa air temperature is over 3σ warmer anomaly in situ winter. We find that main warm temperature advection of the RTDW events is from Atlantic Ocean and Pacific Ocean, named the Atlantic pattern and the Pacific pattern, respectively. The RTDW events of different warm temperature advection have different vertical distributions of warming. Therefore, different polar vortex splitting is induced and the splitting of polar vortex means movement of the cold air mass. The impacts of RTDW events on the Northern Hemisphere cold winter are mainly through the Asia‐Europe path caused by the Atlantic pattern and the North America path caused by the Pacific pattern. As for the Asia‐Europe path, the cold air invades the northern hemisphere from northwest Europe to Southeast Asia throughout the entire Eurasian continent. In the case of the North America path, the negative temperature anomalies are located in the west of Canada and America. The different warm temperature advection of RTDW events leads to different regions of the polar cold air mass invasion through different paths. Our results may help find out where the severe cold events impact on after the Arctic RTDW events with the occurrence of different warm temperature advection. Plain Language Summary: In our research, we show that there are two impacts of Arctic rapid tropospheric daily warming events on cold winters over Northern Hemisphere. The Arctic rapid tropospheric daily warming with warm temperature advection from Atlantic will cause cooling in Asia‐Europe and from Pacific will cause cold air outbreaks in North America in the next few days. Key Points: Main warm temperature advection of rapid tropospheric daily warming events is from Atlantic Ocean and Pacific OceanImpacts of rapid tropospheric daily warming events on Northern Hemisphere cold winter are mainly manifested as two paths [ABSTRACT FROM AUTHOR]

Inversion of radio occultation (RO) measurements to atmospheric parameters in the neutral atmosphere utilizes the assumption of spherical symmetry by implementation of the Abel transform. The main contribution to the retrieved refractional angle and other geophysical parameters comes from gaseous properties of the atmosphere. The atmospheric refraction is expressed by a function of air pressure, air temperature, and water vapor pressure. Such commonly adopted methodology results in highly comparable RO retrievals with background models. However, in the lowermost troposphere referred to as planetary boundary layer, inversion in spherically symmetric atmosphere is an ill‐conditioned problem. The presence of superrefractions introduces negative errors in the RO‐retrieved refractivity (N‐bias). We show that significant refractivity gradients are frequently collocated with clouds over oceans in tropical and subtropical regions. Based on gridded monthly means we show that superrefractions usually occur at altitudes up to 2 km and the largest cloud fractions tend to suspend at underlying layers. The magnitude of clouds expressed in terms of refractivity units can exceed 1.5, which corresponds to 0.5% in terms of fractional differences. We use both geometrical optics and wave optics techniques to illustrate propagation mechanisms in RO retrievals. Simulation experiments suggest that RO inversions in cloudy planetary boundary layer lead to larger negative N‐biases. Low‐level clouds retrieved from numerical weather prediction model could therefore be used as an indicator of erroneous RO observations. A better agreement with RO refractivity could be achieved by incorporating cloud variables into background fields especially over the Pacific and Atlantic Oceans. Key Points: Superrefractions show geographical and temporal relationship to cloud liquid water in PBLOccultations measured under superrefraction conditions can be affected by clouds to a nonnegligible order (1.5 N‐units or 0.5%)Statistics of systematic errors in RO observations indicate particularly active oceanic regions [ABSTRACT FROM AUTHOR]

Chad is the largest country of the Sahel region with different climatic zones, varying from arid in the north to tropical in the south. These climatic zones respond differently to climate change signals. Therefore, their detection over major cities, which are scattered within different climatic zones, is of utmost importance. The changes in hydroclimatic fields such as rainfall and temperature were examined over the major cities in various regions for the period 1950 to 2014. Rainfall shows a significant decreasing trend especially over cities close to Lake Chad (Lere, Mondou, Mongo, and Sarh), whereas no significant trend is observed for cities farther from the Lake. However, a consistently increasing trend in temperature is found across all cities. The cities in the north (Faya, Abeche, and Ati) receive far less rainfall than those located in southern Chad. All cities (except Faya and Lere) received higher rainfall during 1950–1965 (wet period), entering a dry regime between 1966 and 1990 (dry period) and subsequently recovering rainfall totals, toward previous levels, between 1991 and 2014 (recovery phase). A substantial rise in air temperature is observed after 1980–1985, reflecting the gradual rise of temperature in recent times. In summary, rainfall is recovering from a dry regime and temperature is rising over all the major cities of Chad. More researches in this region is needed to develop local scale mitigation strategies and adaptation technology. Key Points: Detection of climate change signal over the major cities of Chad that scattered within different climatic zones is of utmost importanceThe rainfall was decreasing during wet period, which recovers gradually at a slower rate over most citiesA consistent increasing trend in temperature is found over all the cities, but substantially, warming is found after 1980–1985 [ABSTRACT FROM AUTHOR]

Urban buildings and parks play an important role in regulating urban climate and ecosystem services. Diurnal air temperature range (DTRa) of rocklike buildings is commonsensically regarded as higher due to no latent heat from evapotranspiration and its smaller thermal inertia compared to wet soil. Therefore, the building DTRa is supposed to be higher than that of parks due to its building fabric. However, we found an opposite phenomenon (smaller building DTRa than that of parks), and the underlying mechanisms explaining this phenomenon are unclear. Here we conducted, in Beijing (China), a long‐term observational campaign of standard meteorological variables and radiation/energy fluxes to provide a new valuable evidence (a part of external energy) to explain this phenomenon. The observations indicated external heat energies from horizontal advection and anthropogenic heat sources. We found a significantly lower building DTRa than that of parks (ΔDTRa =2.53±1.93 °C), with a maximum difference of 3.54±1.96 °C in autumn; which was mainly attributed to higher daily minimum air temperature. We also found the large differences in air temperature contribution between the buildings and the parks happened mainly at night. The external heat sources of the building contributed 16.71% to the nighttime air temperature, which was higher than that of the parks (8.39%). The more indoor and outdoor anthropogenic heat sources in the building footprints were the major cause of the slower decrease in Tmin. The comparison between buildings and parks can be extensively applied to analyzing the effects of urbanization on climate. Plain Language Summary: An effective mosaic of buildings and parks, as the two core elements of cities, plays an important role in regulating urban climate and providing ecosystem services. A comfortable urban environment is becoming the pivotal theme for the United Nations Sustainable Development Goals 2030 related to resilient and sustainable cities and to mitigate climate change. While the urbanization effect on the diurnal air temperature range (DTRa) is still being debated within the climate community, we, surprisingly, found a significantly lower DTRa of buildings than of parks, which contradicts our common knowledge. The observations showed that nocturnal radiation cooling did not result in a decrease of air temperature, largely due to external heat input (60.17±22.98 W/m2). Heat fluxes of both the building roofs from their large indoor heat sources and the mixed peripheral advection with anthropogenic heat discharges contributed to a higher minimum air temperature. In daytime, despite additional input from human‐induced energy, neighboring vegetation transpiration and building/tree shadows offset those heat fluxes and slowed down the air temperature increase. The findings provide new knowledge on urban heat island mitigation and climate change adaptation. Key Points: The diurnal air temperature range of buildings is significantly lower than that of parks (ΔDTRa = 2.53±1.93 °C)The lower diurnal air temperature range of buildings compared to parks is attributed to a higher daily minimum air temperatureThe contribution of external heat sources by buildings (16.71%) to the nighttime air temperature was twice that of the park (8.39%) [ABSTRACT FROM AUTHOR]

The Atmospheric Infrared Sounder (AIRS) Obs4MIPs (Observations for Model Intercomparison Projects) Version 2 data set includes monthly mean tropospheric air temperature, specific humidity, and relative humidity for each calendar month from September 2002 to September 2016, on a global 1° × 1° latitude‐longitude spatial grid and on the Coupled Model Intercomparison Project eight mandatory vertical pressure levels from 1,000 to 300 hPa. It also includes standard error and number of observations, for an estimate of AIRS data retrieval error and sampling uncertainty. Three new technical notes describe the data set. This data set is designed for Coupled Model Intercomparison Project climate model evaluation and has been published at Earth System Grid Federation data centers since April 2018. It adds new monthly mean tropospheric relative humidity data to Obs4MIPs and updates and extends the monthly mean tropospheric air temperature and specific humidity data in AIRS Obs4MIPs Version 1 data set. The data source for this data set is the AIRS Version 6 Level 3 standard monthly mean air temperature, specific humidity, and relative humidity data products in the "TqJoint" grids from the AIRS and Advanced Microwave Sounding Unit A combined physical retrievals. This paper documents this data set in terms of motivation for this data set, data description, data origin and processing procedures, major improvements from the previous version, and caveats for its use in climate model evaluation. Plain Language Summary: We have published a new data set designed for climate model evaluation from the infrared and microwave atmospheric sounding system (Atmospheric Infrared Sounder and Advanced Microwave Sounding Unit A) on the NASA Aqua satellite. In this paper, we briefly describe the reasons for providing this data set, its detailed data structure, the original data and processing procedures used to generate this data set, major improvements in comparison to the previous version, and the issues to be considered for climate model evaluation with it. Key Points: A new data set containing Atmospheric Infrared Sounder observations and designed for climate model evaluation has been publishedThis data set includes monthly mean gridded tropospheric air temperature, specific humidity, and relative humidity at eight pressure levelsThe standard error and number of observations, for an estimate of data uncertainty, along with three technical notes are also provided [ABSTRACT FROM AUTHOR]