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The results of airborne measurements and statistical characteristics of mesoscale fluctuations of wind velocity, temperature, and concentrations of gas constituents at different heights of a stably stratified troposphere are presented. The measurements were carried out in September 2022 in the Arctic region of Russia with the aircraft laboratory Tu-134 "Optik." The obtained spectra and structure functions of the fluctuations are interpreted with the theoretical model of formation of the spectrum of mesoscale wind velocity and temperature fluctuations described in the paper. The presence at high wavenumbers of a steep section in the obtained horizontal wavenumber spectra of the fluctuations of wind velocity and greenhouse gas concentration with a slope close to −3 is discussed. The fluctuation spectra along different slanted tracks of the aircraft crossing the tropospheric layer between altitudes of 1 and 9 km are also obtained and analyzed with the theoretical model. [ABSTRACT FROM AUTHOR]

In this paper, we perform a fractional integration analysis of the average monthly temperature and precipitation data in 17 departments of Guatemala. Two analyses are performed, the first with the original data and the second with the anomalies based on the period January 1994–December 1999. The results indicate that there is a significant positive time trend in temperatures in the departments of Guatemala (0.0045°C month−1), Quetzaltenango (0.0040°C month−1), Escuintla (0.0034°C month−1), and Huehuetenango (0.0047°C month−1), whereas in the case of precipitation no time trend was observed. An important relevant result is that the departments of El Progreso, Baja Verapaz, and Guatemala occupy the second, third and fourth highest levels of persistence for both temperatures and precipitation, with Sacatepéquez and Quiché displaying the first places for temperature and precipitation, respectively, thus making these five departments the ones that are most vulnerable to climate change since a shock would take a long time to disappear. [ABSTRACT FROM AUTHOR]

Instrumental data from the pre–Industrial Revolution period are important to -understand climate change. In this paper, the observations made by the French missionary J. Amiot in present-day central Beijing during 1757–62 were processed and analyzed. The observations represent the earliest continuous dataset of meteorological records found in China that have been digitized recently. Comparisons between the Amiot annual temperature range and extreme values with modern observations showed that the observations were read at approximately 0800 and 1500 local solar time (LST) in a well-ventilated outdoor site. The daily maximum, minimum, and mean temperatures (T-max, T-min, and T-mean, respectively) during 1757–62 were determined by examining the relationship between temperature at 0800 and 1500 LST and T-max, T-min, and T-mean in modern reference series. Nearly 260 years ago, Beijing's climate was typical of an inland temperate monsoon zone with annual T-mean, annual mean T-max, and annual mean T-min being 11.5°, 17.8°, and 6.1°C, respectively; further, the temperatures did not vary considerably from the 1951–1980 temperatures, but differed evidently compared to relatively recent decades (1981–2020). The difference was larger than the magnitudes of global and regional temperature changes. Thus, climate warming since the pre–Industrial Revolution period in the urban areas of Beijing has dominantly occurred over the last four decades. Uncertainties related to the thermometer and observational conditions 260 years ago and the interpolation method used have also been discussed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we present a late summer (August-September) temperature reconstruction over the period 1792-2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP). The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period 1960-2020 and captured the warming trend since the 1960s, which would support the current warming on the TP. In addition, a warming hiatus existed during 2001-12 and the last 20 years (2000-20) were the warmest period in the past two centuries. The reconstruction matched other MXD- and mean latewood density (LWD)-based late summer temperature reconstructions from neighboring regions, and fluctuated in synchrony with the Climatic Research Unit (CRU) Northern Hemisphere land surface temperature during 1850-2020. Multitaper method analysis and wavelet analysis revealed significant periodicities of 2-3, 20-30, and 40-60 years in the reconstructed series. Our reconstructed series was very consistent and highly correlated with the Atlantic multidecadal oscillation (AMO). During the warm phase of the AMO, higher pressure and divergent horizontal winds over the TP contribute to warmer summers in the region. In addition, we found that the southern TP experienced the lowest temperature and downward solar radiation in the second year following large volcanic eruptions. The decrease in downward solar radiation may be directly responsible for the occurrence of the lowest temperatures. The results indicate that the AMO and large volcanic eruptions were impacting factors on temperature in our study area. [ABSTRACT FROM AUTHOR]

This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes. We estimate the EBM-SS model using historical datasets at the global level for the period 1955–2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from Coupled Model Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models. Significance Statement: We develop a statistical model to understand Earth's energy balance and how it impacts temperature across the planet's surface and deep oceans. Unlike previous approaches, ours is fully statistical. This means that we use maximum likelihood to calculate the estimates and evaluate the level of uncertainty in the model's parameters. By applying the model to historical data from 1955 to 2020, we are able to make predictions about the average global surface temperature that are consistent with those from the Coupled Model Intercomparison Project. Our findings support current predictions about global temperatures using a straightforward statistical climate model, grounded in historical data. [ABSTRACT FROM AUTHOR]

Studying temperature probability distributions and the physical processes that shape them is important for understanding extreme temperature events. Previous work has used a conditional mean temperature framework to reveal whether horizontal temperature advection drives temperature to extreme or median values at a specific location as a method to dynamically interpret temperature probability distributions. In this paper, we generalize this method to study how other processes shape temperature probability distributions and explore the diverse effects of horizontal temperature advection on temperature probability distributions at different locations and different temperature percentiles. We apply this generalized method to several representative regions to demonstrate its use. We find that temperature advection drives temperatures toward more extreme values over most land in the midlatitudes (i.e., cold air advection occurs during cold anomalies and warm air advection occurs during warm anomalies). In contrast, we find that horizontal temperature advection dampens temperature anomalies in some coastal summer monsoon regions, where extreme temperatures result from other processes, such as horizontal humidity advection and vertical temperature advection. By calculating the mean of processes conditioned on the temperature percentile, this method enables composite analysis of processes that contribute to events for all percentiles and a range of processes. We show examples of composites at different percentiles for certain processes and regions to illustrate the conditional mean analysis. This general approach may benefit future studies related to temperature probability distributions and extreme events. [ABSTRACT FROM AUTHOR]

Sudden stratospheric warmings (SSWs) are impressive events that occur in the winter hemisphere's polar stratosphere and are capable of producing temperature anomalies upward of +50 K within a matter of days. While much work has been dedicated toward determining how SSWs occur and their ability to interact with the underlying troposphere, one underexplored aspect is the role of radiation, especially during the recovery phase of SSWs. Using a radiative transfer model and a heating rate analysis for distinct layers of the stratosphere averaged over the 60°–90°N polar region, this paper accounts for the radiative contribution to the removal of the anomalous temperatures associated with SSWs. In total 17 events are investigated over the 1979–2016 period. This paper reveals that in the absence of dynamical heating following major SSWs, longwave radiative cooling dominates and often results in a strong negative temperature anomaly. The polar winter stratospheric temperature change driven by the radiative cooling is characterized by an exponential decay of temperature with an increasing e-folding time of 5.7 ± 2.0 to 14.6 ± 4.4 days from the upper to middle stratosphere. The variability of the radiative relaxation rates among the SSWs was determined to be most impacted by the initial temperature of the stratosphere and the combined dynamic and solar heating rates following the onset of the events. We also found that trace-gas anomalies have little impact on the radiative heating rates and the temperature evolution during the SSWs in the mid- to upper stratosphere. [ABSTRACT FROM AUTHOR]

Management of adverse health-related effects from heat waves requires comprehensive and accessible sources of information. This paper examines the effects of temperature and air pollution on human health and identifies areas with increased occurrence of emergency ambulance dispatches in the city of Würzburg, Bavaria, Germany, and discusses the applicability for health care interventions and urban planning. An overdispersed Poisson generalized additive model was used to examine and predict the association and potential lag of exposure between temperature, air pollution, and three types of emergency ambulance dispatches during the study period from 2011 to 2019. A linear model was used to estimate heat-wave effects. A line density function was used to identify areas with increased occurrence of dispatches. Significant effects of temperature were detected for nontraumatic and cardiovascular diseases after exceeding a threshold temperature. The exposure–response relationships showed an increased relative risk up to two days after exposure for nontraumatic and cardiovascular diseases. Results indicate a significant association between presence of heat waves and cardiovascular diseases with up to 17% (95% confidence interval: 5.9%–30.0%) increased relative risk on a heat-wave day relative to a non-heat-wave day. Dispatches for cardiovascular diseases occur more often in areas with a high population and building density, especially in summer. The analyses identified hotspots of heat-related dispatches in areas with increased population and building density and provides baseline information for interventions in future urban planning and public health care management based on data commonly available even in small cities. Significance Statement: The purpose of this study is to demonstrate how authorities in even medium- and small-sized cities can assess health impacts of heat stress or air pollution using free accessible emergency ambulance data and software to incorporate the outcomes in their spatial planning or health care management. This is important as ongoing climate change requires all urban communities to adapt and reduce adverse impacts of climate change and air pollution. Our results show that extreme heat leads to increased emergency ambulance dispatches in a medium-sized city in Germany and provide a spatial overview of where health care interventions and urban planning can focus to mitigate adverse effects. [ABSTRACT FROM AUTHOR]

Reconstructing the history of polar temperature from ice core water isotope (δ18O) calibration has remained a challenge in paleoclimate research, because of our incomplete understanding of various temperature--δ18O relationships. This paper resolves this classical problem in a new framework called the unified slope equations (USE), which illustrates the general relations among spatial and temporal δ18O--surface temperature slopes. The USE is applied to the Antarctica temperature change during the last deglaciation in model simulations and observations. It is shown that the comparable Antarcticamean spatial slope with deglacial temporal slope in δ18O--surface temperature reconstruction is caused, accidentally, by the compensation responses between the δ18O--inversion layer temperature relation and the inversion layer temperature itself. Furthermore, in light of the USE, we propose that the present seasonal slope of δ18O--inversion layer temperature is an optimal paleothermometer that is more accurate and robust than the spatial slope. This optimal slope suggests the possibility of reconstructing past Antarctic temperature changes using present and future instrumental observations. [ABSTRACT FROM AUTHOR]

A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

The Southern Hemisphere temperature has experienced obvious changes with great spatial differences over the past several decades. Most regions show extreme warming, especially those located at 35°–55°S. In contrast, subsurface cooling exists between 15° and 35°S in the Indian and Pacific basins. The subsurface temperature and salinity change can be divided into spiciness change and heave components. The results show the warming due to isopycnal movement being largely offset by significant spiciness cooling at middepth. Surface warming and subduction into the interior ocean account for subsurface spiciness cooling near 45°S, while surface freshening and penetration along isopycnals are more important to the subsurface spiciness cooling farther north. The isobaric temperature change is associated with pure warming and pure heaving, and the subsurface cooling observed in the Indian and Pacific subtropics is predominantly attributed to pure heaving. This study provides a quantitative estimate of the relative contribution of surface temperature, salinity change, and circulation adjustment in subsurface temperature change, highlighting the importance of circulation change in producing subsurface cooling. Further research is needed to understand why different processes dominate in different ocean sections. Significance Statement: While the global ocean is warming, the subsurface temperature change exhibits a significant regional disparity. This paper attempts to explain the deep-reaching warming at 35°–55°S and cooling at 15°–35°S based on three historical observation datasets. We find that the cooling mostly occurs between 400 and 1000 m in the south Indian and Pacific subtropics (15°–35°S), which is attributed to pure heaving, indicating the importance of circulation change in these regions. The midlatitude warming (35°–55°S) is mainly caused by the pure warming process, which is related to heat uptake at the subpolar surface and northward and downward heat transport. The spiciness cooling near 45°S is mainly driven by the subduction of the surface warming signal while the freshening process has a stronger impact on spiciness cooling farther north. [ABSTRACT FROM AUTHOR]

In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales. Significance Statement: Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used. [ABSTRACT FROM AUTHOR]

This paper presents a statistical analysis of structural changes in the Central England temperature series, one of the longest surface temperature records available. A changepoint analysis is performed to detect abrupt changes, which can be regarded as a preliminary step before further analysis is conducted to identify the causes of the changes (e.g., artificial, human-induced, or natural variability). Regression models with structural breaks, including mean and trend shifts, are fitted to the series and compared via two commonly used multiple changepoint penalized likelihood criteria that balance model fit quality (as measured by likelihood) against parsimony considerations. Our changepoint model fits, with independent and short-memory errors, are also compared with a different class of models termed long-memory models that have been previously used by other authors to describe persistence features in temperature series. In the end, the optimal model is judged to be one containing a changepoint in the late 1980s, with a transition to an intensified warming regime. This timing and warming conclusion is consistent across changepoint models compared in this analysis. The variability of the series is not found to be significantly changing, and shift features are judged to be more plausible than either short- or long-memory autocorrelations. The final proposed model is one including trend shifts (both intercept and slope parameters) with independent errors. The analysis serves as a walk-through tutorial of different changepoint techniques, illustrating what can be statistically inferred. [ABSTRACT FROM AUTHOR]

Knowledge about the characteristics of the atmospheric boundary layer is vital for the understanding of redistribution of air and suspended contents that are particularly driven by turbulent motions. Despite many modeling studies, detailed observations are still demanded of the development of turbulent exchange under stable and unstable conditions. In this paper, we present an attempt to observationally describe atmospheric internal waves and their associated turbulent eddies in detail, under varying stable conditions. Therefore, we mounted 198 high-resolution temperature (T) sensors with 1-m spacing on a 200-m-long cable. The instrumented cable was attached along the 213-m-tall meteorological mast of Cabauw, Netherlands, during late summer 2017. The mast has standard meteorological equipment at extendable booms at six levels in height. A sonic anemometer is at 60 m above ground. The T sensors have a time constant in air of τa ≈ 3 s and an apparent drift about 0.1°C month−1. Also due to radiation effects, short-term measurement instability is 0.05°C h−1 during nighttime and 0.5°C h−1 during daytime. These T-sensor characteristics hamper quantitative atmospheric turbulence research, due to a relatively narrow inertial subrange of maximum one order of magnitude. Nevertheless, height–time images from two contrasting nights show internal waves up to the buoyancy period of about 300 s, and shear and convective deformation of the stratification over the entire 197-m range of observations, supported by nocturnal marginally stable stratification. Moderate winds lead to 20-m-tall convection across weaker stratification, weak winds to episodic <10-m-tall shear instability across larger stratification. [ABSTRACT FROM AUTHOR]

The impact of two different coupled cirrus microphysics-radiation parameterizations on the zonally averaged temperature and humidity biases in the tropical tropopause layer (TTL) of a Met Office climate model configuration is assessed. One parameterization is based on a linear coupling between a model prognostic variable, the ice mass mixing ratio q i, and the integral optical properties. The second is based on the integral optical properties being parameterized as functions of q i and temperature, T c, where the mass coefficients (i.e., scattering and extinction) are parameterized as nonlinear functions of the ratio between q i and T c. The cirrus microphysics parameterization is based on a moment estimation parameterization of the particle size distribution (PSD), which relates the mass moment (i.e., second moment if mass is proportional to size raised to the power of 2) of the PSD to all other PSD moments through the magnitude of the second moment and T c. This same microphysics PSD parameterization is applied to calculate the integral optical properties used in both radiation parameterizations and, thus, ensures PSD and mass consistency between the cirrus microphysics and radiation schemes. In this paper, the temperature-non-dependent and temperature-dependent parameterizations are shown to increase and decrease the zonally averaged temperature biases in the TTL by about 1 K, respectively. The temperature-dependent radiation parameterization is further demonstrated to have a positive impact on the specific humidity biases in the TTL, as well as decreasing the shortwave and longwave biases in the cloudy radiative effect. The temperature-dependent radiation parameterization is shown to be more consistent with TTL and global radiation observations. [ABSTRACT FROM AUTHOR]

Scholars have been taking the impact of weather on social movements for granted for some time, despite a lack of supporting empirical evidence. This paper takes the topic more seriously, analyzing more than 7000 social movement events and 36 years of weather records in Washington, D.C., and New York City (1960-95). Here, 'good weather' is defined as midrange temperature and little to no precipitation. This paper uses negative binomial regression models to predict the number of social movements per day and finds social movements are more likely to happen on good days than bad, with seasonal patterns controlled for. Results from logistic regression models indicate violence occurs more frequently at social movement events when it is warmer. Most interestingly, the effect of weather is more salient when there are more political opportunities and resources available. This paper discusses the implications and suggests future research on weather and social movement studies. [ABSTRACT FROM AUTHOR]

Sound speed profile (SSP) affecting underwater acoustics is closely related to the temperature and the salinity fields. It is of great value to obtain the temperature and the salinity information through the high-precision sound speed profiles. In this paper, a data assimilation scheme by introducing sound speed profiles as a new constraint is proposed within the framework of 3DVAR data assimilation [referenced as SSP-constraint 3DVAR (SSPC-3DVAR)], which aims at improving the analysis accuracy of initial fields of the temperature and salinity in coastal sea areas. To validate the performance of the new assimilation scheme, ideal experiments are first carried out to show the advantages of the new proposed SSPC-3DVAR. Then the temperature, the salinity, and the SSP observations from field experiments in a coastal area are assimilated into the Princeton Ocean Model to validate the performance of short-time forecasts, adopting the SSPC-3DVAR scheme. Results show that it is efficient to improve the estimate accuracy by as much as 14.6% and 11.1% for the temperature and salinity, respectively, when compared with the standard 3DVAR. It demonstrates that the proposed SSPC-3DVAR approach works better in practice than the standard 3DVAR and will primarily benefit from variously and widely distributed observations in the future. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) radio occultation (RO) observations, first made of Earth's atmosphere in 1995, have contributed in new ways to the understanding of the thermal structure and variability of the tropical upper troposphere–lower stratosphere (UTLS), an important component of the climate system. The UTLS plays an essential role in the global radiative balance, the exchange of water vapor, ozone, and other chemical constituents between the troposphere and stratosphere, and the transfer of energy from the troposphere to the stratosphere. With their high accuracy, precision, vertical resolution, and global coverage, RO observations are uniquely suited for studying the UTLS and a broad range of equatorial waves, including gravity waves, Kelvin waves, Rossby and mixed Rossby–gravity waves, and thermal tides. Because RO measurements are nearly unaffected by clouds, they also resolve the upper-level thermal structure of deep convection and tropical cyclones as well as volcanic clouds. Their low biases and stability from mission to mission make RO observations powerful tools for studying climate variability and trends, including the annual cycle and intraseasonal-to-interannual atmospheric modes of variability such as the quasi-biennial oscillation (QBO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO). These properties also make them useful for evaluating climate models and detection of small trends in the UTLS temperature, key indicators of climate change. This paper reviews the contributions of RO observations to the understanding of the three-dimensional structure of tropical UTLS phenomena and their variability over time scales ranging from hours to decades and longer. [ABSTRACT FROM AUTHOR]

The sampling error caused by the uneven distribution of radio occultation (RO) profiles in both space and time domains is an important error source of RO climatologies. In this paper, the sampling error RO temperature climatologies is investigated using the 4-yr (2007-10) data from the Constellation Observing System for Meteorology, Ionosphere, and Climate mission. The error is divided into three parts, including local time component (LTC), temporal component (TC), and spatial component (SC). The characteristics of the three components are investigated. Results show the following: 1) The LTC part of the total sampling error is characterized by a pattern of periodic positive and negative deviations, with a full cycle of about four months. The most significant LTC values are found in the area around 608N/S and the polar regions. 2) The TC part is mainly associated with the extent of day-to-day temperature variability and the daily number of RO profiles observed in each month. The most pronounced TC part is shown in high-latitude areas in wintertime, where the day-to-day temperature variability is high. 3) The SC part shows distinct features in different altitude ranges. It is characterized by a systemic error in the lower troposphere (2-8 km) but exhibits a seasonal trend at the altitude range from 8 to 40 km. 4) The total sampling error is dominated by the TC and SC parts in the troposphere and lower stratosphere, whereas in the upper stratosphere it is dominated by the LTC part. [ABSTRACT FROM AUTHOR]

Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S-80°N, 1979-2016) = 0.174°C decade−1], ~30% larger than our previous version of the dataset [global trend (70°S-80°N, 1979-2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics. [ABSTRACT FROM AUTHOR]

Temperature observations of the upper-air atmosphere are now available for more than 40 years from both ground- and satellite-based observing systems. Recent years have seen substantial improvements in reducing long-standing discrepancies among datasets throughmajor reprocessing efforts. The advent of radio occultation (RO) observations in 2001 has led to further improvements in vertically resolved temperature measurements, enabling a detailed analysis of upper-troposphere/lower-stratosphere trends. This paper presents the current state of atmospheric temperature trends from the latest available observational records. We analyze observations from merged operational satellite measurements, radiosondes, lidars, and RO, spanning a vertical range fromthe lower troposphere to the upper stratosphere. The focus is on assessing climate trends and on identifying the degree of consistency among the observational systems. The results showa robust cooling of the stratosphere of about 1-3 K, and a robust warming of the troposphere of about 0.6-0.8K over the last four decades (1979- 2018). Consistent results are found between the satellite-based layer-average temperatures and vertically resolved radiosonde records. The overall latitude-altitude trend patterns are consistent between RO and radiosonde records. Significant warming of the troposphere is evident in the RO measurements available after 2001, with trends of 0.25-0.35K per decade. Amplified warming in the tropical upper-troposphere compared to surface trends for 2002-18 is found based on ROand radiosonde records, in approximate agreement withmoist adiabatic lapse rate theory. The consistency of trend results from the latest upper-air datasets will help to improve understanding of climate changes and their drivers. [ABSTRACT FROM AUTHOR]

The accurate prediction of plant phenology is of significant importance for more sustainable and effective land management. This research develops a framework of phenological modeling to estimate vegetation abundance [indicated by the normalized difference vegetation index (NDVI)] 7 days into the future in the geographically diverse Upper Colorado River basin (UCRB). This framework uses phenological regions (phenoregions) as the basic units of modeling to account for the spatially variant environment-vegetation relationships. The temporal variation of the relationships is accounted for via the identification of phenological phases. The modeling technique of Multivariate Adaptive Regression Splines (MARS) is employed and tested as an approach to construct enhanced predictive phenological models in each phenoregion using a comprehensive set of environmental drivers and factors. MARS has the ability to deal with a large number of independent variables and to approximate complex relationships. The R2 values of the models range from 91.62% to 97.22%. The root-mean-square error values of all models are close to their respective standard errors ranging from 0.016 to 0.035, as indicated by the results of cross and field validations. These demonstrate that the modeling framework ensures the accurate prediction of short-term vegetation abundance in regions with various environmental conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a regularized regression procedure for finding a predictive relation between one variable and a field of other variables. The procedure estimates a linear prediction model under the constraint that the regression coefficients have smooth spatial structure. The smoothness constraint is imposed using a novel approach based on the eigenvectors of the Laplace operator over the domain, which results in a constrained optimization problem equivalent to either ridge regression or least absolute shrinkage and selection operator (LASSO) regression, which can be solved by standard numerical software. In addition, this paper explores an unconventional procedure whereby regression models are estimated from dynamical model output and then verified against observations-the reverse of the traditional order. The methodology is illustrated by constructing statistical prediction models of summer Texas-area temperature based on concurrent Pacific sea surface temperature (SST). None of the regularized regression models have statistically significant skill when estimated from observations. In contrast, when estimated from dynamical model output, the regression models have skill with respect to dynamical model data because of the substantially larger sample size available from dynamical model output. In addition, the regression models estimated from dynamical model data can predict observed anomalies with significant skill, even though no observations were used directly to estimate the regression models. The results indicate that dynamical models had no significant skill because they could not accurately predict the SST itself, not because they could not capture realistic SST teleconnections. [ABSTRACT FROM AUTHOR]

Under stably stratified conditions, the dissipation rate « of turbulence kinetic energy (TKE) is related to the structure function parameter for temperature C2T, through the buoyancy frequency and the so-called mixing efficiency. A similar relationship does not exist for convective turbulence. In this paper, we propose an analytical expression relating « and C2T in the convective boundary layer (CBL), by taking into account the effects of nonlocal heat transport under convective conditions using the Deardorff countergradient model. Measurements using unmanned aerial vehicles (UAVs) equipped with high-frequency response sensors to measure velocity and temperature fluctuations obtained during the two field campaigns conducted at Shigaraki MU observatory in June 2016 and 2017 are used to test this relationship between « and C2T in the CBL. The selection of CBL cases for analysis was aided by auxiliary measurements from additional sensors (mainly radars), and these are described. Comparison with earlier results in the literature suggests that the proposed relationship works, if the countergradient term gD in the Deardorff model, which is proportional to the ratio of the variances of potential temperature u and vertical velocity w, is evaluated from in situ (airplane and UAV) observational data, but fails if evaluated from large-eddy simulation (LES) results. This appears to be caused by the tendency of the variance of u in the upper part of the CBL and at the bottom of the entrainment zone to be underestimated by LES relative to in situ measurements from UAVs and aircraft. We discuss this anomaly and explore reasons for it. [ABSTRACT FROM AUTHOR]

Realistic ocean state prediction and its validation rely on the availability of high quality in situ observations. To detect data errors, adequate quality check procedures must be designed. This paper presents procedures that take advantage of the ever-growing observation databases that provide climatological knowledge of the ocean variability in the neighborhood of an observation location. Local validity intervals are used to estimate binarily whether the observed values are considered as good or erroneous. Whereas a classical approach estimates validity bounds from first- and second-order moments of the climatological parameter distribution, that is, mean and variance, this work proposes to infer them directly from minimum and maximum observed values. Such an approach avoids any assumption of the parameter distribution such as unimodality, symmetry around the mean, peakedness, or homogeneous distribution tail height relative to distribution peak. To reach adequate statistical robustness, an extensive manual quality control of the reference dataset is critical. Once the data have been quality checked, the local minima and maxima reference fields are derived and the method is compared with the classical mean/variance-based approach. Performance is assessed in terms of statistics of good and bad detections. It is shown that the present size of the reference datasets allows the parameter estimates to reach a satisfactory robustness level to always make the method more efficient than the classical one. As expected, insufficient robustness persists in areas with an especially low number of samples and high variability. [ABSTRACT FROM AUTHOR]

In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information. [ABSTRACT FROM AUTHOR]

Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu). [ABSTRACT FROM AUTHOR]

This paper presents an analysis of changes in global land extreme temperature indices (1951-2015) based on the new global land surface daily air temperature dataset recently developed by the China Meteorological Administration (CMA). The linear trends of the gridpoint time series and global land mean time series were calculated by using a Mann--Kendall method that accounts for the lag-1 autocorrelation in the time series of annual extreme temperature indices. The results, which are generally consistent with previous studies, showed that the global land average annual and seasonal mean extreme temperature indices series all experienced significant long-term changes associated with warming, with cold threshold indices (frost days, icing days, cold nights, and cold days) decreasing, warm threshold indices (summer days, tropical nights, and warm days) increasing, and all absolute indices (TXx, TXn, TNx, and TNn) also increasing, over the last 65 years. The extreme temperature indices series based on daily minimum temperatures generally had a stronger and more significant trend than those based on daily maximum temperatures. The strongest warming occurred after the mid-1970s, and a few extreme temperature indices showed no significant trend over the period from 1951 to the mid-1970s.Most parts of the global land experienced significant warming trends over the period 1951-2015 as a whole, and the largest trends appeared in mid- to high latitudes of the Eurasian continent. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is the midtropospheric measurements made by the Microwave Sounding Unit (MSU) channel 2 and the Advanced Microwave Sounding Unit (AMSU) channel 5. Previous versions of the Remote Sensing Systems (RSS) dataset have used a diurnal climatology derived from general circulation model output to remove the effects of drifting local measurement time. This paper presents evidence that this previous method is not sufficiently accurate and presents several alternative methods to optimize these adjustments using information from the satellite measurements themselves. These are used to construct a number of candidate climate data records using measurements from 15 MSU and AMSU satellites. The new methods result in improved agreement between measurements made by different satellites at the same time. A method is chosen based on an optimized second harmonic adjustment to produce a new version of the RSS dataset, version 4.0. The new dataset shows substantially increased global-scale warming relative to the previous version of the dataset, particularly after 1998. The new dataset shows more warming than most other midtropospheric data records constructed from the same set of satellites. It is also shown that the new dataset is consistent with long-term changes in total column water vapor over the tropical oceans, lending support to its long-term accuracy. [ABSTRACT FROM AUTHOR]

This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network-Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT's higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations. [ABSTRACT FROM AUTHOR]

Exposure to weather extremes, such as heatwaves, can cause discomfort, harm, or death in grazing cattle in pastures. While the Australian Bureau of Meteorology issues sheep graziers alerts when there is an exposure risk to chill for livestock, there is no equivalent alert for heat stress for Australian cattle. Before any such alert system can be developed, a robust assessment and comparison of relevant cattle thermal stress indices is required. This study evaluates and compares the multiyear climatology of three cattle thermal heat stress indices across Australia in the warm season months (October–March). The same indices are then used to assess historical Australian heat events where cattle died from heat exposure. These events are based off official records and survey responses from northern Australian graziers. In the seven historical heat events studied, high relative humidity combined with low wind speeds, or high solar exposure combined with high surface temperatures, exacerbated the impact of heat stress on cattle. In the two historic events where multiple compounding weather factors combined (e.g., high humidity, low winds, and high solar exposure), the cattle mortality levels were significantly high. These events were characterized by rainy conditions followed by a rapid warming, meaning cattle were likely unable to acclimatize to such dramatic temperature changes. This study highlights the need for using more than one thermal stress index when verifying cattle heat stress events and, importantly, calls for further research on standardizing the risk classifications of these thermal indices for cattle in Australia's variable climate. Significance Statement: Cattle across Australia's northern tropical and semiarid regions often experience extreme hot and humid conditions in the summer months, which increases the risk of heat stress. This is the first study of its kind to evaluate observations of cattle heat stress across Australia using indices that describe the combined effects of solar exposure, wind speed, relative humidity, and surface temperatures. These cattle heat stress indices can be used to evaluate historical cattle mortality events in feedlots and in grazed pastures. This study lays the groundwork for the development of Australian-wide cattle heat stress forecast products on the 7-day to multiweek time scales. [ABSTRACT FROM AUTHOR]

The article comments on the article "Short-Term Precipitation and Temperature Trends along an Elevation Gradient in Northeastern Puerto Rico," which evaluates urban versus nonurban average temperature values, not about inferring about temperature trends. It mentions significant differences in temperature trends between urban and rural areas, after controlling for potential variability related to ecological life zones.

In studies of global warming, increases in tropospheric temperature as well as increases in surface temperature have attracted attention. Simulations of trends in these two temperatures appear to differ from trends in observations by surface sites, radiosondes, and satellites. Moreover, observation errors such as uncertainties in measurement precision and calibration, environmental changes, and the reorganization of network sites hamper the ability to quantify these influences on temperature trends. This paper presents multidecadal (1965–2016) trends in lower-tropospheric temperature for south-central Japan derived from thickness temperature, a measurement based on pressure data from exactly known altitudes at the summit of Mount Fuji (3776 m) and surrounding meteorological sites. The resulting trend is compared with the trends in surface temperature and in the temperature at 700 hPa measured by radiosonde. Although surface temperature increased faster than tropospheric (thickness) temperature in the study area for the 1965–2016 period, tropospheric temperature increased faster than surface temperature after 1985. Additionally, it is found that radiosonde data are not appropriate for determining the temperature trend at constant altitudes because atmospheric warming raises the altitude of the pressure levels. [ABSTRACT FROM AUTHOR]

The T5 expendable bathythermographs reach the greatest depth within the current XBT family. Since the early 1970s, in several areas they have been providing a significant part of available temperature profiles below 1000 m and therefore represent an important resource for ocean climate study. In this paper we present new results from laboratory tests of Sippican T5 and T5/20 probes and analyses of more than 350 XBT–CTD matched pairs from our own field trials and the World Ocean Database (WOD), and we propose an improved fall rate equation (coefficients: A = 6.720 ± 0.025 m s−1, B = 0.001 60 ± 0.000 15 m s−2, Offset = 1.00 ± 0.65 m). Possible influences of probe physical characteristics and initial launch conditions on the probe motion have also been investigated with launching height and probe weight being identified as important factors. Analyses also confirm that fall speed and pure temperature error increase with water temperature, as previously reported for other XBT types. The uncertainties in depth and temperature measurements are then calculated. Finally, a new correction for a global T5 dataset is proposed, with an update of the currently available schemes. [ABSTRACT FROM AUTHOR]

This paper presents an acoustic archival tag capable of tracking small marine animals. It is also a technology that can be ported to other platforms, such as the next-generation acoustic and Argo floats as well as gliders. Tracking is achieved by standard RAFOS triangulation using the arrival times of unique sound signals emitted by moored sources. At the core of the tag is a custom microchip that controls all system operations. It incorporates the critical acoustic arrival time detector, a thermal sensor, and a pressure sensor interface. All the electronic components are housed inside a cylindrical hydrophone of 25.4-mm length and 10.7-mm diameter. The collected data are archived in nonvolatile memory chips with a total capacity of 4 Mb, sufficient storage to record position, temperature, and pressure on an hourly basis for 2 years. The tag consumes 4-5 μW in standby mode and between 60 and 90 μWwhile the sound arrival time detector is in operation. The power is provided by two button cell silver-oxide batteries, which enable an active tag lifetime of approximately 2 years. [ABSTRACT FROM AUTHOR]

Indian Ocean (IO) stratification has important effects on the air–sea interaction, ocean dynamics, and ecology. It is, therefore, of significance to investigate the changes in IO stratification. In this study, we use ensemble empirical mode decomposition (EEMD) to extract the nonlinear long-term trend in the upper IO stratification quantified by potential energy anomalies. The results show that the strengthening of the stratification is spatially and temporally nonuniform. Specifically, the trend of stratification intensified gradually before 1996, but accelerated rapidly after 1996. Temperature and salinity changes play a crucial role in the fast enhancement of stratification and its regional differences. Temperature variations dominate the stratification trend in ∼90% of the IO area, while the contributions of salinity changes are mainly in the southeast Indian Ocean (SEIO). Vertically, the rapid enhancement of stratification is caused by the trend of temperature and salt in the upper 400 m. We further perform temperature budget analysis and find that the warming trend in the upper 400 m south of the IO is mainly modulated by vertical advection and meridional advection, while the warming in the north of the IO is mainly induced by air–sea heat fluxes. Salinity budget analysis shows that ocean advection has played a primary role in modulating SEIO salinity over the past 20 years. [ABSTRACT FROM AUTHOR]

Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA's monthly Global Historical Climatology Network (GHCNm) averages 0.02°C cooler between 1880 and 1940 than Berkeley Earth's and 0.14°C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA's GHCNm(V4), PHA0, we propose two revisions to account for autocorrelation in climate variables. One version, PHA1, makes minimal modification to PHA0 by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA2, uses penalized likelihood to detect breakpoints through optimizing a model-selection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA2. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA1 and PHA2 both similarly outperform PHA0 in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA1- and 50 PHA2-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880–2022 trend by 0.16 [0.12, 0.19]°C century−1 (95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]°C over 2010–22 relative to 1880–1900. Significance Statement: Accurately correcting for systematic errors in observational records of land surface air temperature (LSAT) is critical for quantifying historical warming. Existing LSAT estimates are subject to systematic offsets associated with processes including changes in instrumentation and station movement. This study improves a pairwise homogenization algorithm by accounting for the fact that climate signals are correlated over time. The revised algorithms outperform the original in identifying discontinuities and recovering accurate warming trends. Applied to monthly station temperatures, the revised algorithms adjust trends in continental mean LSAT since the 1880s to be 0.16°C century−1 greater relative to raw data. Our estimate is most consistent with that from Berkeley Earth and indicates lesser and greater warming than estimates from NOAA and the Met Office, respectively. [ABSTRACT FROM AUTHOR]

Using the ERA5 reanalysis data and the Climate Feedback Response Analysis Method (CFRAM), we attribute the mechanism of summer upper-tropospheric temperature (UTT) variations in the South Asian summer monsoon (SASM) region to several external forcing and internal feedback processes. The summer UTT in the SASM region is dominated by two modes. The first empirical orthogonal function (EOF) mode (EOF1) is a monopolar warming pattern, and the second EOF mode (EOF2) shows a meridional dipole pattern. CFRAM results show that summer UTT anomalies are mainly attributed to cloud feedback and nonradiative processes of atmospheric dynamics (ATD) and surface-related processes. For EOF1, ocean heat storage and partial cloud feedback processes contribute most UTT anomalies over the Indian Ocean. The ATD increases the UTT over East Asia through the adiabatic warming caused by anomalous anticyclone in the upper troposphere. The formation of EOF2 is closely linked to the ATD, while the cloud process partially compensates for the excessive changes in UTT caused by the ATD. The South Asian high and its circulation in the midlatitude region are significantly enhanced. The anomalous anticyclone over northern East Asia along with the anomalous easterly wind on the south side of the South Asian high favors increased warm advection and adiabatic heating, contributing to the warming of UTT. Meanwhile, adiabatic cooling resulting from the atmospheric ascent in the middle and upper troposphere leads to UTT cooling over the Indian Ocean. The quantitative attribution of UTT has great implications for better understanding future SASM variation. Significance Statement: The purpose of this study is to understand the physical mechanisms of upper-tropospheric temperature (UTT) changes in the South Asian summer monsoon (SASM). Although previous studies have examined temperature variation from the perspective of atmospheric circulation, there has been limited investigation into the influence of various feedback processes. Our study reveals that the summer UTT in the SASM region is dominated by monopolar and meridional dipole modes. Utilizing a climate feedback–response analysis method, we attribute the UTT anomalies in the SASM region to the cloud feedback, oceanic heat storage, and atmospheric dynamics processes, and explore the physical mechanisms of their effects. These results have important implications for the better prediction of monsoon variability. [ABSTRACT FROM AUTHOR]

Large temperature variations in a temperate climate, particularly in late winter and early spring, can be disruptive for native ecosystems and agricultural crops. As warmer temperatures occur earlier in the year in midlatitude regions as a result of anthropogenic climate change, springtime temperatures may become less consistent, leading to potential damage to species and crops that are vulnerable to the return of historically cooler temperatures, including late-spring frosts, after an initial warm-up. In this work, we quantify shifting patterns in late-winter and springtime temperature variations at eight sites across New Jersey from 1950 to 2019. Many sites located along the coast or in the coastal plain experience increases in the number of times the temperature climbs above 15.5°C (60°F) and then falls below freezing (i.e.,0°C, or 32°F). Sites in southern New Jersey (where much of the state's agriculture is located) experience the most significant (P < 0.05) increases in large springtime temperature variations. Across all sites, there is a general increase in both the percentage and magnitude of temperature variations that occur as early as February. At 75% of sites, day-to-day variation in daily maximum temperature has increased from the 1950s through 2019; day-to-day variation in daily minimum temperatures has increased over the same time at more than half of sites considered. These amplifications in extreme temperature variations indicate the need for both mitigation and adaptation strategies to protect vulnerable crops and ecosystems in the region during this critical time of the year. Significance Statement: Human-caused climate change has made it more likely for warmer temperatures to occur earlier in the year, causing many locations to experience late-winter and early-springtime temperatures that are less consistent than they may have been in the past. These variations can be highly problematic for both vital agricultural crops and critical ecosystems. Here, we evaluate how late-winter and early-springtime temperatures have changed throughout New Jersey (home to a variety of agriculture and unique ecosystems) from the mid-twentieth century until 2019. We find critical changes to temperature patterns during late winter and early spring, including larger and more frequent temperature swings (particularly in February) and increased day-to-day variation in high and low temperatures. [ABSTRACT FROM AUTHOR]

This paper demonstrates that an operational forecast model can skillfully predict week-3-4 averages of temperature and precipitation over the contiguous United States. This skill is demonstrated at the gridpoint level (about 1° × 1°) by decomposing temperature and precipitation anomalies in terms of an orthogonal set of patterns that can be ordered by a measure of length scale and then showing that many of the resulting components are predictable and can be predicted in observations with statistically significant skill. The statistical significance of predictability and skill are assessed using a permutation test that accounts for serial correlation. Skill is detected based on correlation measures but not based on mean square error measures, indicating that an amplitude correction is necessary for skill. The statistical characteristics of predictability are further clarified by finding linear combinations of components that maximize predictability. The forecast model analyzed here is version 2 of the Climate Forecast System (CFSv2), and the variables considered are temperature and precipitation over the contiguous United States during January and July. A 4-day lagged ensemble, comprising 16 ensemble members, is used. The most predictable components of winter temperature and precipitation are related to ENSO, and other predictable components of winter precipitation are shown to be related to the Madden-Julian oscillation. These results establish a scientific basis for making week-3-4 weather and climate predictions. [ABSTRACT FROM AUTHOR]

Most of the globe has experienced significant warming trends that have been attributed to anthropogenic climate change. However, these rates of warming are also influenced by short-term climate fluctuations driven by atmospheric circulation dynamics, resulting in inconsistent trend magnitudes in both time and space. This research evaluated winter (December-February) temperature trends over 1950-2020 at 91 climate stations across British Columbia (BC), Alberta (AB), and Saskatchewan (SK), Canada, and determined the components attributed to thermodynamic and dynamic (atmospheric circulation) factors. A synoptic climatological approach was used to classify atmospheric circulation patterns in the midtroposphere, relate those patterns to surface temperature, and evaluate changes in frequency. Moderate to high temperature increases over 71 years were found for most of the region, averaging 3.18C in southern SK to 4.1℃ in central-northern AB, and a maximum of 5.8℃ in northern BC. Low to moderate increases were found for southern BC, averaging 1.2℃. Changes in atmospheric circulation accounted for 29% and 31% of observed temperature changes in central-northern BC and AB, respectively. Dynamic factors were a moderate driver in southern AB (18%) and central-northern SK (13%), and low in southern SK (5%). Negative dynamic contributions in southern BC (-6%), suggest that atmospheric circulation changes counteracted thermodynamically driven temperature changes. Results were consistent with trend analyses, indicating this method is well suited for trend detection and identification of thermodynamic and dynamic drivers. Results of this research improve our understanding of the magnitude of winter temperature changes critical for informing adaptation and climate-related policy decisions. [ABSTRACT FROM AUTHOR]

The seasonal cycle in temperature is a large and well-observed response to radiative forcing, suggesting its potential as a natural analog to human-caused climate change. Although there have been advances constraining some climate feedback parameters using seasonal observations, the seasonal cycle has not been used to inform about the local temperature sensitivity to greenhouse gas forcing. In this study, we uncover a nonlinear relationship between the amplitude and phase of the seasonal cycle and forced temperature trends in seven CMIP5-era large ensembles across the Northern Hemisphere extratropical continents. We develop a mixture energy balance model that reproduces this relationship and reveals the unexpected finding that the phasing of the seasonal cycle-in addition to the amplitude-contains information about local temperature sensitivity to seasonal forcing over land. Using this energy balance model framework, we compare the pattern and magnitude of the seasonally inferred sensitivity of the surface temperature response to anthropogenic radiative forcing. The seasonally constrained model largely reproduces the pattern of human-caused temperature trends seen in climate models (r = 0.81, p value < 0.01), including polar amplification, but the magnitude of the response is smaller by about a factor of 3. Our results show the relevance of both phasing and amplitude for constraining patterns of local feedbacks and suggest the utility of additional research to better understand the differences in sensitivity between seasonal and greenhouse gas forcing. [ABSTRACT FROM AUTHOR]

Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection–Cloud Chamber generates a turbulent cloud with a microphysical steady state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and collocated Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to Da ≲ 1 , i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that Da ≲ 1 is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth. Significance Statement: The saturation ratio (humidity) in clouds controls the growth rate and formation of cloud droplets. When air in a turbulent cloud mixes, the humidity varies in space and time throughout the cloud. This is important because it means cloud droplets experience different growth histories, thereby resulting in broader size distributions. It is often assumed that growth and evaporation of cloud droplets buffers out some of the humidity variations. Measuring these variations has been difficult, especially in the field. The purpose of this study is to measure the saturation ratio distribution in clouds with a range of conditions. We measure the in-cloud saturation ratio using a convection cloud chamber with clean to polluted cloud properties. We found in clouds with low concentrations of droplets that the variations in the saturation ratio are not suppressed. [ABSTRACT FROM AUTHOR]

Because they are rare, extreme weather events have critical impacts on societies and ecosystems and attract public and scientific attention. The most unusual events are regularly documented as part of routine climate monitoring by meteorological services. A growing number of attribution studies also aim at quantifying how their probability has evolved under human-induced climate change. However, it is often recognized that (i) the selection of studied events is geographically uneven, and (ii) the definition of a given event, in particular, its spatiotemporal scale, is subjective, which may impact attribution statements. Here we present an original method that objectively selects, defines, and compares extreme events that have occurred worldwide in the recent years. Building on previous work, the event definition consists of automatically selecting the spatiotemporal scale that maximizes the event rarity, accounting for the nonstationary context of climate change. We then explore all years, seasons, and regions and search for the most extreme events. We demonstrate how our searching procedure can be both useful for climate monitoring over a given territory, and resolve the geographical selection bias of attribution studies. Ultimately, we provide a selection of the most exceptional hot and cold events in the recent past, among which are iconic heatwaves such as those seen in 2021 in Canada and in 2003 in Europe. [ABSTRACT FROM AUTHOR]

Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning–based system for nowcasting the probability of subfreezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of subfreezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system. Significance Statement: The purpose of this study is to better understand the strengths and weaknesses of a system that predicts the probability of subfreezing road surface temperatures. We found that the system performed well in general, but underpredicted the probabilities when frozen precipitation was predicted to reach the surface. These biases were substantially improved by modifying the system to increase its focus on situations with falling precipitation. The updated system should allow for improved monitoring and forecasting of potentially hazardous conditions during winter storms. [ABSTRACT FROM AUTHOR]

Vertical temperature profiles influence the wind power generation of large offshore wind farms through stability-dependent effects such as blockage and gravity waves. However, numerical tools that are used to model these effects are often computationally too expensive to cover the large variety of atmospheric states occurring over time. Generally, an informed decision about which representative nonidealized situations to simulate is missing because of the lack of easily available information on representative vertical profiles, taking into account their spatiotemporal variability. Therefore, we present a novel framework that allows a smart selection of vertical temperature profiles. The framework consists of an improved analytical temperature model for the atmospheric boundary layer and lower troposphere, a subsequent clustering of these profiles to identify representatives, and last, a determination of areas with similar spatiotemporal characteristics of vertical profiles. When applying this framework on European ERA5 data, physically realistic representatives were identified for Europe, excluding the Mediterranean. Two or three profiles were found to be dominant for the open ocean, whereas more profiles prevail for land. Over the open ocean, weak temperature gradients in the boundary layer and a clear capping inversions are widespread, and stable profiles are absent except in the region of the East Icelandic Current. Interestingly, according to the ERA5 data, at its resolution, coastal areas and seas surrounded by land behave more similar to the land areas than to the open ocean, implying that a larger set of model integrations are needed for these areas to obtain representative results for offshore wind power assessments in comparison with the open ocean. SIGNIFICANCE STATEMENT: Numerical tools used to simulate the effect of large, offshore wind farms on neighboring farms and the atmosphere are very expensive. Therefore, they can only be computed for a limited number of cases. As temperature is one of the most important parameters in these kinds of simulations, this work provides a new vertical temperature model and an analysis framework that allows for a smart selection of these cases such that they ideally represent the full variation of the atmosphere's temperature profiles. [ABSTRACT FROM AUTHOR]

This paper introduces a quality control (QC) program for the real-time hourly land surface temperature observation developed by the Central Weather Bureau in Taiwan. There are three strategies involved. The first strategy is a range check scheme that inspects whether the observation falls inside the climatological limits of the station to screen out the obvious outliers. Limits are adjusted according to the station's elevation. The second strategy is a spatial check scheme that scrutinizes whether the observation falls inside the derived confidence interval, according to the data from the reference stations and the correlations among the stations, to judge the reliability of the data. The scheme is specialized, as it employs the theorems of unbiased and minimum error estimators to determine the weights. The performance evaluation results show that the new method is in theory superior to the spatial regression test (You et al.). The third strategy is a temporal check scheme that examines whether the temperature difference of two successive observations exceeds the temperature variation threshold for judging the rationality of the data. Different thresholds are applied for the data observed in different times under different rainfall conditions. Procedurally, the observation must pass the range check first and then go through the spatial or the temporal check. The temporal check is applied only when the spatial check is unavailable. Post-examinations of the data from 2014 show that the QC program is able to filter out most of the significant errors. [ABSTRACT FROM AUTHOR]

Previous studies have shown strong negative correlation between multidecadal signatures in length of day (LOD)-an inverse measure of Earth's rotational rate-and various climate indices. Mechanisms remain elusive. Climate processes are insufficient to explain observed rotational variability, leading many to hypothesize external (astronomical) forcing as a common source for observed low-frequency signatures. Here, an internal source, a core-to-climate, one-way chain of causality, is hypothesized. To test hypothesis feasibility, a recently published, model-estimated forced component is removed from an observed dataset of Northern Hemisphere (NH) surface temperatures to isolate the intrinsic component of climate variability, enhancing its comparison with LOD. To further explore the rotational connection to climate indices, the LOD anomaly record is compared with sea surface temperatures (SSTs)-global and regional. Because climate variability is most intensely expressed in the North Atlantic sector, LOD is compared to the dominant oceanic pattern there-the Atlantic multidecadal oscillation (AMO). Results reveal that the LOD-related signal is more global than regional, being greater in the global SST record than in the AMO or in global-mean (land + ocean) or land-only surface temperatures. Furthermore, the strong (4σ) correlation of LOD with the estimated NH intrinsic component is consistent with the view proffered here, one of an internally generated, core-to-climate process imprinted on both the climate and Earth's rotational rate. While the exact mechanism is not elucidated by this study's results, reported correlations of geomagnetic and volcanic activity with LOD offer prospects to explain observations in the context of a core-to-climate chain of causality. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of the NCAR Community Atmosphere Model, version 4 (CAM4), in simulating observed annual extremes of near-surface temperature and provides the first assessment of the impact of stochastic parameterizations of subgrid-scale processes on such performance. Two stochastic parameterizations are examined: the stochastic kinetic energy backscatter scheme and the stochastically perturbed parameterization tendency scheme. Temperature extremes are described in terms of 20-yr return levels and compared to those estimated from ERA-Interim and the Hadley Centre Global Climate Extremes Index 2 (HadEX2) observational dataset. CAM4 overestimates warm and cold extremes over land regions, particularly over the Northern Hemisphere, when compared against reanalysis. Similar spatial patterns, though less spatially coherent, emerge relative to HadEX2. The addition of a stochastic parameterization generally produces a warming of both warm and cold extremes relative to the unperturbed configuration; however, neither of the proposed parameterizations meaningfully reduces the biases in the simulated temperature extremes of CAM4. Adjusting warm and cold extremes by mean conditions in the respective annual extremes leads to good agreement between the models and reanalysis; however, adjusting for the bias in mean temperature does not help to reduce the observed discrepancies. Based on the behavior of the annual extremes, this study concludes that the distribution of temperature in CAM4 exhibits too much variability relative to that of reanalysis, while the stochastic parameterizations introduce a systematic bias in its mean rather than alter its variability. [ABSTRACT FROM AUTHOR]