Values Of Surface Air Temperature Research Articles

City-Wise Assessment of Suitable CMIP6 GCM in Simulating Different Urban Meteorological Variables over Major Cities in Indonesia

This study evaluates the performance of 6 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating temperature, precipitation, wind speed, and relative humidity over 29 cities in Indonesia. Modern-Era Retrospective Analysis for Research Applications (MERRA-2) was considered as reference data to assess the city-wise performance of surface air temperature, precipitation, wind speed, and relative humidity simulated by the CMIP6 GCMs during 1980–2014. Six statistical measures were computed in this process (mean annual, seasonal amplitude, mean annual bias, root mean square error, correlation coefficient, and standard deviation). For 29 cities, the mean annual values of surface air temperature, precipitation, wind speed, and relative humidity obtained from the GCMs range between 290 to 302 K, 100 cm to 450 cm, 1 to 6 m/s, and 70 to 94%, respectively. The correlation coefficient between the GCMs and the surface air temperature (precipitation) reanalysis dataset ranges from 0.3 to 0.85 (−0.14 to 0.77). The correlation coefficient for wind speed (relative humidity) varies from 0.2 to 0.6 and is positive in some cases (0.2 to 0.8). Subsequently, the relative error that combines the statistical measurement results was calculated for each city and meteorological variable. Results show that for surface air temperature and precipitation, the performance of TaiESM was outstanding over the 10 or more cities. In contrast, for wind speed and relative humidity, NOR-MM and MPI-HR were the best over 7 and 19 cities, respectively. For all the meteorological variables, the performance of AWI was found to be worst over all the cities. The outcomes of this study are essential for climate-resilience planning and GCM selection while performing downscaling experiments. It will also be useful for producing updated national climate change projections for each city in Indonesia and providing new insights into the climate system.

Estimation of tendencies, homogeneity and stationarity of air temperature at the Ukrainian Antarctic Akademik Vernadsky station during 1951—2020

In this paper results of the complex analysis of surface air temperature tendencies investigations at the Ukrainian Antarctic Akademik Vernadsky station are represented. Antarctica is a region that has a high rate of surface air temperature increase. The Antarctic Peninsula has experienced particularly fast warming, which has the highest temperature rise in the Southern Hemisphere. Therefore, in Antarctica, the study of surface air temperature change is important.
 The Ukrainian Antarctic Akademik Vernadsky station is located on Galindez Island near the Antarctic Peninsula. Investigation of the surface air temperature is especially relevant to the Akademik Vernadsky station, because it has difficult conditions for its formation. The research goal is the estimation of tendencies, homogeneity and stationarity of the annual and mean monthly values of surface air temperature at the Ukrainian Antarctic Akademik Vernadsky station based on a combined approach with the use of several statistical and graphical methods. The use of various statistical methods that differ in characteristics (sensitivity to the law of distribution, autocorrelation, etc.) allows obtaining more reliable estimates. Graphic methods give an opportunity to analyze the tendencies over time and its change periods, the cyclical fluctuations and their characteristics (phases of increase and decrease, their duration, synchronicity, in-phase). Therefore, 4 statistical tests (standard normal Alexandersson test, Buishand test, Pettitt test, von Neumann relation) and 2 graphical methods (mass curve and residual mass curve) were used in the study.
 At the Ukrainian Antarctic Akademik Vernadsky station, the observation series of the mean annual air temperature are quasi-homogeneous and quasi-stationary, as it has only a cooling phase and a warming phase of long-term cyclical fluctuations, which are also unfinished. The transition from the cooling phase to the warming phase took place in 1982. Tendencies in mean monthly air temperatures are similar to tendencies in mean annual temperatures. The differences are only for some months, namely, for the period from September to December.

Understanding convective storms in a tropical, high-altitude location with in-situ meteorological observations and GPS-derived water vapor

We investigate convective storms over the Sabana de Bogotá, a high-altitude and densely populated area in the Colombian tropical Andes. Convective events are identified using infrared satellite images and in-situ precipitation data. As expected, convection shows a strong early-afternoon peak during the two rainy seasons. Previous studies hypothesize that early-afternoon westerly winds and their moisture advection from the warmer Magdalena Valley are the main explanatory mechanism for intense storms. We find that early-afternoon westerlies are present in 78% of the rainy season days, but convective events develop in only 26% of the days. Thus, although westerlies seem necessary for convection due to the convergence they generate, they only occasionally generate storms, and are therefore not a good predictor. Furthermore, reanalysis data indicate that precipitable water vapor (PWV) at the Magdalena Valley is anomalously low during convective days, suggesting that moisture converges locally instead of being advected from the west. Based on composites of surface wind speed, air temperature, pressure and GPS-derived PWV, we identify the most prominent signals associated to deep convection: a weaker than average wind speed throughout the morning, higher than normal values of surface air temperature towards noon, followed by an anomalous steep increase of PWV and wind speed. These features indicate that convection results from a strong diurnal forcing facilitated by convergence of westerly winds, combined with sufficient water vapor convergence, with a timescale of about 3 hours. This highlights the relevance of high temporal resolution monitoring of PWV offered by Global Navigational Satellite System stations.

Characterization of the summer surface mesoscale dynamics at Dome F, Antarctica

This article characterizes the mesoscale surface air temperature (SAT) gradients around the Dome F on the Antarctic Plateau combining ERA5 reanalysis outputs and in-situ weather observations. For this we took advantage of a mobile automatic weather station (M-AWS) that allowed us to record meteorological observations in interesting areas not covered by other AWS. We found that night-time SAT gradients are very variable from night to night. ERA5 does not adequately represent thermal gradients and their daily changes and tends to underestimate them. In particular, it fails to reproduce the cold pool observed by M-AWS over the depressed areas of the terrain. The performance of ERA5 over the plateau is better when observed SAT gradients are weak. Besides, we observed surface meso-β eddies with warm cores and horizontal gradients of more than 5 °C 100 km−1 simulated by ERA5 that have implications for site selection to establish new telescopes for astronomical observations. This study helps to interpret the daily performance of SAT values provided by reanalyisis on the Antarctic Plateau and complements climatological evaluations of SAT in the region. Finally, this study raises the necessity to increase in-situ weather observations, not only on the ridges of the Antarctic plateau where the majority are located, but also in the areas of depression to have a better picture of the weather and climate of the plateau.

Integrating Pre-Earthquake Signatures From Different Precursor Tools

Potential earthquake precursors include, among others, electromagnetic fields, gas emissions, Land Surface Temperature (LST), Sea Surface Temperature (SST), and Surface Air Temperature (SAT) anomalies. These observables have been individually studied, before earthquakes, by many researchers. The ionospheric studies concerning earthquakes (EQs) using magnetic data from Low Earth Orbit (LEO) satellites are increasingly being used to detect ionospheric anomalies before large EQs. Also, LST, SST, and SAT values retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) are considered as physical precursors before EQs. In this work, we jointly analyze magnetic, MODIS, and MERRA-2 data in space and time around the epicenters before the selected EQs in Mexico, Japan, Chile, and Indonesia. Our analyses present interesting findings where anomalies in temperature and magnetic field, preceding the considered EQs, are confirmed through different methods. Particularly, we utilize the Fast Fourier Transform (FFT) and the Discrete Cosine Transform (DCT) for analyzing magnetic data over the designated EQs regions. We use the magnetic data acquired by Swarm satellites in the top side ionosphere along with MODIS and MERRA-2. Five case studies are described to prove the effectiveness of our analyses. Precursory anomalies were observed using these methods in different anomalous days from the considered four regions of interest around the epicenter. It is concluded that these methods could be effective and reliable in detecting anomalies preceding the upcoming EQs.

Air temperature changes in the Arctic in the period 1951\u20132015 in the light of observational and reanalysis data

Recent air temperature changes in the high Arctic (HA) have been investigated based on mean seasonal and annual data calculated for the period 1951–2015 and for two sub-periods 1976–2015 and 1996–2015. Two kinds of air temperature data (observational and reanalysis) have been used in the research. The observational data were compared with data taken from six reanalysis products (20CRv2c, CERA-20C, ERA-Int, MERRA-2, NCEP-CFSRR, JRA-55). The scale of the HA warming for the period 1996–2015 relative to the reference period 1951–1990 reached 1.6 °C for annual mean and was greatest in autumn (1.9 °C) and in winter (1.7 °C), while it was smallest in summer (0.9 °C). Evidently, the greatest warming was observed in the Atlantic and Siberian climatic regions, while in the rest of the HA, the rate of warming was usually weaker than trends calculated for the period 1976–2015. Air temperature tendencies in all study periods 1951–2015, 1976–2015 and 1996–2015 showed a predominance of positive trends that were statistically significant at the level of 0.05. In the two latter periods, the rate of warming was on average 2–3 times faster than for the entire study period. In the HA, there has not been a slowdown in the rate of warming (“hiatus”) in the last two decades (in contrast to that which was noted for the Northern Hemisphere). Our results reveal that, in most cases, the closest fit to observations was obtained for two reanalysis products (the ERA-Interim and JRA-55, since 1979) and the six reanalysis average. Two new polar amplification (PA) metrics based on scaled and standardised values of surface air temperature (SAT) reveal the non-existence of this phenomenon in the period 1951–2015. One of the metrics shows very small PA in the periods 1976–2015 and 1996–2015.

An efficient surface energy–mass balance model for snow and ice

Abstract. A comprehensive understanding of the state and dynamics of the land cryosphere and associated sea level rise is not possible without taking into consideration the intrinsic timescales of the continental ice sheets. At the same time, the ice sheet mass balance is the result of seasonal variations in the meteorological conditions. Simulations of the coupled climate–ice-sheet system thus face the dilemma of skillfully resolving short-lived phenomena, while also being computationally fast enough to run over tens of thousands of years. As a possible solution, we present the BErgen Snow SImulator (BESSI), a surface energy and mass balance model that achieves computational efficiency while simulating all surface and internal fluxes of heat and mass explicitly, based on physical first principles. In its current configuration it covers most land areas of the Northern Hemisphere. Input data are daily values of surface air temperature, total precipitation, and shortwave radiation. The model is calibrated using present-day observations of Greenland firn temperature, cumulative Greenland mass changes, and monthly snow extent over the entire domain. The results of the calibrated simulations are then discussed. Finally, as a first application of the model and to illustrate its numerical efficiency, we present the results of a large ensemble of simulations to assess the model's sensitivity to variations in temperature and precipitation.

Multi-annual hydro-climatic trends in the Dunajec Basin (Polish Carpathians)

Owing to changing climate conditions, this study analyses potential hydro-climatic trends for the most recent 60 years (1956–2015). The study area is situated in the mountain basin of the Dunajec River in the Polish Carpathians. The focus of the study was to evaluate multi-annual trends in mean monthly river flow for each season of the year as well as to compare the direction of these trends with potential trends in seasonal values of surface air temperature and precipitation. An increase in minimum flow(p<0.05) during the winter was identified for sites located along the upper and middle course of the Dunajec River, while no trends in winter flow were found at the studied lower-course site. An increase in spring flow is significant (p<0.1, weak evidence) at the upper-course site only. Moreover, an increase in maximum flow was found for autumn at the studied middle-course and downstream sites (p<0.05 and p<0.1, respectively). Increasing trends in mean and maximum air temperature (p<0.005) were identified for the spring and summer, together with an increase in minimum summer air temperature (p<0.005). Precipitation shows an increasing tendency in maximum values for spring, but a decrease in minimum values for summer (p<0.05), as well as a decrease in maximum values for winter (p<0.1). Overall, the revealed trends imply discernible, multi-annual changes in hydro-climatic conditions for the studied mountain basin.

Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature

Surface air temperature (SAT) is a critical metric that is used to assess regional warming and cooling patterns, and maximum and minimum SATs are required to evaluate the model predictions of climate extremes. Since station SAT data are irregularly distributed, land surface temperature (LST) values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data are used to estimate regional SAT by using linear regression methods. The deviations between SAT and LST are largely dependent on space and time, which hampers the estimation of linear regression, especially for the maximum SAT. To obtain accurate regional SAT estimates, a three-stage hierarchical Bayesian (HB) model is proposed that incorporates the MODIS LSTs as model covariates and specifies the deviations with structured dependence of MODIS LST fields. Sampling of model parameters and estimation of SAT values are implemented under the Bayesian paradigm using a Markov Chain Monte Carlo algorithm. Sensitivity analyses involving various model configurations and running processes are discussed to help build a robust HB model. The model's performance is evaluated using station measurements that are not used in the modeling process, with RMSEs of 2.15 K (0.75%) and 1.97 K (0.73%) for monthly maximum and minimum SATs, respectively. The evaluation indicates that HB modeling is an effective method to estimate SAT from MODIS LST. The verified HB model with the covariate inputs of both MODIS daytime and nighttime LSTs is used to reproduce monthly maximum and minimum SATs that are spatially continuous over the Qinghai province in Northwestern China for 2003–2011. From the comparison between MODIS LST and HB-estimated SAT, it is found that the spatial structure and warming patterns of LST and SAT show significant distinctions, implying that they cannot be substituted for one another when assessing the regional warming trends. The spatial heterogeneity of HB model estimation is able to provide thorough insights into regional SAT status changes that could otherwise be biased by station deployment.

Long-term air temperature variability on the Crimean Peninsula

A comparison is made of the calculated values of solar radiation incident on the upper atmospheric boundary with the measured values of surface temperature on the territory of the Crimean Peninsula. It is shown that the long-term temperature regime on the territory of the Crimean Peninsula is characterized by a stability. It is determined that the stability of the long-term regime of mean annual surface air temperatures is associated with the characteristics of the latitudinal distribution of solar radiation incident on the upper atmospheric boundary. The incident solar radiation increases in the regions of heat sources and decreases in the regions of heat sink. Stability of long-term mean annual values of surface air temperature is associated with the location of the Crimea on the boundary of the regions of heat sources and sinks. The study revealed the chronological structure of long-term changes in surface air temperature. The anomaly in the long-term surface air temperature variability is characterized by short-duration variations. An analysis is made of the chronological structure of interannual variability in surface air temperature on the territory of the peninsula. The dominant interannual and 2–3-year periodicities in the temperature regime variations are correlated with variations in incident solar radiation. In 62.7% of cases, the sign of interannual variability in surface air temperature corresponds to the sign of interannual variability in incident solar radiation. Thus it is shown that a small tendency in the long-term surface air temperature variability on the territory of the Crimean Peninsula, and the characteristics of its variations are determined largely by the specific character of the input and distribution of solar radiation incident on the upper atmospheric boundary.

Modulations of aerosol impacts on cloud microphysics induced by the warm Kuroshio Current under the East Asian winter monsoon

AbstractIn our previous aircraft observations, the possible influence of high sea surface temperature (SST) along the Kuroshio Current on aerosol‐cloud interactions over the western North Pacific was revealed. The cloud droplet number concentration (Nc) was found to increase with decreasing near‐surface static stability (NSS), which was evaluated locally as the difference between the SST and surface air temperature (SAT). To explore the spatial and temporal extent to which this warm SST influence can be operative, the present study analyzed Nc values estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite measurements. The comparison of the local Nc values between the high and low SST − SAT days revealed a marked increase in Nc (up to a factor of 1.8) along the Kuroshio Current in the southern East China Sea, where particularly high SST − SAT values (up to 8 K) were observed in winter under monsoonal cold air outflows from the Asian Continent. This cold airflow destabilizes the atmospheric boundary layer, which leads to enhanced updraft velocities within the well‐developed mixed layer and thus greater Nc. The monsoonal northwesterlies also bring a large amount of anthropogenic aerosols from the Asian continent that increase Nc in the first place. These results suggest that the same modulations of cloud microphysics can occur over other warm western boundary currents, including the Gulf Stream, under polluted cool continental airflows. Possibilities of influencing the cloud liquid water path are also discussed.

Performance of ALADIN-Climate/CZ over the area of the Czech Republic in comparison with ENSEMBLES regional climate models

Nowadays Regional Climate Models (RCMs) are increasingly used for downscaling of information from the coarse resolution of global climate models (GCMs) and they represent a more and more popular tool for assessment of future climate changes and their impacts at regional scales. In spite of continual progress of RCMs, their outputs still suffer from many uncertainties and biases. Therefore, it is necessary to assess their ability to simulate observed climate characteristics and uncertainties in their outputs before they are applied in subsequent studies. In the present study, the assessment of RCM performance in simulating climate in the reference period of 1961–1990 over the area of Czech Republic is presented. Furthermore, we focused on the intercomparison of the models’ results, mainly on the comparison of the Czech model ALADIN-Climate/CZ with outputs of other RCMs. Simulation of ALADIN-Climate/CZ in 25-km horizontal resolution, and thirteen RCM simulations from the ENSEMBLES project were assessed. Attention was paid especially to comparison of simulated and observed spatial and temporal variability of several climatic variables. The monthly and seasonal values of surface air temperature, precipitation totals and relative humidity were examined for evaluation of temporal variability and 30-year seasonal and monthly values with respect to spatial variability. Climate model performance was evaluated in several ways, namely by boxplots, maps of variability characteristics, skill scores based on mean square error and Taylor diagrams. Model errors detected by model evaluation depend on many factors (e.g. considered variables and their characteristics, area of analysis, time scale of interest and the method of assessment). On the basis of incorporated performance criteria model ALADIN-Climate/CZ belonged to a better group of RCMs in most cases. However, it was definitely the worst in simulating spring monthly means of air temperature and relative humidity in all seasons.

Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble

Abstract About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.

Joint multifractal description of the relationship between wind patterns and land surface air temperature

Land surface air temperature (SAT), registered at 1.25–2m above the ground, is influenced by wind patterns. As a consequence, some phenomena such as urban heat islands and the formation of ground-level air pollutants are affected. Detailed understanding of the effects of wind circulations on SAT is convenient to improve the knowledge of these phenomena. Thus, the joint multifractal analysis has been applied to describe time series recorded at Cordoba (southern Spain) from 2001 to 2008 revealing the presence of seasonal patterns related with warm and cold winds blowing from the SW (later spring and summer) and NE (later autumn and winter), respectively, that provoke different heterogeneity in SAT values. In addition, the extreme high SAT values seem to be related with summer SW winds. However, the presence of rare low SAT values produced by NE winds is less relevant.

Spatiotemporal modeling of monthly precipitation in the upper Shiyang River watershed in west central Gansu, northwest China

This paper outlines a methodology to estimate monthly precipitation surfaces at 1-km resolution for the Upper Shiyang River watershed (USRW) in northwest China. Generation of precipitation maps is based on the application of a four-variable genetic algorithm (GA) trained on 10 years of weather and ancillary data, i.e., surface air temperature, relative humidity, Digital Elevation Model-derived estimates of elevation, and time of year collected at 29 weather stations in west-central Gansu and northern Qinghai province. An observed-to-GA predicted data comparison of 10 years of precipitation collected at the 29 weather stations showed that about 84% of the variability in observed values could be explained by the trained GA, including variability in two independent datasets. Point-comparisons of observed and modeled precipitation along an elevation-rainfall gradient demonstrated near-similar spatiotemporal patterns. A precipitation surface for USRW for July, 2005, was developed with the trained GA and input surfaces of surface air temperature and relative humidity generated from Moderate Resolution Imaging Spectroradiometer sensor (MODIS) products of land surface temperature. Spatial tendencies in predicted maximum and minimum values of surface air temperature, relative humidity, and precipitation within a 2-km radius circle around selected weather stations were in close agreement with the values measured at the weather stations.

Simulation of the El Niño Southern Oscillation: Results from the Coupled Model Intercomparison Project

The El Nino Southern Oscillation (ENSO) simulations of 17 global coupled ocean–atmosphere general circulation models (CGCMs) from the Coupled Model Intercomparison Project (CMIP) are analyzed. Monthly mean values of surface air temperature, sea-level pressure and precipitation for 80 years are analyzed from control runs. Nearly half of the models studied do not employ any form of flux correction. Flux-corrected CGCMs were better at simulating the base state and annual cycle of the temperature field in the equatorial Pacific but not necessarily better at simulating ENSO variability. While a subset of the models produce realistic amplitudes of NINO3 and the Southern Oscillation Index (SOI), ENSO often tends to occur at a higher than observed frequency. A few models also capture the teleconnection between NINO3 temperature anomalies, SOI, and the global surface air temperature, sea level pressure and precipitation. Only a few models simulate phase locking of ENSO with the seasonal cycle. Most models have problems in simulating the ENSO related sea-level pressure variations over the eastern Pacific and this is associated with errors in the precipitation response. The models fall into three distinct categories based on their ability to simulate large-scale ENSO related patterns of sea-level pressure, surface air temperature and precipitation. Group 1 consists of models that represent well the Walker circulation anomalies, the warming and enhanced rainfall in the central/east Pacific. Models in group 2 exhibit a westward displacement and/or have weak responses, while group 3 models have the weakest ENSO signatures.

The Relationship of Cloud Cover to Near-Surface Temperature and Humidity: Comparison of GCM Simulations with Empirical Data

One of the possible ways to check the adequacy of the physical description of meteorological elements in global climate models (GCMs) is to compare the statistical structure of these elements reproduced by models with empirical data from the world climate observational system. The success in GCM development warranted a further step in this assessment. The description of the meteorological element in the model can be considered adequate if, with a proper reproduction of the mean and variability of this element (as shown by the observational system), the model properly reproduces the internal relationships between this element and other climatic variables (as observed during the past several decades). Therefore, to distinguish more reliable models, the authors suggest first analyzing these relationships, “the behavior of the climatic system,” using observational data and then testing the GCMs’ output against this behavior. In this paper, the authors calculated a set of statistics from synoptic data of the past several decades and compared them with the outputs of seven GCMs participating in the Atmospheric Model Intercomparison Project (AMIP), focusing on cloud cover, one of the major trouble spots for which parameterizations are still not well established, and its interaction with other meteorological fields. Differences between long-term mean values of surface air temperature and atmospheric humidity for average and clear sky or for average and overcast conditions characterize the long-term noncausal associations between these two elements and total cloud cover. Not all the GCMs reproduce these associations properly. For example, there was a general agreement in reproducing mean daily cloud–temperature associations in the cold season among all models tested, but large discrepancies between empirical data and some models are found for summer conditions. A correct reproduction of the diurnal cycle of cloud–temperature associations in the warm season is still a major challenge for two of the GCMs that were tested.