Distribution Of Surface Air Temperature Research Articles

Impact of Proxies and Prior Estimates on Data Assimilation Using Isotope Ratios for the Climate Reconstruction of the Last Millennium

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

Era-interim Forced Simulation of the Indian Summer Monsoon

In the present study, mean rainfall for the months of June-July-August-September (JJAS) during summer monsoon is simulated over India and its adjoining regions for a period between 1982 and 2006. The study was carried out using Regional Climate Model (RegCM) version 4.6 at a resolution of 25 km. The simulated mean JJAS monsoon rainfall was validated against the observational IMD data. Comparison of JJAS seasonal mean summer rainfall for the first decade 1982–1991, with the later decade 1997–2006 indicate that the intensity of rainfall increases over Indian land-mass during the later decade under the forced conditions of Era-Interim. The observed JJAS mean rainfall indicates two maximum rainfall areas i.e. the Western Ghats and the Himalayan region. A significant bias is observed in the central and Jammu and Kashmir (J&K) region. The JJAS mean seasonal surface air temperature distribution at 0.25 × 0.25-degree grids resolution shows a decreasing trend of temperature over the Indian landmass.

Performance of the CMIP5 models in the simulation of the Himalaya-Tibetan Plateau monsoon

In this paper, the performance of 28 CMIP5 models in simulating the climate of the Himalaya-Tibetan Plateau (HTP) for the recent past (1975–2005) is evaluated using the observations from the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE). Many models realistically simulate the spatial distribution of surface air temperature (Tas) and precipitation with pattern correlation as high as 0.8; however, they possess severe biases in their magnitude. The biases in Tas appear to be associated with the biases in the surface elevation. All the models capture the observed phase of the annual cycle of the Tas but underestimate the amplitude. For precipitation, the phase is captured by most models (except few), but the amplitude is overestimated in all models. In the mid-intensity precipitation range (10–80 mm day−1), most of the models overestimate the probability of occurrence and show large intermodel differences. Most of the models fail to simulate the spatial distribution of the trend in Tas and precipitation. As compared to many individual models, the biases are noted to reduce when using multimodel means (MMMs); however, the MMMs also failed to capture the observed trends in both Tas and precipitation. Many models still struggle to capture the large-scale phenomena, such as the location and intensity of upper-level Asian anticyclone and middle troposphere temperature maximum over the HTP, which have large implications on the HTP as well as the Indian summer monsoon. The results show that none of the models capture all features of the HTP monsoon, and hence, further improvement in the parameterization schemes and resolution is required to gain more confidence in the projection of HTP climate using these models.

Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics

This study represents the first large-scale systematic dendroclimatic sampling focused on developing chronologies from different species in the eastern Mediterranean region. Six reconstructions were developed from chronologies ranging in length from 115years to 600years. The first reconstruction (18852000) was derived from principal components (PCs) of 36 combined chronologies. The remaining five, 18002000, 17002000, 16002000, 15002000 and 14002000 were developed from PCs of 32, 18, 14, 9, and 7 chronologies, respectively. Calibration and verification statistics for the period 19312000 show good levels of skill for all reconstructions. The longest period of consecutive dry years, defined as those with less than 90% of the mean of the observed MayAugust precipitation, was 5years (15911595) and occurred only once during the last 600years. The longest reconstructed wet period was 5years (16011605 and 17511755). No long term trends were found in MayAugust precipitation during the last few centuries. Regression maps are used to identify the influence of large-scale atmospheric circulation on regional precipitation. In general, tree-ring indices are influenced by MayAugust precipitation, which is driven by anomalous below (above) normal pressure at all atmospheric levels and by convection (subsidence) and small pressure gradients at sea level. These atmospheric conditions also control the anomaly surface air temperature distribution which indicates below (above) normal values in the southern regions and warmer (cooler) conditions north of around 40N. A compositing technique is used to extract information on large-scale climate signals from extreme wet and dry summers for the second half of the twentieth century and an independent reconstruction over the last 237years. Similar main modes of atmospheric patterns and surface air temperature distribution related to extreme dry and wet summers were identified both for the most recent 50years and the last 237years. Except for the last few decades, running correlation analyses between the major European-scale circulation patterns and eastern Mediteranean summer precipitation over the last 237years are non-stationary and insignificant, suggesting that local and/or sub-regional geographic factors and processes are important influences on tree-ring variability over the last few centuries.

An analysis of spatial representativeness of air temperature monitoring stations

Surface air temperature is an essential variable for monitoring the atmosphere, and it is generally acquired at meteorological stations that can provide information about only a small area within an r m radius (r-neighborhood) of the station, which is called the representable radius. In studies on a local scale, ground-based observations of surface air temperatures obtained from scattered stations are usually interpolated using a variety of methods without ascertaining their effectiveness. Thus, it is necessary to evaluate the spatial representativeness of ground-based observations of surface air temperature before conducting studies on a local scale. The present study used remote sensing data to estimate the spatial distribution of surface air temperature using the advection-energy balance for air temperature (ADEBAT) model. Two target stations in the study area were selected to conduct an analysis of spatial representativeness. The results showed that one station (AWS 7) had a representable radius of about 400 m with a possible error of less than 1 K, while the other station (AWS 16) had the radius of about 250 m. The representable radius was large when the heterogeneity of land cover around the station was small.

The effect of a well-resolved stratosphere on East Asian winter climate

This study performs a comparison between models with and without well-resolved stratosphere on the simulation of the East Asian winter climate. The results reveal that the Fifth Coupled Model Inter-comparison Project (CMIP5) models with model top above the stratopause have a better simulation of the distribution of surface air temperature, sea level pressure and precipitation than the models with a low-top below the stratopause. The discrepancy of the East Asian winter climate between high-top (HT) and low-top (LT) CMIP5 models is also evident in the future projection under higher (RCP85) and midrange (RCP45) emission scenarios. The HT models present about 1.3 and 1.7 °C higher surface air temperature in East Asian region under RCP45 and RCP 85 scenarios by the end of this century than that of the LT models. Therefore, the insufficient representation of the stratosphere may lead to underestimation of the anthropogenic global warming in regional scale and hence have the potential to lead to insufficient response action and mitigation measures.

Biases and improvements in three dynamical downscaling climate simulations over China

A dynamical downscaling is performed to improve the regional climate simulation in China. It consists of using a variable resolution model LMDZ4 nested into three global climate models (GCMs): BCC-csm1-1-m, FGOALS-g2 and IPSL-CM5A-MR, respectively. The regional climate from different simulations is assessed in terms of surface air temperature and rainfalls through a comparison to observations (both station data and gridded data). The comparison includes climatic trends during the last 40 years, statistical distribution of sub-regional climate, and the seasonal cycle. For surface air temperature, a significant part of the improvement provided by LMDZ4 is related to the effect of surface elevation which is more realistic in high-resolution simulations; the rest is related to changes in regional or local atmospheric general circulation. All GCMs and the downscaling model LMDZ4 are, more or less, able to describe the spatial distribution of surface air temperature and precipitation in China. LMDZ4 does show its superiority, compared to GCMs, in depicting a good regional terrain including the Tibetan Plateau, the Sichuan Basin and the Qilian Mountains.

Modelling of Surface Air Temperature Element in Malaysia

In spatial interpolation approaches, attribute data assumes continuous over space and spatially dependence. The surface air temperature is a collection of data, which are observed at discrete locations. Normally, spatial interpolation analysis applies to produce a continuous surface of this discrete data. There are a number of spatial interpolation techniques available to create continuous distribution of surface air temperature. To generate map of surface air temperature, this study examined the interpolation technique of inverse distance weighting (IDW). In applying the IDW technique, two different types of main data were assessed, i.e. mean monthly temperature data of T and estimation error of T – T’, where T was observed mean monthly surface air temperature and T’ was estimated mean monthly mean surface air temperature from a multiple regression. The multiple regression model was developed based on eight independent variables of elevation, location of latitude and longitude, distances of a station to nearest coastline, four types of land use, which included water bodies, forest, agriculture and build up areas. Cross validation analysis was conducted to calculate five different measured of errors. Inverse distance weighting (IDW) spatial interpolation of T ‐ Tʹ main data was produced acceptable errors and reliable map for mean monthly mean surface air temperature element in Peninsular Malaysia.

Geomagnetic activity and the North Atlantic Oscillation

The North Atlantic Oscillation (NAO) is the prominent pattern of winter climate variability that has a strong effect on weather in the North Atlantic region and the adjacent continents. At present, uncertainty prevails as to the mechanisms controlling the variability of the NAO. It is also difficult to explain why the positive phase of the NAO has prevailed over the past 37 years (1972–2008). We found high positive correlation coefficients between geomagnetic activity (used as a measure of solar wind intensity) and the NAO indices that equal 0.76 for 1962–1994 and 0.63 for 1961–2011. Positive correlations of the distribution of surface air temperature with the NAO and similarly with geomagnetic activity occur in the Northern Hemisphere. These results encourage our search for possible causes controlling the NAO. We have found that at times of high geomagnetic activity the NAO index is positive and magnetic reconnection may enable the solar wind to initiate downward winds in the magnetosphere. Wind anomalies originate at the edge of the stratospheric polar vortex and propagate downward through the troposphere taking part in the intensification of the vortex and of the westerlies. Stronger northerly winds over Greenland carry cold air southward and, together with the enhanced westerlies, advect the warm air from the Atlantic along the deep Icelandic low into Eurasia increasing temperatures there. On the other hand, at times of low geomagnetic activity, the NAO index is negative and the stratospheric polar vortex is weak. Warm air from the subtropics is carried into the Arctic and a rapid amplification of planetary waves propagating upward may cause displacement or even splitting of the weak vortex and sudden stratospheric warming. During this negative NAO phase the weakened westerlies allow more cold air to build up over North America and Eurasia.

Interpolation of temperature in a mountainous region using nonlinear profiles and non‐Euclidean distances

ABSTRACTIn mountain regions, the distribution of surface air temperature happens to show marked horizontal gradients and nonlinear variations with topographic height. These pose a major challenge for the construction of area‐wide temperature datasets on a regular grid. This study introduces a new deterministic method's for the spatial interpolation of daily temperature from station measurements. Building on a scale‐separation concept, the methods main features are (1) a nonlinear parametric function to model nonlinearities in the vertical thermal profile at the scale of major basins, and (2) a distance weighting scheme with a non‐Euclidean metric that accounts for terrain effects on the spatial representativity of measurements. The method is configured for the territory of Switzerland (European Alps) and is applied for the construction of a km‐scale grid dataset from 70‐100 stations over all days from 1961 to 2010. Several illustrative cases attest the method's potential, also under challenging conditions. Temperature patterns from basin‐scale inversions and surface heated/cooled boundary layers are realistically reproduced. In situations with valley‐scale cold‐air pools and foehn, the method is less prone to artificial upslope/downslope extrapolation often observed with other techniques. With a network of 100 stations in Switzerland, typical interpolation errors (mean absolute error MAE, cross‐validation) range from 0.5 °C over flat and hilly terrain in summer to 1.5 °C in the Alps in winter. Larger and partly systematic errors (MAE ≥ 3 °C) must be expected in un‐sampled valleys in winter due to the missing out of local‐scale cold pools. Interpolation accuracy was found to vary with the change in station density over time, demonstrating improvements in the overall representativity of the measurement network. But this also compromises the long‐term homogeneity of the grid dataset, despite it being based on homogeneous records. The presented method may be applicable in other mountain regions after some configuration.

Twenty‐first century changes in daily temperature variability in CMIP3 climate models

AbstractChanges in the daily mean surface air temperature distribution between the time periods 1981–2000 and 2081–2100 in 15 climate model simulations participating in the Third Coupled Model Intercomparison Project (CMIP3) are analysed. Variability is defined by first removing the annual cycle after which the range and skewness of the temperature distribution are calculated. Present‐day biases for the different aspects of the distribution are also studied, using the ERA‐Interim reanalysis as the main reference dataset. The variability in the simulations compared to reanalysis is generally smaller over oceans and larger over land areas. For skewness of the temperature distribution, inter‐model variability is much larger and systematic model biases are more local. Model performance regarding the mean values is potentially a good indicator of the model performance for range, but for skewness this relation is weaker. For future changes in the range of daily temperature variability, the models agree best over the Northern Hemisphere mid‐ to high‐latitude land areas, where variability is simulated to decrease. Elsewhere, the inter‐model differences are generally larger than the multi‐model mean changes. Near the sea ice border, significant negative correlations are found between the changes in mean temperature and the temperature range. For changes in skewness, the signal‐to‐noise ratio is very low over most areas and no firm conclusions can be drawn from the ensemble results. Our results are relatively insensitive to the precise definition of the range and skewness metrics and to internal variability of climate.

Variation of Surface Air Temperature in Complex Terrain

AbstractData from three micronetworks with eddy correlation data and three additional micronetworks without eddy correlation data are analyzed to study the spatial variability of surface air temperature in complex terrain. A simple similarity relationship is constructed to relate the spatial variation of air temperature along the slope to the thermal forcing and mixing. Mixing is not included in present empirical formulations of the surface air temperature distribution in complex terrain. The development of surface temperature gradients along the slope, resulting from surface heating or cooling, is bounded by a maximum (or saturated) value, where a further increase of temperature gradients is restricted by redistribution of heat by thermally driven slope circulations. Although much of the spatial variation of the surface air temperature is governed by complex three-dimensionality and surface vegetation, a relatively simple relationship is able to account for much of the diurnal and day-to-day variation of the spatial distribution of air temperature. This relationship requires information on the surface heat flux and friction velocity over a reference surface. Required generalizations of the relationship are outlined before it can be applied to an arbitrary site.

Global surface temperature in relation to northeast monsoon rainfall over Tamil Nadu

The local and teleconnective association between Northeast Monsoon Rainfall (NEMR) over Tamil Nadu and global Surface Temperature Anomalies (STA) is examined using the monthly gridded STA data for the period 1901–2004. Various geographical regions which have significant teleconnective signals associated with NEMR are identified. During excess (deficient) NEMR years, it is observed that the meridional gradient in surface air temperature anomalies between Europe and north Africa, in the month of September is directed from the subtropics (higher latitudes) to higher latitudes (subtropics). It is also observed that North Atlantic Oscillation (NAO) during September influences the surface air temperature distribution over north Africa and Europe. Also, the NAO index in January shows significant inverse relationship with NEMR since recent times. The central and eastern equatorial Pacific oceanic regions have significant and consistent positive correlation with NEMR while the western equatorial region has significant negative correlation with NEMR. A zonal temperature anomaly gradient index (ZTAGI) defined between eastern equatorial Pacific and western equatorial Pacific shows stable significant inverse relationship with NEMR

Numerical Simulation of Local Weather for a High Photochemical Oxidant Event Using the WRF Model

We evaluated the performance of the newly developed atmospheric mesoscale model, WRF, for the simulation of urban-scale weather in the Tokyo metropolitan area during a high photochemical Oxidant event. The simulation clearly shows that WRF represents the spatial distribution of surface air temperature during the daytime, although the model temperature is lower than the observations in the late afternoon to evening in the urban area. The wind system can be well reproduced in WRF. Simulated convergence zone moves toward the inland areas located to the northwest of the coastal area during the three hours. These results are consistent with the observations of temperature and Photochemical Oxidant, indicating that WRF has enough potential to predict the ongoing Oxidant concentration.

Sahel rainfall variability and response to greenhouse warming

The NCEP/NCAR re‐analyses as well as ensemble integrations with an atmospheric GCM indicate that interannual variations in Sahel rainfall are related to variations in the mean sea level pressure (MSLP) over the Sahara. In turn the MSLP variations are related to the global distribution of surface air temperature (SAT). An increase in SAT over the Sahara, relative to the surrounding oceans, decreases the MSLP over the Sahara, thereby increasing the Sahel rainfall. We hypothesize that through this mechanism greenhouse warming will cause an increase in Sahel rainfall, because the warming is expected to be more prominent over the summer continents than over the oceans. This has been confirmed using an ensemble of 62 coupled model runs forced with a business as usual scenario. The ensemble mean increase in Sahel rainfall between 1980 and 2080 is about 1–2 mm day−1 (25–50%) during July–September, thereby strongly reducing the probability of prolonged droughts.

Reconstructions of spring/summer precipitation for the Eastern Mediterranean from tree-ring widths and its connection to large-scale atmospheric circulation

This study represents the first large-scale systematic dendroclimatic sampling focused on developing chronologies from different species in the eastern Mediterranean region. Six reconstructions were developed from chronologies ranging in length from 115 years to 600 years. The first reconstruction (1885–2000) was derived from principal components (PCs) of 36 combined chronologies. The remaining five, 1800–2000, 1700–2000, 1600–2000, 1500–2000 and 1400–2000 were developed from PCs of 32, 18, 14, 9, and 7 chronologies, respectively. Calibration and verification statistics for the period 1931–2000 show good levels of skill for all reconstructions. The longest period of consecutive dry years, defined as those with less than 90% of the mean of the observed May–August precipitation, was 5 years (1591–1595) and occurred only once during the last 600 years. The longest reconstructed wet period was 5 years (1601–1605 and 1751–1755). No long term trends were found in May–August precipitation during the last few centuries. Regression maps are used to identify the influence of large-scale atmospheric circulation on regional precipitation. In general, tree-ring indices are influenced by May–August precipitation, which is driven by anomalous below (above) normal pressure at all atmospheric levels and by convection (subsidence) and small pressure gradients at sea level. These atmospheric conditions also control the anomaly surface air temperature distribution which indicates below (above) normal values in the southern regions and warmer (cooler) conditions north of around 40°N. A compositing technique is used to extract information on large-scale climate signals from extreme wet and dry summers for the second half of the twentieth century and an independent reconstruction over the last 237 years. Similar main modes of atmospheric patterns and surface air temperature distribution related to extreme dry and wet summers were identified both for the most recent 50 years and the last 237 years. Except for the last few decades, running correlation analyses between the major European-scale circulation patterns and eastern Mediteranean spring/summer precipitation over the last 237 years are non-stationary and insignificant, suggesting that local and/or sub-regional geographic factors and processes are important influences on tree-ring variability over the last few centuries.

Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains, China

Understanding temporal and spatial distribution of surface air temperature (SAT) at the landscape scale is essential in assessing the potential ecological conditions for ecological restoration and in making decisions for regional management in the Qilian mountains, northwest China. Based on the measurement of air temperature, this study developed a linear regression relationship between the monthly mean SAT and elevation and locational/topographic factors. On average over the year, the model had a higher accuracy to predict SAT in the southern Qilian mountainous terrain of the Heihe River Basin. The study also compared the built linear regression model with geostatistical methods (i.e., ordinary kriging, splines and inverse distance weight), generally, the predictions errors obtained by the geostatistical methods were larger than that by regression method. The worst results were produced by spline. It was noteworthy that for several months (i.e. growing seasons) ordinary kriging yielded smaller prediction errors than the linear regression of temperature against elevation and locational/topographic factors did. We selected the OK method to estimate the SAT in the growing seasons, because accurately estimating surface air temperature during the ecologically meaningful time period was very important to model future ecological processes. Modeled SAT increased from northeast to southwest with highest value occurring in the Yinglou gorge, the outlet of Heihe River in the study area. Temporally, highest SAT value, ranging from 9.2 to 18.7 °C, appeared in the July, and the lowest SAT value, from 3.3 to 13.3 °C, was seen in May.

On the Near-0.DEG.C. Frequency Maximum in Surface Air Temperature under Precipitation: A Statistical Evidence for the Melting Effect.

By using hourly data at 808 automated stations for 19 years, the frequency distribution of surface air temperature was analyzed, with attention to the near-0 °C frequency maximum resulting from the melting of snow particles. Temperature in precipitation cases has a distinct frequency maximum just above 0 °C (0.3 °C on the average), whereas no peak exists in no precipitation cases. The peak height, defined by the maximum frequency deviation from neighboring temperature ranges, is 122% for the average over all the precipitation cases. The peak height is dependent on precipitation intensity and strongly on wind speed, and reaches several hundred or a thousand percent for calm cases with heavy precipitation (4-6 mmh-1 and ≥7 mmh-1). There are some regional differences in peak height, with relatively high values in plains facing the Pacific coast, especially in the inland part of the Kanto plain. Finally, the depth of the isothermal layer due to melting of snow particles was estimated by applying some simple assumptions to the present results. It was shown that the average case corresponds to an isothermal layer of a few hundred meters depth, in rough agreement with some vertical sounding data.

The use of general circulation models in the analysis of the ecosystem impacts of climatic change

The use of general circulation models in the estimation of the impact of climatic change on the global ecosystem is seen to depend primarily on their ability to reliably depict the seasonal and geographical distribution of the changes in surface climate variables. While present GCMs generally simulate the observed distribution of surface air temperature reasonably well, they show significantly different changes in the equilibrium temperature as a result of doubled CO2, for example. These disagreements are attributed to differences in the model's resolution and parameterization of subgrid-scale processes. Such model-dependent errors notwithstanding, much more information of possible use in impact analysis can be extracted from general circulation model simulations than has generally been done so far. The completeness, consistency and experimental possibilities offered by simulated data sets permit the systematic extraction of a wide variety of statistics important to the surface ecosystem, such as the length of the growing season, the duration of rainless periods, and the surface moisture stress. Assuming further model improvements, the elements of a model-assisted methodology for climate impact analysis are seen to be: (1) the determination of the seasonal and geographical distribution of that portion of simulated climatic changes which are both statistically and physically significant; (2) the transformation of the (significant) large-scale climatic changes onto the local scale of impact (the climate ‘inversion’ problem); and (3) the design of specific statistical parameters or functions relevant to local ecosystem impacts.