Surface Temperature Trends on the Central Siberian Plateau

Environmental Research Letters, 2021

Arctic surface temperature has increased at approximately twice the global rate over the past few decades and is also projected to warm most in the 21st century. However, the mechanism of Arctic vegetation response to this warming remains largely uncertain. Here, we analyse variations in the seasonal profiles of MODerate resolution Imaging Spectroradiometer Leaf Area Index (LAI) and ERA-interim cumulative near-Surface Air Temperature (SATΣ) over the northern Russia, north of 60° N for 2000–2019. We find that commonly used broad temporal interval (seasonal) trends cannot fully represent complex interannual variations of the LAI profile over the growing season. A sequence of narrow temporal interval (weekly) LAI trends form an inverted S-shape over the course of the growing season with enhanced green-up and senescence, but balanced during the growing season’s peak. Spatial patterns of weekly LAI trends match with those of weekly SATΣ trends during the green-up, while the drivers of th...

Understanding the warming trends at local level is critical; and, the development of relevant adaptation and mitigation policies at those levels are quite challenging. Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composite surface temperature data; (ii) weather station-based yearly average air temperature data; and (iii) air temperature normal (i.e., 30 year average) data over the Canadian province of Alberta during the period 1961–2010. Thus, we analysed the station-based air temperature data in generating relationships between air temperature normal and yearly average air temperature in order to facilitate the selection of year-specific MODIS-based surface temperature data. These MODIS data in conjunction with weather station-based air temperature normal data were then used to model local warming trends. We observed that almost 88% areas of the province experienced warming trends (i.e., up to 1.5˚C). The study concluded that remote sensing technology could be useful for delineating generic trends associated with local warming.

Remote Sensing

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the stu...

Climate change in Siberia and more generally in high latitudes, is impacting strongly the environment and the societies. If the present climate warming evolves as projected, these impacts are likely to increase, greatly affecting ecosystems, cultures, lifestyles and economies. The CLASSIQUE French research project is focused on these questions, with a special attention to land cover evolution, forest vulnerability and permafrost reduction in Siberia. It mobilizes climatologists, hydrologists, agronomists, demographers, geographers and specialists of scientific mediation in a trans-disciplinary effort to better quantify (1) future changes of climate and vegetation properties in Siberia; (2) the consecutive evolution of the agricultural potential of the region; (3) the demographic and societal effects of these changes; and (4) the interactions and feedbacks induced. The chosen approach aims to develop integrated models able to predict the evolution of land cover and hydrology and the ...

Remote Sensing of Environment, 2023

The Tibetan Plateau (TP) is the highest plateau in the world, which imposes the intense thermal and dynamical forcings on the atmosphere and then impacts the climate in its surroundings. The TP has been undergoing a rapid warming, which accelerates glacial melting, and causes more natural hazards. Although the warming on the TP has been widely investigated, there is no complete picture of its thermal status during the past decades for lack of high-quality, long-term, spatiotemporal-continuous observations. The number of weather stations are rather limited and are mainly located in the east of the TP. The analysis based on these stations is confronted with the spatial representativeness problem. On the other hand, Satellites can monitor the earth seamlessly in space and time, but reliable land surface temperatures (LSTs) have only been available in recent 20 years, and moreover their physical meaning also differs from that of the most commonly used surface air temperatures (SATs). For climate change research, the period length of these satellite LSTs is too short to obtain a definitive conclusion. In this study, the entire algorithm consists of two primary steps. One is to develop an stacking-based ensemble learning algorithm to convert LSTs to SATs with the random forest model as both base learner and meta learner. The other is to construct a Bayesian-based temporal extension algorithm to merge satellite SATs and station SATs to obtain long-term, spatiotemporal-continuous SATs. After validating the reliability of these SATs and the warming trends based on them, 60 years (1961-2020) of SATs on the TP are implemented to examine the warming status of the TP. The spatial pattern of temperature trends illustrates that the warming occurs almost everywhere on the TP, and lots of areas with intensive warming, cannot be detected only based on station observations , especially in the western part of the TP. Similarly, ERA5-Land and CRU datasets underestimate the warming in these areas. The newly-derived warming rate arrives at 0.03 • C/year and is 50% greater than those computed based on ERA5-Land and CRU dataset, implying an unexpected severe threat to the cryosphere.

International Journal of Applied Earth Observation and Geoinformation, 2012

Spatio-temporal variability in energy fluxes at the earth's surface implies spatial and temporal changes in observed land surface temperatures (LST). These fluxes are largely determined by variation in meteorological conditions, surface cover and soil characteristics. Consequently, a change in these parameters will be reflected in a different temporal LST behavior which can be observed by remotely sensed time series. Therefore, the objective of this paper is to perform a quantitative analysis on the parameters that determine this variability in LST to estimate the impact of changes in these parameters on the surface thermal regime. This study was conducted in the Russian Altay Mountains, an area characterized by strong gradients in meteorological conditions and surface cover. Spatio-temporal variability in LST was assessed by applying the fast Fourier transform (FFT) on 8 year of MODIS Aqua LST time series, herein considering both day and nighttime series as well as the diurnal difference. This FFT method was chosen as it allows to discriminate significant periodics, and as such enables distinction between short-term weather components, and strong, climate related, periodic patterns. A quantitative analysis was based on multiple linear regression models between the calculated, significant Fourier components (i.e. the annual and average component) and five physiographic variables representing the regional variability in meteorological conditions and surface cover. Physiographic predictors were elevation, potential solar insolation, topographic convergence, vegetation cover and snow cover duration. Results illustrated the strong inverse relationship between averaged daytime and diurnal difference LST and snow duration, with a R 2 adj of 0.85 and 0.60, respectively. On the other hand, nocturnal LST showed a strong connection with elevation and the amount of vegetation cover. Amplitudes of the annual harmonic experienced both for daytime and for nighttime LST similar trends with the set of physiographic variables -with stronger relationships at night. As such, topographic convergence was found to be the principal single predictor which demonstrated the importance of severe temperature inversions in the region. Furthermore, limited contribution of the physiographic predictors to the observed variation in the annual signal of the diurnal difference was retrieved, although a significant phase divergence was noticed between the majority of the study region and the perennial snowfields. Hence, this study gives valuable insights into the complexity of the spatio-temporal variability in LST, which can be used in future studies to estimate the ecosystems' response on changing climatic conditions.

Climate Dynamics, 2012

For the first time we present a multi-proxy data set for the Russian Altai, consisting of Siberian larch treering width (TRW), latewood density (MXD), d 13 C and d 18 O in cellulose chronologies obtained for the period 1779-2007 and cell wall thickness (CWT) for 1900-2008. All of these parameters agree well between each other in the high-frequency variability, while the low-frequency climate information shows systematic differences. The correlation analysis with temperature and precipitation data from the closest weather station and gridded data revealed that annual TRW, MXD, CWT, and d 13 C data contain a strong summer temperature signal, while d 18 O in cellulose represents a mixed summer and winter temperature and precipitation signal. The temperature and precipitation reconstructions from the Belukha ice core and Teletskoe lake sediments were used to investigate the correspondence of different independent proxies. Low frequency patterns in TRW and d 13 C chronologies are consistent with temperature reconstructions from nearby Belukha ice core and Teletskoe lake sediments showing a pronounced warming trend in the last century. Their combination could be used for the regional temperature reconstruction. The long-term d 18 O trend agrees with the precipitation reconstruction from the Teletskoe lake sediment indicating more humid conditions during the twentieth century. Therefore, these two proxies could be combined for the precipitation reconstruction.

Environmental Research Letters, 2012

The concern about Arctic greening has grown recently as the phenomenon is thought to have significant influence on global climate via atmospheric carbon emissions. Earlier work on Arctic vegetation highlighted the role of summer sea ice decline in the enhanced warming and greening phenomena observed in the region, but did not contain enough details for spatially characterizing the interactions between sea ice, temperature and vegetation photosynthetic absorption. By using 1 km resolution data from the Moderate Resolution Imaging Spectrometer (MODIS) as a primary data source, this study presents detailed maps of vegetation and temperature trends for the Siberian Arctic region, using the time integrated normalized difference vegetation index (TI-NDVI) and summer warmth index (SWI) calculated for the period 2000-11 to represent vegetation greenness and temperature respectively. Spatio-temporal relationships between the two indices and summer sea ice conditions were investigated with transects at eight locations using sea ice concentration data from the Special Sensor Microwave/Imager (SSM/I). In addition, the derived vegetation and temperature trends were compared among major Arctic vegetation types and bioclimate subzones. The fine resolution trend map produced confirms the overall greening (+1% yr −1 ) and warming (+0.27% yr −1 ) of the region, reported in previous studies, but also reveals browning areas. The causes of such local decreases in vegetation, while surrounding areas are experiencing the opposite reaction to changing conditions, are still unclear. Overall correlations between sea ice concentration and SWI as well as TI-NDVI decreased in strength with increasing distance from the coast, with a particularly pronounced pattern in the case of SWI. SWI appears to be driving TI-NDVI in many cases, but not systematically, highlighting the presence of limiting factors other than temperature for plant growth in the region. Further unravelling those limiting factors constitutes a priority in future research. This study demonstrates the use of medium resolution remotely sensed data for studying the complexity of spatio-temporal vegetation dynamics in the Arctic.

Springer Environmental Science and Engineering, 2012

This chapter provides observational evidence of climatic variations in Siberia for three time scales: during the past 10,000 years, during the past millennium prior to instrumental observations, and for the past 130 years during the period of large-scale meteorological observations. The observational evidence is appended with the global climate model projections for the twenty-fi rst century based on the most probable scenarios of the future dynamics of the major anthropogenic and natural factors responsible for contemporary climatic changes. Historically, climate of Siberia varied broadly. It was both warmer and colder than the present. However, during the past century, it became much warmer; the cold season precipitation north of 55°N increased, but no rainfall increase over most of Siberia has occurred. This led to drier summer conditions and to increased possibility of droughts and fi re weather. Projections of the future climate indicate the further temperature increases, more in the cold season and less in the warm season, signi fi cant changes in the

ISPRS International Journal of Geo-Information

Warming, i.e., increments of temperature, is evident at the global, regional, and local level. However, understanding the dynamics of local warming at high spatial resolution remains challenging. In fact, it is very common to see extremely variable land cover/land use within built-up environments that create micro-climatic conditions. To address this issue, our overall goal was to generate a local warming map for the period 1961–2010 at 15 m spatial resolution over the southern part of the Canadian province of Alberta. Our proposed methods consisted of three distinct steps. These were the: (i) construction of high spatial resolution enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) maps; (ii) conversion of air temperature (Ta) normal (i.e., 30 years average) at higher spatial resolution using vegetation indices (VI); and (iii) generation of a local warming map at 15m spatial resolution. In order to execute this study, we employed MODIS-driven air temp...