scholarly journals Characteristics of Climate Change and Extreme Weather from 1951 to 2011 in China

2018 ◽  
Vol 15 (11) ◽  
pp. 2540 ◽  
Author(s):  
Chunli Zhao ◽  
Jianguo Chen ◽  
Peng Du ◽  
Hongyong Yuan
Keyword(s):  
Climate Change ◽  
Spatial Scale ◽  
Spatial Scales ◽  
Seasonal Fluctuation ◽  
Extreme Weather ◽  
Temperature And Precipitation ◽  
Weather Variation ◽  
Variation Characteristics ◽  
Established Fact ◽  
Temporal And Spatial

It has been demonstrated that climate change is an established fact. A good comprehension of climate and extreme weather variation characteristics on a temporal and a spatial scale is important for adaptation and response. In this work, the characteristics of temperature, precipitation, and extreme weather distribution and variation is summarized for a period of 60 years and the seasonal fluctuation of temperature and precipitation is also analyzed. The results illustrate the reduction in daily and annual temperature divergence on both temporal and spatial scales. However, the gaps remain relatively significant. Furthermore, the disparity in daily and annual precipitation are found to be increasing on both temporal and spatial scales. The findings indicate that climate change, to a certain extent, narrowed the temperature gap while widening the precipitation gap on temporal and spatial scales in China.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

The shifting of climate types: manifestation to phenology and ecosystems structure

2021 ◽  
Author(s):  
Gunta Kalvāne ◽  
Andis Kalvāns ◽  
Agrita Briede ◽  
Ilmārs Krampis ◽  
Dārta Kaupe ◽  
...  
Keyword(s):  
Climate Change ◽  
Breaking Point ◽  
Data Sets ◽  
Precipitation Regime ◽  
Climate Type ◽  
Data Set ◽  
Temperature And Precipitation ◽  
Spatial Changes ◽  
Temporal And Spatial ◽  
The Impact

&lt;p&gt;According to the K&amp;#246;ppen climate classification, almost the entire area of Latvia belongs to the same climate type, Dfb, which is characterized by humid continental climates with warm (sometimes hot) summers and cold winters.&amp;#160; In the last decades whether conditions on the western coast of Latvia more characterized by temperate maritime climates. In this area there has been a transition (and still ongoing) to the climate type Cfb.&lt;/p&gt;&lt;p&gt;Temporal and spatial changes of temperature and precipitation regime have been examined in whole territory to identify the breaking point of climate type shifts. We used two type of climatological data sets: gridded daily temperature from the E-OBS data set version 21.0e (Cornes et al., 2018) and direct observations from meteorological stations (data source: Latvian Environment, Geology and Meteorology Centre). The temperature and precipitation regime have changed significantly in the last century - seasonal and regional differences can be observed in the territory of Latvia.&lt;/p&gt;&lt;p&gt;We have digitized and analysed more than 47 thousand phenological records, fixed by volunteers in period 1970-2018. Study has shown that significant seasonal changes have taken place across the Latvian landscape due to climate change (Kalv&amp;#257;ne and Kalv&amp;#257;ns, 2021). The largest changes have been recorded for the unfolding (BBCH11) and flowering (BBCH61) phase of plants&amp;#160;&amp;#8211; almost 90% of the data included in the database demonstrate a negative trend. The winter of 1988/1989 may be considered as breaking point, it has been common that many phases have begun sooner (particularly spring phases), while abiotic autumn phases have been characterized by late years.&lt;/p&gt;&lt;p&gt;Study gives an overview aboutclimate change (also climate type shift) impacts on ecosystems in Latvia, particularly to forest and semi-natural grasslands and temporal and spatial changes of vegetation structure and distribution areas.&lt;/p&gt;&lt;p&gt;This study was carried out within the framework of the Impact of Climate Change on Phytophenological Phases and Related Risks in the Baltic Region (No. 1.1.1.2/VIAA/2/18/265) ERDF project and the Climate change and sustainable use of natural resources&amp;#160;institutional research grant&amp;#160;of the University of Latvia (No. AAP2016/B041//ZD2016/AZ03).&lt;/p&gt;&lt;p&gt;Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res. Atmos., 123(17), 9391&amp;#8211;9409, doi:10.1029/2017JD028200, 2018.&lt;/p&gt;&lt;p&gt;Kalv&amp;#257;ne, G. and Kalv&amp;#257;ns, A.(2021): Phenological trends of multi-taxonomic groups in Latvia, 1970-2018, Int. J. Biometeorol., doi:https://doi.org/10.1007/s00484-020-02068-8, 2021.&lt;/p&gt;

scholarly journals Application of dendroclimatology in evaluation of climatic changes

2018 ◽  
Vol 64 (No. 3) ◽  
pp. 139-147 ◽  
Author(s):  
Khaleghi Mohammad Reza
Keyword(s):  
Climate Change ◽  
Tree Rings ◽  
Arid Regions ◽  
Ring Width ◽  
Climatic Changes ◽  
The Past ◽  
Temperature And Precipitation ◽  
Temporal And Spatial ◽  
Artificial Neural Network Ann ◽  
High Degree

The present study tends to describe the survey of climatic changes in the case of the Bojnourd region of North Khorasan, Iran. Climate change due to a fragile ecosystem in semi-arid and arid regions such as Iran is one of the most challenging climatological and hydrological problems. Dendrochronology, which uses tree rings to their exact year of formation to analyse temporal and spatial patterns of processes in the physical and cultural sciences, can be used to evaluate the effects of climate change. In this study, the effects of climate change were simulated using dendrochronology (tree rings) and an artificial neural network (ANN) for the period from 1800 to 2015. The present study was executed using the Quercus castaneifolia C.A. Meyer. Tree-ring width, temperature, and precipitation were the input parameters for the study, and climate change parameters were the outputs. After the training process, the model was verified. The verified network and tree rings were used to simulate climatic parameter changes during the past times. The results showed that the integration of dendroclimatology and an ANN renders a high degree of accuracy and efficiency in the simulation of climate change. The results showed that in the last two centuries, the climate of the study area changed from semiarid to arid, and its annual precipitation decreased significantly.

scholarly journals Tropical rainfall trend and stationarity analysis

2020 ◽  
Vol 20 (7) ◽  
pp. 2471-2483
Author(s):  
Chun Kang Ng ◽  
Jing Lin Ng ◽  
Yuk Feng Huang ◽  
Yi Xun Tan ◽  
Majid Mirzaei
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
River Basin ◽  
Spatial Scales ◽  
Kendall Test ◽  
Temporal Analysis ◽  
Northern Region ◽  
Rainfall Series ◽  
Kelantan River Basin ◽  
Temporal And Spatial

Abstract Climate change is most likely to cause changes to the temporal and spatial variability of rainfall. A trend analysis to investigate the rainfall pattern can detect changes over temporal and spatial scales for a rainfall series. In this study, trend analysis using the Mann–Kendall test and Sen's slope estimator was conducted in the Kelantan River Basin, Malaysia. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test was applied to evaluate the stationarity of the rainfall series. This basin annually faces onslaughts of varying year-end flooding conditions. The trend analysis was applied for monthly, seasonal and yearly rainfall series between 1989 and 2018. The temporal analysis results showed that both increasing and decreasing trends were detected for all rainfall series. The spatial analysis results indicated that the northern region of the Kelantan River Basin showed an increasing trend, whilst the southwest region showed a decreasing trend. It was found that almost all the rainfall series were stationary except at two rainfall stations during the Inter Monsoon 1, Inter Monsoon 2 and yearly rainfall series. Results obtained from this study can be used as reference for water resources planning and climate change assessment.

The Temporal and Spatial Scales of Global Climate Change and the Limits of Individualistic and Rationalistic Ethics

2011 ◽  
Vol 69 ◽  
pp. 101-116 ◽  
Author(s):  
J. Baird Callicott
Keyword(s):  
Climate Change ◽  
Environmental Ethics ◽  
Moral Philosophy ◽  
Global Climate Change ◽  
Global Climate ◽  
Spatial Scales ◽  
Environmental Ethic ◽  
Original Position ◽  
Temporal Scales ◽  
Temporal And Spatial

Here I argue that the hyper-individualistic and rationalistic ethical paradigms – originating in the late eighteenth century and dominating moral philosophy, in various permutations, ever since – cannot capture the moral concerns evoked by the prospect of global climate change. Those paradigms are undone by the temporal and spatial scales of climate change. To press my argument, I deploy two famous philosophical tropes – John Rawls's notion of the original position and Derek Parfit's paradox – and another that promises to become famous: Dale Jamieson's six little ditties about Jack and Jill. I then go on to argue that the spatial and especially the temporal scales of global climate change demand a shift in moral philosophy from a hyper-individualistic ontology to a thoroughly holistic ontology. It also demands a shift from a reason-based to a sentiment-based moral psychology. Holism in environmental ethics is usually coupled with non-anthropocentrism in theories constructed to provide moral considerability for transorganismic entities – such as species, biotic communities, and ecosystems. The spatial and temporal scales of climate, however, render non-anthropocentric environmental ethics otiose, as I more fully explain. Thus the environmental ethic here proposed to meet the moral challenge of global climate change is holistic but anthropocentric. I start with Jamieson's six little ditties about Jack and Jill.

scholarly journals A Methodology for Attributing the Role of Climate Change in Extreme Events: A Global Spectrally Nudged Storyline

2021 ◽  
Author(s):  
Linda van Garderen ◽  
Frauke Feser ◽  
Theodore G. Shepherd
Keyword(s):  
Climate Change ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Extreme Weather ◽  
Climate Impacts ◽  
Lower Atmosphere ◽  
Extreme Weather Events ◽  
Past Century ◽  
Signal To Noise ◽  
Noise Ratio

&lt;p&gt;Extreme weather events are generally associated with unusual dynamical conditions, yet the signal-to-noise ratio of the dynamical aspects of climate change that are relevant to extremes appears to be small, and the nature of the change can be highly uncertain. On the other hand, the thermodynamic aspects of climate change are already largely apparent from observations, and are far more certain since they are anchored in agreed-upon physical understanding. The storyline method of extreme event attribution, which has been gaining traction in recent years, quantitatively estimates the magnitude of thermodynamic aspects of climate change, given the dynamical conditions. There are different ways of imposing the dynamical conditions. Here we present and evaluate a method where the dynamical conditions are enforced through global spectral nudging towards reanalysis data of the large-scale vorticity and divergence in the free atmosphere, leaving the lower atmosphere free to respond. We simulate the historical extreme weather event twice: first in the world as we know it, with the events occurring on a background of a changing climate, and second in a &amp;#8216;counterfactual&amp;#8217; world, where the background is held fixed over the past century. We describe the methodology in detail, and present results for the European 2003 heatwave and the Russian 2010 heatwave as a proof of concept. These show that the conditional attribution can be performed with a high signal-to-noise ratio on daily timescales and at local spatial scales. Our methodology is thus potentially highly useful for realistic stress testing of resilience strategies for climate impacts, when coupled to an impact model.&lt;/p&gt;

scholarly journals CLIMATIC VARIABILITY PREDICTION WITH SATELLITE REMOTE SENSING AND METEOROLOGICAL DATA IN THE SOUTH WESTERN NIGERIA

2013 ◽  
Vol 39 (2) ◽  
pp. 59-63
Author(s):  
Ebenezer Yemi Ogunbadewa
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Spatial Scales ◽  
Climatic Variability ◽  
Data Availability ◽  
The South ◽  
Suitable Alternative ◽  
Temporal And Spatial ◽  
Satellite Sensor

Climatic variability affects both seasonal phenological cycles of vegetation and monthly distribution of rainfall in the south western Nigeria. Variations in vegetation biophysical parameters have been known to be a good indicator of climate variability; hence they are used as key inputs into climate change models. However, understanding the response of vegetation to the influence of climate at both temporal and spatial scales have been a major challenge. This is because most climatic data available are derived from ground-based instruments, which are mainly point measurements and are characterized by sparse network of meteorological stations that lacks the spatial coverage required for climate change investigation. Satellite remote sensing instruments can provide a suitable alternative of time-reliable datasets in a more consistent manner at both temporal and spatial scales. The aim of this study is to test the suitability of one year time series datasets obtained from satellite sensor and meteorological stations as a starting point for the development of a climate change model that can be exploited in planning adaptation strategies. Taking into consideration that rainfall is the most variable element of climate in the study area, rainfall data acquired from five meteorological stations for the year 2006 were correlated with changes in Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite sensor for the same period using a linear regression equation. The results shows that rainfall–NDVI relationship was stronger along the seasonal track with R2 ranging from 0.74 to 0.94, indicating that NDVI seasonal variations can be used as a surrogate data source for monitoring climate change for short and long term scales ranging from regional to global magnitude especially in areas where data availability from ground-based measurements are unreliable.

scholarly journals A Methodology for Attributing the Role of Climate Change in Extreme Events: A Global Spectrally Nudged Storyline

2020 ◽  
Author(s):  
Linda van Garderen ◽  
Frauke Feser ◽  
Theodore G. Shepherd
Keyword(s):  
Climate Change ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Extreme Weather ◽  
Climate Impacts ◽  
Lower Atmosphere ◽  
Extreme Weather Events ◽  
Past Century ◽  
Signal To Noise ◽  
Noise Ratio

Abstract. Extreme weather events are associated with unusual dynamical conditions, yet the signal-to-noise ratio of the dynamical aspects of climate change that are relevant to extremes appears to be small, and the nature of the change can be highly uncertain. On the other hand, the thermodynamic aspects of climate change are already largely apparent from observations, and are far more certain since they are anchored in agreed-upon physical understanding. The storyline method of extreme event attribution, which has been gaining traction in recent years, quantitatively estimates the magnitude of thermodynamic aspects of climate change, given the dynamical conditions. There are different ways of imposing the dynamical conditions. Here we present and evaluate a method where the dynamical conditions are enforced through global spectral nudging towards reanalysis data of the large-scale vorticity and divergence in the free atmosphere, leaving the lower atmosphere free to respond. We simulate the historical extreme weather event twice: first in the world as we know it, with the events occurring on a background of a changing climate, and second in a ‘counterfactual’ world, where the background is held fixed over the past century. We describe the methodology in detail, and present results for the European 2003 heatwave and the Russian 2010 heatwave as a proof of concept. These show that the conditional attribution can be performed with a high signal-to-noise ratio on daily timescales and at local spatial scales. Our methodology is thus potentially highly useful for realistic stress testing of resilience strategies for climate impacts, when coupled to an impact model.

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