Assessing urbanization’s contribution to warming in mainland China using satellite-estimated air temperature data

2021 ◽  
pp. 030913332198885
Author(s):  
Rui Yao ◽  
Lunche Wang ◽  
Xin Huang ◽  
Xiaojun Wu ◽  
Liu Yang ◽  
...  
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Regional Scale ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Local Scale ◽  
Rapid Urbanization ◽  
The Past ◽  
The Mean ◽  
Global Surface

The global surface air temperature (Ta) has increased significantly in the past several decades. However, it remains disputable how much effect rapid urbanization has had on warming trends in mainland China. In this study, a gridded Ta dataset was created using satellite data. Then, a series of satellite-based methods to evaluate the contribution of urbanization to warming were developed. Subsequently, the contribution of urbanization to warming during 2001–2018 was estimated. The national average Ta was found to have increased significantly (0.23°C/decade) in mainland China. At the national scale, the contribution of urbanization to warming was negligible (less than 1%) since built-up areas account for only approximately 2.66% of the area of China. At the regional scale, the contribution of urbanization was also small in most areas and was even negative in some areas. At the local scale, the contributions of urbanization to warming were 53.18%, 54.30% and 47.25% for the mean, maximum and minimum Ta, respectively, averaged for 31 major cities. This study demonstrated that the contribution of urbanization to warming was significant at the local scale, while the contribution of urbanization to large-scale warming was limited. The contribution of urbanization was underestimated at the local scale but overestimated at the national and regional scales by many previous studies due to the sparse and uneven distribution of meteorological stations.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2011 ◽  
Vol 7 (3) ◽  
pp. 1647-1692 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Regional Scale ◽  
Kappa Statistic ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Global Agreement

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variation between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then, we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with data. In average for the nine PMIP2 models, we measure a global agreement by kappa statistic of 0.80 with CTRL permafrost data, against 0.68 for the GAM method. In both cases, the provided local information reduces the inter-variation between climate models. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs is not significantly better than large-scale fields with 0.46 (GAM) and 0.49 (ML-GAM) of global agreement with LGM permafrost data. At the LGM, both methods do not reduce the inter-variation between climate models. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with permafrost data, than in the CTRL period. These differences reduce the contribution of downscaling and depend on several other factors deserving further studies.

Trend of Surface Air Temperature in Eastern China and Associated Large-Scale Climate Variability over the Last 100 Years

2014 ◽  
Vol 27 (12) ◽  
pp. 4693-4703 ◽  
Author(s):  
Ping Zhao ◽  
Phil Jones ◽  
Lijuan Cao ◽  
Zhongwei Yan ◽  
Shuyao Zha ◽  
...  
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Eastern China ◽  
Surface Air Temperature ◽  
Tropical Indian Ocean ◽  
Population Data ◽  
Warming Trend ◽  
Climate Indices ◽  
The Past ◽  
Linear Trends

Abstract Using the reconstructed continuous and homogenized surface air temperature (SAT) series for 16 cities across eastern China (where the greatest industrial developments in China have taken place) back to the nineteenth century, the authors examine linear trends of SAT. The regional-mean SAT over eastern China shows a warming trend of 1.52°C (100 yr)−1 during 1909–2010. It mainly occurred in the past 4 decades and this agrees well with the variability in another SAT series developed from a much denser station network (over 400 sites) across this part of China since 1951. This study collects population data for 245 sites (from these 400+ locations) and split these into five equally sized groups based on population size. Comparison of these five groups across different durations from 30 to 60 yr in length indicates that differences in population only account for between 9% and 24% of the warming since 1951. To show that a larger urbanization impact is very unlikely, the study additionally determines how much can be explained by some large-scale climate indices. Anomalies of large-scale climate indices such as the tropical Indian Ocean SST and the Siberian atmospheric circulation systems account for at least 80% of the total warming trends.

scholarly journals DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

2021 ◽  
Author(s):  
Dan Lunt ◽  
Keyword(s):  
Boundary Conditions ◽  
Air Temperature ◽  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Model Parameters ◽  
Global Mean

<div> <div> <div>We present results from an ensemble of eight climate models, each of which has carried out simulations of theearly Eocene climate optimum (EECO, ∼50 million years ago). These simulations have been carried out in the framework of DeepMIP (www.deepmip.org), and as such all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO<sub>2</sub> boundary conditions contribute between 3 and 5<sup>o</sup>C to Eocene warmth. Compared to results from previous studies, the DeepMIP simulations show in general reduced spread of global mean surface temperature response across the ensemble for a given atmospheric CO<sub>2</sub> concentration, and an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with preindustrial arises mostly from decreases in emissivity due to the elevated CO<sub>2</sub> (and associated water vapour and long-wave cloud feedbacks), whereas in terms of the meridional temperature gradient, the reduction in the Eocene is primarily due to emissivity and albedo changes due to the non-CO<sub>2</sub> boundary conditions (i.e. removal of the Antarctic ice sheet and changes in vegetation). Three of the models (CESM, GFDL, and NorESM) show results that are consistent with the proxies in terms of global mean temperature, meridional SST gradient, and CO<sub>2</sub>, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale the models lack skill. In particular, in the southwest Pacific, the modelled anomalies are substantially less than indicated by the proxies; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxiesor models in this region. Our aim is that the documentation of the large scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability; and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols</div> </div> </div>

Long-Term Surface Air Temperature Trends Over Mainland China

2019 ◽  
Author(s):  
Guoyu Ren ◽  
Guoli Tang ◽  
Kangmin Wen
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Regional Warming ◽  
National Reference ◽  
The Past ◽  
Temperature Trends ◽  
Past Half Century ◽  
Autumn And Winter

Based on a dataset of national reference and basic stations, which have been quality controlled and inhomogeneity processed, updated surface air temperature (SAT) series of the past 67 (1951–2017) and 113 (1905–2017) years for mainland China are constructed and analyzed. The new temperature series show significant warming trends of 0.24°C/10yr and 0.09°C/10yr respectively for the two periods. The rapid regional warming generally begins from the mid-1980s, about a decade later than the northern hemisphere average SAT change. Warming during the period of 1951–2017 is larger and more significant in the northeast, north, northwest and the Qinghai-Tibetan Plateau, and the most significant SAT increase usually occurs in winter and spring except for the Qinghai-Tibetan Plateau where winter and autumn undergo the largest warming. The slowdown of the warming can be clearly detected after 1998, especially for autumn and winter. The effect of urbanization on trends of the region averaged annual and seasonal mean SAT as calculated from the national reference and basic stations has not been adjusted, despite it being generally large and significant. In north China, the increasing trend of annual mean SAT induced by urbanization for the national stations is 0.10°C/10yr for the period 1961–2015, accounting for at least 31% of the overall annual mean warming. The contribution of urbanization to the overall warming of the past half century in Mainland China has also been summarized and discussed referring to the previous studies.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2011 ◽  
Vol 7 (4) ◽  
pp. 1225-1246 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Temperature Conditions

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM and ML-GAM) is not significantly in better agreement with LGM permafrost data than large-scale fields. At the LGM, both methods do not reduce the variability between climate models results. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with LGM data than in the CTRL period. These differences reduce the contribution of downscaling.

scholarly journals DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

2020 ◽  
Author(s):  
Daniel J. Lunt ◽  
Fran Bragg ◽  
Wing-Le Chan ◽  
David K. Hutchinson ◽  
Jean-Baptiste Ladant ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Air Temperature ◽  
Ocean Circulation ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Atmospheric Co2 ◽  
Early Eocene ◽  
Global Mean

Abstract. We present results from an ensemble of seven climate models, each of which has carried out simulations of the early Eocene climate optimum (EECO, ~ 50 million years ago). These simulations have been carried out in the framework of DeepMIP (www.deepmip.org), and as such all models have been configured with identical paleogeographic and vegetation boundary conditions. The results indicate that these non-CO2 boundary conditions contribute between 3 and 5 °C to Eocene warmth. Compared to results from previous studies, the DeepMIP simulations show reduced spread of global mean surface temperature response across the ensemble, for a given atmospheric CO2 concentration. In a marked departure from the results from previous simulations, at least two of the DeepMIP models (CESM and GFDL) are consistent with proxy indicators of global mean temperature, and atmospheric CO2, and meridional SST gradients. The best agreement with global SST proxies from these models occurs at CO2 concentrations of around 2400 ppmv. At a more regional scale the models lack skill in reproducing the proxy SSTs, in particular in the southwest Pacific, around New Zealand and south Australia, where the modelled anomalies are substantially less than indicated by the proxies. However, in these regions modelled continental surface air temperature anomalies are consistent with surface air temperature proxies, implying an inconsistency between marine and terrestrial temperatures in either the proxies or models in this region. Our aim is that the documentation of the large scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability; and encourage sensitivity studies to aspects such as paleogeography and aerosols.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2010 ◽  
Vol 4 (4) ◽  
pp. 2233-2275 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Multinomial Logistic Regression ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Additive Models ◽  
Simple Relationship ◽  
Time Period

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variability between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of surface air temperature (SAT). Then, we define permafrost distribution over Eurasia by SAT conditions. In a first validation step on present climate (CTRL period), GAM shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a stochastic generator approach through a Multinomial Logistic Regression (MLR), which directly models the probabilities of local permafrost indices. The obtained permafrost distributions appear in a better agreement with data. In both cases, the provided local information reduces the inter-variability between climate models. Nevertheless, this also proves that a simple relationship between permafrost and the SAT only is not always sufficient to represent local permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. Our SDMs do not significantly improve permafrost distribution and do not reduce the inter-variability between climate models, at this period. We show that LGM permafrost distribution from climate models strongly depends on large-scale SAT. The differences with LGM data, larger than in the CTRL period, reduce the contribution of downscaling and depend on several factors deserving further studies.

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