scholarly journals Surface Temperature of the Planet Earth from Satellite Data

2020 ◽  
Vol 12 (2) ◽  
pp. 218 ◽  
Author(s):  
José Antonio Sobrino ◽  
Yves Julien ◽  
Susana García-Monteiro
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Air Temperature ◽  
Climate Models ◽  
Warming Trend ◽  
Sea Surface ◽  
Intergovernmental Panel ◽  
Weather Stations ◽  
Annual Means ◽  
Single Pixel

The Intergovernmental Panel on Climate Change regular scientific assessments of global warming is based on measurements of air temperature from weather stations, buoys or ships. More specifically, air temperature annual means are estimated from their integration into climate models, with some areas (Africa, Antarctica, seas) being clearly underrepresented. Present satellites allow estimation of surface temperature for a full coverage of our planet with a sub-daily revisit frequency and kilometric resolution. In this work, a simple methodology is developed that allows estimating the surface temperature of Planet Earth with MODIS Terra and Aqua land and sea surface temperature products, as if the whole planet was reduced to a single pixel. The results, through a completely independent methodology, corroborate the temperature anomalies retrieved from climate models and show a linear warming trend of 0.018 ± 0.007 °C/yr.

Twentieth-Century Surface Air Temperature over China and the Globe Simulated by Coupled Climate Models

2006 ◽  
Vol 19 (22) ◽  
pp. 5843-5858 ◽  
Author(s):  
Tianjun Zhou ◽  
Rucong Yu
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
Air Temperature ◽  
Climate Models ◽  
Eastern China ◽  
Surface Air Temperature ◽  
Future Climate Change ◽  
Intergovernmental Panel ◽  
Coupled Climate Models ◽  
Anthropogenic Forcings

Abstract This paper examines variations of the surface air temperature (SAT) over China and the globe in the twentieth century simulated by 19 coupled climate models driven by historical natural and anthropogenic forcings. Most models perform well in simulating both the global and the Northern Hemispheric mean SAT evolutions of the twentieth century. The inclusion of natural forcings improves the simulation, in particular for the first half of the century. The reproducibility of the SAT averaged over China is lower than that of the global and hemispheric averages, but it is still acceptable. The contribution of natural forcings to the SAT over China in the first half of the century is not as robust as that to the global and hemispheric averages. No model could successfully produce the reconstructed warming over China in the 1920s. The prescribed natural and anthropogenic forcings in the coupled climate models mainly produce the warming trends and the decadal- to interdecadal-scale SAT variations with poor performances at shorter time scales. The prominent warming trend in the last half of the century over China and its acceleration in recent decades are weakly simulated. There are discrepancies between the simulated and observed regional features of the SAT trend over China. Few models could produce the summertime cooling over the middle part of eastern China (27°–36°N), while two models acceptably produce the meridional gradients of the wintertime warming trends, with north China experiencing larger warming. Limitations of the current state-of-the-art coupled climate models in simulating spatial patterns of the twentieth-century SAT over China cast a shadow upon their capability toward projecting credible geographical distributions of future climate change through Intergovernmental Panel on Climate Change (IPCC) scenario simulations.

scholarly journals Global air temperature variability independent of sea-surface temperature influences

2012 ◽  
Vol 37 (1) ◽  
pp. 29-35
Author(s):  
Andrew C. Comrie ◽  
Gregory J. McCabe
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Recent Literature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Sea Surface ◽  
Sst Variability ◽  
Global Surface

Mean global surface air temperature (SAT) and sea surface temperature (SST) display substantial variability on timescales ranging from annual to multi-decadal. We review the key recent literature on connections between global SAT and SST variability. Although individual ocean influences on SAT have been recognized, the combined contributions of worldwide SST variability on the global SAT signal have not been clearly identified in observed data. We analyze these relations using principal components of detrended SST, and find that removing the underlying combined annual, decadal, and multi-decadal SST variability from the SAT time series reveals a nearly monotonic global warming trend in SAT since about 1900.

Mid-Pliocene warming: reducing discrepancies between geological archives and climate models in the NE Atlantic and Nordic Seas

2021 ◽  
Author(s):  
Jonathan Hall ◽  
Stephen Jones ◽  
Tom Dunkley Jones ◽  
James Bendle
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Recent Time ◽  
Sea Surface ◽  
Ne Atlantic ◽  
Irreversible Adsorption ◽  
Intergovernmental Panel ◽  
Nordic Seas ◽  
Global Surface ◽  
Dsdp Site

<p>The mid-Pliocene Warm Period (mPWP) is the most recent time slice (3.264–3.025 Ma) during which average global surface temperatures were 2–3°C warmer than preindustrial conditions, within the range estimated by the Intergovernmental Panel on Climate Change (IPCC) for the end of the 21<sup>st </sup>Century. Global mPWP sea surface temperature (SST) compilations indicate enhanced warming in the NE Atlantic and Nordic Seas, with anomalies of >6°C based on alkenone methods (Dowsett et al., 2012). However, this warming far exceeds the more conservative SST estimates (a rise of 2−3°C) predicted by the Pliocene Research, Interpretation and Synoptic Mapping (PRISM) reconstructions and leading climate models (including HadCM3). Here, we present new mid-Pliocene alkenone SST records from four regional drilling sites (IODP Site U1308, DSDP Site 552, ODP Site 642 and ODP Site 907) to further examine the magnitude of warming in the NE Atlantic and Nordic Seas, and to evaluate regional discrepancies between proxy and model SST estimates. We demonstrate mid-Pliocene SSTs peaked up to 21.5°C and 19.7°C in the NE Atlantic and Nordic Seas, respectively, consistent with existing studies (Robinson et al., 2008; Robinson, 2009). However, we reveal the majority of these SST estimates are derived from GC injections of relatively low total alkenone concentrations (<50 ng/µl), which are susceptible to warming biases caused by chromatographic irreversible adsorption (Grimalt et al., 2001). We subsequently filtered and applied a mathematical correction to our new data to rectify for these warming biases, which results in a reduction in mPWP SSTs, by up to 3.2°C, across all four sites. The corrected (and cooler) alkenone SST records indicate the magnitude of warming in the NE Atlantic and Nordic Seas may be significantly less than previously thought, helping to reduce and explain regional discrepancies between proxy- and model-based SST reconstructions.</p>

scholarly journals Increasing Trends in Air and Sea Surface Temperature in the Central Adriatic Sea (Croatia)

2021 ◽  
Vol 9 (4) ◽  
pp. 358
Author(s):  
Ognjen Bonacci ◽  
Duje Bonacci ◽  
Matko Patekar ◽  
Marco Pola
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Adriatic Sea ◽  
Coastal Region ◽  
Sea Surface ◽  
The Mean ◽  
Increasing Trends

The Adriatic Sea and its coastal region have experienced significant environmental changes in recent decades, aggravated by climate change. The most prominent effects of climate change (namely, an increase in sea surface and air temperature together with changes in the precipitation regime) could have an adverse effect on social and environmental processes. In this study, we analyzed the time series of sea surface temperature and air temperature measured at three meteorological stations in the Croatian part of the Adriatic Sea. To assess the trends and variations in the time series of sea surface and air temperature, different statistical methods were employed, i.e., linear and quadratic regressions, Mann–Kendall test, Rescaled Adjusted Partial Sums method, and autocorrelation. The results evidenced increasing trends in the mean annual sea surface temperature and air temperature; furthermore, sudden variations in values were observed in 1998 and 1992, respectively. Increasing trends in the mean monthly sea surface temperature and air temperature occurred in the warmer parts of the year (from March to August). The results of this study could provide a foundation for stakeholders, decision–makers, and other scientists for developing effective measures to mitigate the negative effects of climate change in the scattered environment of the Adriatic islands and coastal region.

scholarly journals Sea Surface Temperature intercomparison in the framework of the Copernicus Climate Change Service (C3S)

2021 ◽  
pp. 1-102
Author(s):  
Chunxue Yang ◽  
Francesca Elisa Leonelli ◽  
Salvatore Marullo ◽  
Vincenzo Artale ◽  
Helen Beggs ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Spatial Patterns ◽  
Early Period ◽  
Warming Trend ◽  
Sea Surface ◽  
Western Boundary ◽  
Scientific Quality ◽  
Boundary Current

AbstractA joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free Sea Surface Temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003-2018). The main differences between each product are located in western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982-2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users' convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g. El Nino).

scholarly journals Impact of Sea Surface Temperature and Surface Air Temperature on Maximizing Typhoon Rainfall: Focusing on Typhoon Maemi in Korea

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jeonghyeon Choi ◽  
Jeonghoon Lee ◽  
Sangdan Kim
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Water Vapour ◽  
Air Temperature ◽  
Climate Models ◽  
Initial Conditions ◽  
Geographical Location ◽  
Surface Air Temperature ◽  
Sea Surface ◽  
Typhoon Rainfall

In this study, the effects of surface air temperature (SAT) and sea surface temperature (SST) changes on typhoon rainfall maximization are analysed. Based on the numerically reproduced Typhoon Maemi, this study tried to maximize the typhoon-induced rainfall by increasing the amount of saturated water vapour in the atmosphere and the amount of water vapour entering the typhoon. Using the Weather Research and Forecasting (WRF) model, which is one of the regional climate models (RCMs), the rainfall simulated by WRF while increasing the SAT and SST to various sizes at initial conditions and boundary conditions of the model was analysed. As a result of the simulated typhoon rainfall, the spatial distribution of total rainfall depth on the land due to the increase combination of SAT and SST showed a wide variety without showing a certain pattern. This is attributed to the geographical location of the Korean peninsula, which is a peninsula between the continent and the ocean. In other words, under certain conditions, typhoons may drop most of the rainfall on the southern sea of the peninsula before landing on the peninsula. For instance, the 6-hour duration maximum precipitation (MP) in Busan Metropolitan City was 472.1 mm when the SST increased by 2.0°C. However, when the SST increased by 4.0°C, the MP was found to be 395.3 mm, despite the further increase in SST. This indicates that the MP at a particular area and the increase in temperature do not have a linear relationship. Therefore, in order to maximize typhoon rainfall, it is necessary to repeat the numerical experiment on various conditions and search for the combination of SAT and SST increase which is most suitable for the target typhoon.

scholarly journals Hubungan Antara Kelembaban Relatif Dengan Beberapa Variabel Iklim Dengan Pendekatan Korelasi Pearson di Samudera Hindia

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Miftahuddin Miftahuddin ◽  
◽  
Ananda Pratama ◽  
Ichsan Setiawan ◽  
◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Sea Surface ◽  
Aceh Province ◽  
Relationship Of ◽  
The Relationship

The earth's climate is constantly changing, it's just that climate change in the past took place naturally. But until now, climate change has been very closely related to human activity, so the nature of the event has become faster and more drastic. Relative humidity is a parameter that can affect climate change in Indonesia, especially in Aceh Province. Aceh province is one of the provinces located on the island of Sumatra and directly facing the Indian Ocean. Thus, Aceh Province has a considerable impact on climate change. Changes in relative humidity will lead to changes in climate elements. There are several climate elements including air temperature, rainfall, sea surface temperature, wind speed, solar radiation, and dynamic altitude. One of the methods used to look at the relationship of each climate element is the Correlation method. The purpose of this study is to find out the relationship of each variable of the climate elements. The results showed that the relationship between variables X1 (air temperature) and X3 (sea surface temperature) had the highest closeness relationship with a positive correlation value of 0.77. The lowest closeness relationships are variables X2 (rainfall) and X4 (wind speed) with a negative weak correlation value of -0.01.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals CMIP6 model-based analog forecasting for the seasonal prediction of sea surface temperature in the offshore area of China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Weiying Peng ◽  
Quanliang Chen ◽  
Shijie Zhou ◽  
Ping Huang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Global Climate Models ◽  
Sea Surface ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Initial Field ◽  
Offshore Area ◽  
Model Based

AbstractSeasonal forecasts at lead times of 1–12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0°–45°N, 105°–135°E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981–2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

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