An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set

Global Mean Surface Temperature (GMST) is among the most important indicators of climate change, and its associated uncertainties affect the assessment of historical warming and the formulation of mitigation and adaptation policies. China‐MST 3.0 is a newly updated global surface temperature data set that merges China‐LSAT 2.1 for Land Surface Air Temperature (LSAT) and ERSST v6 for Sea Surface Temperature (SST). In this study, we develop a systematic and traceable uncertainty analysis framework for the construction process of this data set. Specifically, we comprehensively evaluate three components of LSAT uncertainty: observation, analysis, and coverage uncertainties, while describing SST uncertainty in terms of both parametric and reconstruction uncertainties. We also provide a quantitative assessment of the spatial and temporal evolution of these uncertainties. The results show that LSAT uncertainty is generally larger than that of SST and is mainly driven by coverage uncertainty. The overall uncertainty in GMST shows a significant downward trend, with the annual 1 σ uncertainty falling below 0.03°C in recent decades, indicating high data reliability. However, uncertainty was high during the second‐half of the 19th century and remains large at high latitudes in the Southern Hemisphere. Comparative analyses indicate that China‐MST 3.0 is broadly consistent with other data sets in both the magnitude and temporal evolution of GMST uncertainty. These findings demonstrate the utility of China‐MST 3.0 as a valuable tool for evaluating global warming since the 1850s.

An online Bayesian filtering framework for Gaussian process regression: Application to global surface temperature analysis

Four earth-viewing nonscanning active cavity radiometers of the ERBS (Earth Radiation Budget Satellite) have been measuring the radiation arising from the earth-atmosphere system since its' launch day, October 5, 1984. The ERBS spacecraft was placed in a non-sun-synchronous trajectory inclined at 57°. Two radiometers out of four, namely the wide field-of-view total (WFOV-T) radiometer which measures the radiation in the total spectral band of 0.2 - 100 μm, and the wide-field-of-view shortwave (WFOV-SW) radiometer measures the Earth's reflected radiation in the wavelength region of 0.2 - 5 μm were used in this study. These sensors were calibrated continuously by observing the in-flight internal black bodies as well as the Sun every two weeks. The WFOV-T channel was found very stable within 0.1%. The monthly flux values of the ERBS nonscanning active cavity radiometers at satellite altitude and the corresponding NCDC (National Climatic Data Center) global surface temperature data for the period of fifteen years (1985-1999) were used in this paper. The effect of Mt. Pinatubo eruption is very clearly noticeable in the running trends of both WFOV-T and WFOV-SW radiometric measurements. Further the resulting twelve month running trends derived from the outgoing longwave radiation was found to follow the twelve month running trend determined from the global surface temperature data set. Both trends are real and increasing. The "global-cooling-like" event caused by the Mt. Pinatubo eruption was also found under both day and nighttime conditions.

Chapter 2 - A Critical Look at Surface Temperature Records

There has been a heightened interest in sea surface temperature (SST) measurements during the past two decades, particularly on a global scale, due largely to the advent of several El Nino episodes and increasing worldwide concern about global warming. Because of the continuous global measurements of SST that satellites can provide they play a fundamental role in acquiring the data sets necessary for studies of such global processes. However, the satellite data still need to be validated against in-situ measurements in order to assess the accuracy of satellite SST retrieval algorithms. Since 1997 radiometric sea surface skin temperatures have been recorded monthly using a TASCO radiometer along a cross shelf transect extending 40 km offshore from a coastal location north of Perth, Western Australia. This SST measurement program is one component of the Hillarys Transect, an ongoing multidisciplinary oceanographic study which aims to quantify the seasonal variation of physical and biological variables in the coastal Indian Ocean. Validation of satellite SST retrieval algorithms is the primary aim of the SST measurement program component of the Hillarys Transect, which also aims to develop a data set which captures the seasonal variation of SST off the coast of southern Western Australia (WA). The radiometric SST measurements are made at 9 stations along the transect and each measurement is calibrated using a portable water-filled blackbody unit. Though the study is low budget, the seasonal variation in SST is clearly captured by the data set and good agreement between in-situ and satellite SST estimates are obtained. Calibration of the radiometer is discussed along with the seasonal cycle of SST off the WA coast and the suitability of the instrumentation for validation of remotely sensed SST.

Satellite measurements of global temperature began in 1979. According to the results of these measurements, the correlation between the global temperature and ocean temperature is very good, with a correlation coefficient of 0.99. The global temperature is controlled by the ocean temperature. The ocean temperature is not always constant but changes periodically, with high and low temperatures occurring repeatedly. This phenomenon is known as the El Niño or La Niña phenomenon. El Niño and La Niña phenomena are monitored by temperature changes in a specific area of the equator in the Pacific Ocean and are called the Oceanic Niño Index (ONI). When the ONI fluctuates significantly, El Niño and La Niña phenomena occur. A comparison of the ONI data from the National Oceanic and Atmospheric Administration (NOAA) and the global temperature data reveals that the temperature change throughout the entire Earth occurred approximately five months after the ONI change. At the western end of the Pacific Ocean, the direction of the warm current changes, and a warm current flows northward via the coast of the Japanese Islands. Even in such a unique location, the temperature change during the El Niño phenomenon changed five months later than did the change in the ONI value. Measurements of atmospheric CO2 concentrations at the Mauna Loa Observatory in Hawaii began in 1958. We compared these CO2 concentration changes with the above global temperature changes via NOAA data. As a result, we found that changes in global CO2 concentrations appeared approximately four months after global temperature changes. The CO2 concentration increases with increasing temperature. El Niño and La Niña phenomena are observed as small fluctuations in atmospheric CO2 concentrations. This is mainly due to increased plant respiration and accelerated decomposition of organic matter in soils due to rising temperatures. CO2 emissions from the ocean are also thought to have a significant impact, but quantitative investigations are a future task. On the other hand, compared with the global CO2 balance, CO2 emissions from anthropogenic activities are low. Our recent research results revealed that temperature and CO2 changes are correlated, but CO2 changes are the result of temperature changes, and we have not found that CO2 changes cause temperature changes.

The measurement of global sea surface temperature (SST) from space is well established with 20 years of useful data already acquired, but the more stringent sampling requirements and the higher degree of accuracy now demanded for applications in both climate monitoring and operational oceanography are increasingly difficult to meet with the standard meteorological polar orbiting sensors that have been the basic sensors used for global SST mapping. The established methods and sensors for measuring SST, both in situ and in space, are reviewed, compared, and their major limitations are identified. Mention is made of phenomena which complicate an apparently simple measurement, including diurnal stratification, the presence of clouds and the contamination of the stratosphere by volcanic aerosols. Recent developments in remote sensing of SST are mentioned, noting the improved microwave sensors now becoming available, the calibrated infrared sensors planned for geostationary platforms, and weighing the benefits of merging these data. The conventional buoy-calibration of SST measurements from space is complicated by the variable thermal structure of the upper few metres of the ocean. The recent improvement of radiometers for ship deployment has led to better understanding of the thermal skin of the ocean which suggests a new approach for the validation of SST algorithms based on radiation transfer models. Finally, a future strategy is outlined for combining measurements from many types of sensor in order to achieve the required accuracy and sampling rate of SST data products, and to identify some of the remaining scientific challenges in this field.

We investigated the scaling properties of two datasets of the observed near-surface global temperature data anomalies: the Met Office and the University of East Anglia Climatic Research Unit HadCRUT4 dataset and the NASA GISS Land–Ocean Temperature Index (LOTI) dataset. We used detrended fluctuation analysis of second-order (DFA2) and wavelet-based spectral (WTS) analysis to investigate and quantify the global pattern of scaling in two datasets and to better understand cyclic behavior as a possible underlying cause of the observed forms of scaling. We found that, excluding polar and parts of subpolar regions because of their substantial data inhomogeneity, the global temperature pattern is long-range autocorrelated. Our results show a remarkable heterogeneity in the long-range dynamics of the global temperature anomalies in both datasets. This finding is in agreement with previous studies. We additionally studied the DFA2 and the WTS behavior of the local station temperature anomalies and satellite-based temperature estimates and found that the observed diversity of global scaling can be attributed both to the intrinsic variability of data and to the methodology-induced variations that arise from deriving the global temperature gridded data from the original local sources. Finally, we found differences in global temperature scaling patterns of the two datasets and showed instances where spurious scaling is introduced in the global datasets through a spatial infilling procedure or the optimization of integrated satellite records.

Abstract. We present a new gridded data set of air temperature change across global land and ocean extending back to the 1780s. This data set, called the GloSAT reference analysis, has two novel features: it uses marine air temperature observations rather than the sea surface temperature measurements typically used by pre-existing data sets, and it extends further into the past than existing merged land and ocean instrumental temperature records which typically estimate temperature changes from the middle to late 19th century onwards. New estimates of diurnal-heating biases in marine air temperatures have enabled the use of daytime observations, extending the data set further into the past compared to nighttime-only marine air temperature data. The data set uses an extended version of the CRUTEM5 station database over land areas, incorporating newly available bias adjustments for non-standard thermometer enclosures used prior to the adoption of Stevenson screens and new climatological normal estimates for stations with limited data in the 1961–1990 baseline period. Land and marine temperature anomalies are combined to produce a gridded data set following the methods developed for HadCRUT5. The GloSAT global and hemispheric temperature anomaly series show close agreement with those based on sea surface temperature for much of the overlapping period of their records but with slightly less warming overall. The GloSAT reference analysis is available from https://doi.org/10.5285/a2519624a593402a83246bd359d098be (Morice et al., 2025b), the GloSATLAT data set is available from https://doi.org/10.5285/ef237f578329487eb02fb42f9db56bb2 (Morice et al., 2025a), and the GloSATMAT data set is available from https://doi.org/10.5285/e6251bf935304cfbb9c9269dc7757a35 (Cornes et al., 2025b).

A limited comparison over the Northern Hemisphere oceans has been made between sea surface temperatures obtained from “Marine lkcks,” air temperatures over the ocean obtained from the same decks, and the historical file of hydrographic data. The intercomparison of these data suggest the following conclusions. 1) The SST observations have been contaminated by a systematic conversion from bucket to injection measurements. The bias so introduced may constitute as much as 30 to 50% of the observed change in sea surface temperature since the turn of the century. 2) The same bias effects are apparent in data sets that are alleged to contain bucket measurements of sea surface temperature only. 3) The behavior of the temperature field over the ocean appears to have significant and substantial differences from the behavior of estimated temperature changes over the Northern Hemisphere land masses. It seems clear that a reliable estimate of hemispheric or global temperature cannot be made without including ...

In recent years, the earth is burning, whether global warming and its influencing factors, based on time series and gray prediction model, put forward the hypothesis and conclusion, provide the data of various factors, to evaluate the past temperature level, to predict the future global temperature level and its trend and analyze the influencing factors. For problem 1, first, based on the analysis of the global average air temperature data from March 2012-February 2022 and March 2022-October 2022, it is unscientific that the global temperature rise in March 2022 led to a larger rise than in the past 10 years. Second, the ARIMA model and the gray prediction model are used to describe the past global temperature levels and predict the future global temperature levels, respectively. The ARIMA model and the grey prediction model show that the global average annual temperature change has been stable in the past, while the prediction that the average temperature of 20.00°C in 2050 or 2100 is inaccurate. The grey prediction model shows that the global temperature levels will continue to rise in the future, and shows that the average temperature in 2,289 observations is expected to reach 20.00°C. According to the forecast, the global average temperature will not, as long as natural resources are properly used, reach 20.00°C. For problem 2, We first divide the Earth into the southern and northern hemisphere, and second into tropical and southern temperate and northern temperate zones. The linear relationship between global average temperature and the two conditions is discussed separately. According to the results, it significantly shows a strong correlation between the global average temperature and time. If unchecked, the global average temperature will grow over time. Second, using the principal component analysis method shows that may affect the global average temperature in the Middle East energy consumption, global hydropower consumption, world oil consumption, global niobium minerals, the world gas proven reserves, global crude oil production, global cadmium production, and to the results are tested, concluded that the principal component analysis method to influence the main indicators of the global average temperature method is more accurate. Therefore, the suggestions for global temperature change are made: to strive to reduce methane emissions; to further strengthen the strategic deployment and practical actions for energy revolution and low-carbon development, while increasing the intensity of energy low-carbon transformation. For problem 3, We propose the feasibility of this model evaluation and prediction results on slowing down or curbing global warming trends. One is direct emissions reduction, with countries signing contracts to force their carbon emissions and unite against global warming. Reduce heat emissions should be put on everyone's daily living habits, with a limited use of daily necessities, to achieve limited production, limited processing, so as to curb industrial flooding, reduce heat emissions. Second, the indirect emission reduction. With the development of industry, carbon dioxide emissions are constantly increasing, leading to a series of problems such as global warming and sea level rise. Afforestation can absorb the carbon dioxide contained in the atmosphere and reduce the greenhouse gas content in the atmosphere. Therefore, we must realize the importance of afforestation, take appropriate measures to improve the effect of afforestation, and better alleviate global warming.

<strong class="journal-contentHeaderColor">Abstract.</strong> Global surface temperature observational datasets are the basis of global warming studies. In the context of increasing global warming and frequent extreme events, it is essential to improve the coverage and reduce the uncertainty in global surface temperature datasets. The China global Merged Surface Temperature Interim version (CMST-Interim) is updated to CMST 2.0 in this study. The previous CMST datasets were created by merging the China global Land Surface Air Temperature (C-LSAT) with sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The CMST 2.0 contains three variants: CMST 2.0 <span class="inline-formula">−</span> Nrec (without reconstruction), CMST 2.0 <span class="inline-formula">−</span> Imax, and CMST 2.0 <span class="inline-formula">−</span> Imin (according to their reconstruction area of the air temperature over the sea ice surface in the Arctic region). The reconstructed datasets significantly improve data coverage, whereas CMST 2.0 <span class="inline-formula">−</span> Imax and CMST 2.0 <span class="inline-formula">−</span> Imin have improved coverage in the Northern Hemisphere, up to more than 95 %, and thus increased the long-term trends at global, hemispheric, and regional scales from 1850 to 2020. Compared to CMST-Interim, CMST 2.0 <span class="inline-formula">−</span> Imax and CMST 2.0 <span class="inline-formula">−</span> Imin show a high spatial coverage extended to the high latitudes and are more consistent with a reference of multi-dataset averages in the polar regions. The CMST 2.0 datasets presented here are publicly available at the website of figshare, <a href="https://doi.org/10.6084/m9.figshare.16929427.v4">https://doi.org/10.6084/m9.figshare.16929427.v4</a> (Sun and Li, 2021a), and the CLSAT2.0 datasets can be downloaded at <a href="https://doi.org/10.6084/m9.figshare.16968334.v4">https://doi.org/10.6084/m9.figshare.16968334.v4</a> (Sun and Li, 2021b). Both are also available at <span class="uri">http://www.gwpu.net</span> (last access: January 2022).

<strong class="journal-contentHeaderColor">Abstract.</strong> Global surface temperature observational datasets are the basis of global warming studies. In the context of increasing global warming and frequent extreme events, it is essential to improve the coverage and reduce the uncertainty in global surface temperature datasets. The China global Merged Surface Temperature Interim version (CMST-Interim) is updated to CMST 2.0 in this study. The previous CMST datasets were created by merging the China global Land Surface Air Temperature (C-LSAT) with sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The CMST 2.0 contains three variants: CMST 2.0 <span class="inline-formula">−</span> Nrec (without reconstruction), CMST 2.0 <span class="inline-formula">−</span> Imax, and CMST 2.0 <span class="inline-formula">−</span> Imin (according to their reconstruction area of the air temperature over the sea ice surface in the Arctic region). The reconstructed datasets significantly improve data coverage, whereas CMST 2.0 <span class="inline-formula">−</span> Imax and CMST 2.0 <span class="inline-formula">−</span> Imin have improved coverage in the Northern Hemisphere, up to more than 95 %, and thus increased the long-term trends at global, hemispheric, and regional scales from 1850 to 2020. Compared to CMST-Interim, CMST 2.0 <span class="inline-formula">−</span> Imax and CMST 2.0 <span class="inline-formula">−</span> Imin show a high spatial coverage extended to the high latitudes and are more consistent with a reference of multi-dataset averages in the polar regions. The CMST 2.0 datasets presented here are publicly available at the website of figshare, <a href="https://doi.org/10.6084/m9.figshare.16929427.v4">https://doi.org/10.6084/m9.figshare.16929427.v4</a> (Sun and Li, 2021a), and the CLSAT2.0 datasets can be downloaded at <a href="https://doi.org/10.6084/m9.figshare.16968334.v4">https://doi.org/10.6084/m9.figshare.16968334.v4</a> (Sun and Li, 2021b). Both are also available at <span class="uri">http://www.gwpu.net</span> (last access: January 2022).

Chapter 3.2 - Ship-Borne Thermal Infrared Radiometer Systems

Aiming at the problem of fish migration with ocean temperature changes, this paper uses the LSTM model to predict the migration trajectory of the fish. Firstly, using the global ocean temperature data set to predict the most suitable ocean surface temperature for fish to survive, and setting a sampling point in the longitude direction of 54.05°N$\sim$60.05°N to obtain the ocean surface temperature of the area in the past 50 years, preparing for further forecast; Secondly, using the LSTM model to model the ocean surface temperature data and predict the ocean surface temperature in the next 50 years, thus deriving the suitable living area of fish for survival and regarding the area closest to this temperature as the current survival address of the fish. Finally, mapping out the migration route of the fish in the next 50 years. It is verified that this method has small errors, high reliability and accuracy, and can fit the migration route of fish schools well.