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Global Climate Change and Its Impacts

temperature and salinity distribution, thereby better reflecting marine climate conditions.
For atmospheric data fusion, variational assimilation methods optimize atmospheric state
descriptions in models by minimizing discrepancies between observational data and model
simulations, incorporating meteorological station measurements of pressure, temperature,
humidityand satellite-observedradiation brightness temperatureinto atmospheric numerical
models. This improves simulation capabilities for atmospheric circulation and weather sys-
tem evolution.
Strict quality control is multi-source dataA critical aspect of multi-source data integra-
tion. For meteorological station observation data, regular instrument calibration must be con-
ducted to ensure measurement accuracy. Establish data quality control standards to perform
completeness and consistency checks on data, eliminating outliers and erroneous data. For
example, verify whether temperature data contains values exceeding plausible ranges, and
check if precipitation data time series demonstrate logical temporal consistency. For satellite
data, rigorous radiometric calibration and atmospheric correction must be implemented to
minimize the impact of sensor errors and atmospheric interference. Employ cross-valida-
tion methods by comparing similar datasets obtained from different satellites or sensors to
assess data reliability. For reanalysis data, numerical model uncertainties must be evaluated
to analyze deviations between reanalysis data and independent observational data. For iden-
tified systematic biases, implement necessary corrections through comparative analysis with
high-quality observational data to enhance the accuracy of reanalysis data.
Through the integration and validation of multi-source climate data, the advantages of
various data types can be fully utilized to overcome the limitations of single data sources,
thereby enhancing the data quality and accuracy of climate change research. The integrated
data can be applied to more precise climate trend analysis, climate model validation, climate
change impact assessment, and other research endeavors, providing robust data support for
gaining deeper insights into climate change processes, predicting future climate trends, and
formulating effective response strategies.

II. Reliability Assessment Indicator System for Climate Change Basis

In the field of climate change research, establishing a scientific and rational indicator
system to assess the reliability of climate change evidence is crucial. This not only helps re-
searchers discern the quality of data and information to ensure the accuracy and credibility
of research conclusions, but also provides policymakers with a solid and reliable scientific
basis for formulating more effective climate change response strategies. The indicator sys-
tem primarily covers key aspects such as data accuracy, representativeness, completeness,
and reliability of data sources. These interconnected dimensions collectively form a rigorous
framework for evaluating the reliability of climate change evidence.
(1) Data Accuracy Indicators
Data accuracy serves as the cornerstone for assessing the reliability of climate change

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