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Chapter II Evidence for Global Climate Change

a maritime area is missing for a consecutive week - it would be challenging to track the
evolution of marine meteorological systems in that area, impacting the continuity of ocean
climate-related research. The completeness of data coverage range alsoveryCrucial, whether
for meteorological station observation data or satellite data, coverage should extend to the
required geographical areas and temporal spans of the research as comprehensively as pos-
sible. When studying climate change in a specific region, long-term data gaps in parts of the
area may lead to biased conclusions. For example, when analyzing the relationship between
water resource variations and climate change in a river basin, persistent absence of meteoro-
logical data in upstream areas would hinder a holistic assessment of the climate-hydrological
response mechanisms across the entire watershed.
(4) Data Source Reliability Indicator
The reliability of data sources directly affects the credibility of climate change ev-
idence. For meteorological station observation data, the qualifications and reputation of
the observing institutions are crucial references. Renowned national-level meteorological
observation agencies, due to their strict observation protocols, advanced technical equip-
ment, and professional research teams, generally provide data with higher reliability. These
institutions have accumulated rich observational experience during long-term development,
established comprehensive quality control systems, and can ensure data accuracy and sta-
bility. The standardization of data collection methods is also vital, as methods following
international standards and industry norms guarantee data quality and comparability. For ex-
ample, when measuring precipitation, using standardized rain gauge installation methods and
measurement procedures ensures the accuracy and reliability of precipitation data. Improper
installation, such as tilted rain gauges, could result in measured precipitation values being
lower than actual amounts, compromising data quality. For satellite data, the capabilities of
satellite manufacturers and R&D institutions, along with the scientific objectives and design
schemes of satellite missions, all influence data quality and reliability. Satellites developed
by institutions with extensive aerospace experience and advanced technologies offer better
quality assurance. For instance, satellites developed by top aerospace institutions incorporate
climate monitoring requirements in their designs, equipped with high-precision sensors and
advanced data processing systems. Regarding reanalysis data, the numerical models usedThe
reliability of reanalysis data is determined by the performance of numerical models and the
advancement of data assimilation algorithms. Advanced numerical models can more accu-
rately simulate physical processes in the climate system, while efficient data assimilation al-
gorithms enable better integration of multi-source observational data, thereby enhancing the
quality of reanalysis data. For example, the new generation of climate numerical models can
more finely depict complex processes such as atmospheric circulation and ocean currents.
When combined with advanced data assimilation algorithms, these models organically inte-
grate observational data from diverse sources to generate reanalysis data products that more
closely resemble real climate states.

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