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Global Climate Change and Its Impacts

mountainous regions, deserts, or oceanic areas, sparse station coverage may fail to compre-
hensively capture climate information from these regions. Taking the Qinghai-Tibet Plateau
as an example, the limited number of meteorological stations across this vast plateau region,
combined with its complex terrain, makes it difficult to fully monitor local climate features,
thereby affecting the comprehensive understanding of climate change patterns across the pla-
teau. Therefore, reasonably increasing the number of meteorological stations in remote areas
and optimizing station layouts can enhance the spatial representativeness of observational
data. For satellite data, the completeness of coverage is crucial. Some satellites may have ob-
servation blind spots or low-frequency observation areas when monitoring Earth, which af-
fects comprehensive global climate monitoring. Enhancing spatial representativeness can be
achieved through satellite constellation networks or optimized orbital designs to expand cov-
erage. For instance, coordinated observations by satellites in different orbits can fill gaps in
single-satellite monitoring, improving the comprehensiveness of global climate observation.
Regarding temporal representativeness, the time span of data determines whether long-term
climate change trends can be captured. Long-term time series of meteorological station data,
such as century-long records of temperature and precipitation, can clearly reveal climate evo-
lution patterns over extended periods. Short-term data may be influenced by random factors
and fail to accurately reflect long-term climate trends. For example, inferring precipitation
trends in a region based solely on five-year data might lead to deviations caused by extreme
precipitation events in specific years, failing to demonstrate actual long-term precipitation
patterns. Data update frequency also affects temporal representativeness. In rapidly chang-
ing climate contexts, high-frequency updated data can more promptly reflect current climate
states, providing strong support for real-time monitoring and climate change prediction. For
instance, in areas with frequent extreme weather events, real-time meteorological data helps
researchers quickly understand event dynamics and assess climate change impacts. In hur-
ricane-prone regions, real-time updates of wind speed and atmospheric pressure enable re-
searchers to timely grasp storm intensity changes, providing critical information for disaster
prevention and mitigation.
(3) Data Completeness Metrics
Complete data is indispensable for comprehensive analysis of climate change. Data-
missing rateis an important indicator for measuring data completeness. In meteorological
station observation data, if precipitation data for a particular month is missing multiple
times, it may lead to deviations in the analysis of that month’s precipitation characteristics
and their relationship with climate change. For example, when studying the association be-
tween seasonal precipitation variations and climate change in a region, the absence of key
monthly precipitation data would make it difficult for analysis results to accurately reflect
the precipitation patterns in that area. For satellite data, if observation data for a specific
region is missing continuously for multiple days, it would affect continuous monitoring
and analysis of climate conditions in that region. Suppose satellite cloud imagery data for

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