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Chapter 1 Scientific Basis of Global Climate Change

ly exist, such as the World Meteorological Organization (WMO) Data Sharing Hub and the
International Oceanographic Data and Information Exchange (IODE) platform, various ob-
stacles persist in practical data sharing processes. Some institutions and researchers maintain
a cautious stance on data sharing due to concerns such as intellectual property protection and
data security. Developing countries face challenges in data acquisition and participation in
data sharing due to technological and financial constraints. To overcome these barriers, it is
necessary to strengthen international cooperation and formulate fair and equitable data shar-
ing policies. Clear definitions of rights and obligations for data providers and users should
be established, safeguarding the intellectual property of data providers while ensuring wide-
spread application of data within reasonable boundaries. Increased technical support and
funding should be directed to developing countries to enhance their data management and
sharing capabilities. By establishing trust mechanisms, global researchers can be encouraged
to actively participate in data sharing, collectively advancing the development of climate
change research.

VI. Uncertainty in Climate Change Projections and Improvement
Strategies

Climate change projection is an extremely complex task with multiple sources of un-
certainty that significantly affect the accuracy and reliability of prediction results. Model
structure constitutes one of the major sources of uncertainty. Existing climate change models
inevitably employ simplifications and approximations when describing physical, chemical,
and biological processes within the climate system. For instance, in simulating cloud forma-
tion, development, and dissipation processes, models struggle to precisely capture every de-
tail due to the extreme complexity of cloud microphysical processes. Different cloud param-
eterization schemes exhibit variations in describing cloud optical and radiative properties,
leading to deviations in model simulations of radiative transfer and precipitation processes.
Additionally, interactions between different spheres within the climate system—such as cou-
plings between atmosphere-ocean and land ecosystems—are difficult to fully and accurately
represent in models. These structural imperfections mean different models may produce sub-
stantially divergent results when simulating identical climate scenarios.
The uncertainty in initial conditions also significantly impacts climate change predic-
tions. The climate system exhibits highly nonlinear characteristics, where minor differences
in initial states may be continuously amplified during long-term evolution, leading to vastly
different prediction outcomes. This phenomenon is known as the “butterfly effect.” In prac-
tice, due to limitations in observational technology and uneven spatiotemporal distribution
of observation networks, it is difficult to obtain precise initial states of the climate system.
For instance, in ocean observations, measurements of temperature, salinity, and currents in
deep-sea regions remain relatively scarce, resulting in substantial uncertainties in the initial
conditions for ocean models. In atmospheric observations, sparse meteorological observa-

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