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Global Climate Change and Its Impacts

tativeness of the data to some extent. For satellite data, researchers evaluated its observation
accuracy across different seasons and surface types. Results show that satellite temperature
retrieval achieves higher precision (±0.3°C error) in vegetation-covered areas, while larger
retrieval errors (up to ±1.5°C) occur in snow and ice-covered regions due to the complex
optical properties of ice and snow. To address this, the research team developed a satellite
data calibration algorithm specifically for ice-snow areas, incorporating factors like spectral
reflectance characteristics and thickness variations of ice and snow, thereby enhancing the
representativeness of satellite data in these regions.
From a modeling perspective, consider the simulation of 20th-century global tempera-
ture changes by a global climate model as an example. The model’s simulation results align
with actual observational data in overall trends, both showing temperature increases. How-
ever, deviations exist in specific periods and regions, such as simulated temperatures being
1-1.5°C lower than actual observations in parts of Europe during the 1970s-1980s. Through
sensitivity analysis of model parameters, it was found that inaccurate parameter settings for
cloud radiative effects in the model led to significant deviations between simulated and ob-
served temperatures in cloud-abundant regions. The improvement direction involves further
research on cloud physical processes, incorporating the latest observational data (e.g., using
advanced cloud radar to observe the vertical structure of clouds) to optimize cloud radiation
parameter settings. Simultaneously, the model’s spatial resolution will be enhanced from
the originalhundred kilometersscale to a tens-of-kilometers scale to better simulate region-
al-scale temperature variation characteristics.
In terms of information communication, the initial research report only presented sin-
gle-trend data of global average temperature increase, without fully considering data un-
certainty. During subsequent refinements, uncertainty expressions such as error ranges and
confidence intervals were introduced. For example, the report states that “according to com-
prehensive data analysis, the global average temperature has risen by 0.8 - 1.2°C since the
20th century, with a confidence interval of 0.7 - 1.3°C at the 95% confidence level,” enabling
readers to more comprehensively understand the reliability of the research results. Mean-
while, the research team also created visual charts to intuitively present information such as
temperature change trends and error ranges, such as using bar charts to display temperature
increase magnitudes and their error ranges across different periods, allowing non-profession-
als toeasily understandthe research findings.
Analysis of this case reveals that the verification and evaluation of climate change evi-
dence constitutes a complex and ongoing process. It requires comprehensive application of
various methods to scrutinize multiple aspects including data sources, data quality, model
simulations, and information communication. For identified issues such as data bias, insuffi-
cient representativeness, inaccurate model parameters, and incomplete information delivery,
targeted improvement measures should be implemented. These include data calibration, op-
timizing station layout, refining model algorithms, and enhancing information presentation.

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