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Chapter Three Impacts of Global Climate Change

disturbances are like grains of sand mixed into pearls, potentially compromising the accu-
racy of analytical outcomes. To address this challenge, researchers typically employ data
smoothing techniques such as moving average method and exponential smoothing method
for preprocessing raw data. The moving average method smooths short-term fluctuations and
highlights long-term trends by calculating the average of data within a specific time window,
while the exponential smoothing method assigns different weights to data points based on
their temporal proximity, giving higher weights to recent data to more sensitively reflect
trend changes. These preprocessing methods effectively enhance the reliability and accuracy
of time series analysis results, ensuring the authenticity of long-term cumulative effect infor-
mation extracted from climate data.
(2) Cumulative Sum Model Approach
The Cumulative Sum (CUSUM) model demonstrates unique advantages and powerful
capabilities in quantifying the long-term cumulative effects of climate change. This model
acts like a faithful recorder, accurately and effectively tracking the gradual accumulation
process of climate change impacts across temporal dimensions. Taking the study of cumu-
lative effects in sea-level rise – a high-profile climate change phenomenon – as an example,
the application of the CUSUM model reveals a clear process. First, through a global network
of sea-level monitoring stations, researchers continuously and precisely obtain annual mea-
surements of sea-level rise relative to the previous year. These seemingly insignificant annual
variations in fact encapsulate tremendous energy from Earth’s climate transformations.
Subsequently, the core algorithm of the cumulative sum model is applied to sequentially
accumulate these annual sea level rise values. Assuming an initial baseline value for sea level
height is set at the starting point, with a 3 mm rise in the first year and an additional 3.2 mm
rise in the second year based on the first year’s level. Following the computational rules of
the cumulative sum model, the cumulative sea level rise relative to the initial baseline by the
second year would be 3 + 3.2 = 6.2 mm. As time progresses, continuing this cumulative sum-
mation of annual sea level changes vividly illustrates the long-term accumulation process of
sea level rise, akin to gradually unrolling a scroll painting. The precise quantitative results
obtained through this process hold immeasurable value for assessing the impacts of sea level
rise on coastal ecosystems, infrastructure, and other aspects. It provides crucial data support
for coastal regions to formulate flood prevention plans, optimize urban planning layouts, and
protect marine ecosystems, ensuring these decisions are grounded in scientific accuracy.
In the field of ecosystem research, the Cumulative Sum Model demonstrates remarkable
capabilities for quantifying the long-term cumulative impacts of climate change on biodiver-
sity. As the core component of Earth’s ecosystems, biodiversity exhibits extreme sensitivity
to climate change. By systematically monitoring annual variations in key biodiversity indica-
tors such as species count, population size, and species distribution range over extended peri-
ods, the Cumulative Sum Model enables the progressive summation of these yearly changes.
For instance, if a region’s avian species count shows a decreasing trend at an annual rate of 0.5

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