IELTS Reading: Tác động của AI đến phân tích dự đoán trong khoa học khí hậu – Đề thi mẫu có đáp án chi tiết

Mở bài

Chủ đề về trí tuệ nhân tạo (AI) và khoa học khí hậu đang trở thành một trong những đề tài phổ biến nhất trong IELTS Reading, xuất hiện thường xuyên từ năm 2020 đến nay. Sự kết hợp giữa công nghệ tiên tiến và vấn đề môi trường toàn cầu tạo nên những bài đọc học thuật đầy thử thách, đòi hỏi khả năng hiểu sâu về cả thuật ngữ kỹ thuật lẫn khái niệm khoa học phức tạp.

Trong bài viết này, bạn sẽ được trải nghiệm một đề thi IELTS Reading hoàn chỉnh gồm 3 passages với độ khó tăng dần từ Easy đến Hard. Bạn sẽ làm quen với đa dạng các dạng câu hỏi thường gặp như Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác. Mỗi passage được thiết kế sát với format thi thật, kèm theo đáp án chi tiết và giải thích cặn kẽ giúp bạn hiểu rõ cách tiếp cận từng loại câu hỏi.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, đặc biệt hữu ích cho những ai đang nhắm đến band 7.0-8.0 và muốn làm quen với chủ đề khoa học công nghệ trong IELTS Reading.

Hướng dẫn làm bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, không bị trừ điểm khi sai. Độ khó của các passages tăng dần, với Passage 1 là dễ nhất và Passage 3 khó nhất.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (13 câu hỏi)
• Passage 2: 18-20 phút (13 câu hỏi)
• Passage 3: 23-25 phút (14 câu hỏi)

Lưu ý dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet. Không có thời gian thêm cho việc này!

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất:

1. Multiple Choice – Chọn đáp án đúng từ các phương án cho sẵn
2. True/False/Not Given – Xác định thông tin đúng, sai hay không được nhắc đến
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Matching Headings – Chọn tiêu đề phù hợp cho mỗi đoạn
5. Summary Completion – Điền từ vào chỗ trống trong đoạn tóm tắt
6. Matching Features – Nối đặc điểm với đối tượng tương ứng
7. Short-answer Questions – Trả lời câu hỏi ngắn

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IELTS Reading Practice Test

PASSAGE 1 – The Dawn of Intelligent Climate Prediction

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Climate change represents one of the most pressing challenges facing humanity in the 21st century. Scientists have long struggled to create accurate predictions about future climate patterns due to the enormous complexity of Earth’s atmospheric, oceanic, and terrestrial systems. However, the emergence of artificial intelligence (AI) technology is revolutionizing the field of climate science, particularly in the area of predictive analytics.

Traditional climate models have relied on mathematical equations that describe physical processes such as heat transfer, fluid dynamics, and chemical reactions in the atmosphere. While these models have provided valuable insights, they require massive computational resources and often take weeks or even months to produce results. Furthermore, they struggle to account for the countless variables and feedback loops that influence climate behavior. This is where AI is making a significant difference.

Machine learning, a subset of AI, excels at identifying patterns in large datasets. Climate scientists now use machine learning algorithms to analyze decades of historical climate data, including temperature records, precipitation patterns, ocean currents, and atmospheric composition. These algorithms can detect subtle relationships between variables that human researchers might miss. For instance, AI systems have identified previously unknown connections between sea surface temperatures in the Pacific Ocean and rainfall patterns in Africa, improving drought predictions in vulnerable regions.

One of the most promising applications of AI in climate science is extreme weather forecasting. Traditional methods often fail to predict the exact timing and intensity of events like hurricanes, heatwaves, or flooding. AI models, trained on vast amounts of meteorological data, can now forecast these events with greater accuracy and longer lead times. In 2019, researchers at the European Centre for Medium-Range Weather Forecasts demonstrated that their AI system could predict hurricane paths up to five days in advance with remarkable precision, potentially saving thousands of lives through earlier evacuations.

Neural networks, inspired by the structure of the human brain, represent another breakthrough in climate prediction. These sophisticated AI systems consist of interconnected layers of artificial neurons that process information in ways similar to biological brains. When fed with climate data, neural networks can learn to recognize complex patterns and make predictions without being explicitly programmed with the underlying physics. This capability is particularly valuable for modeling phenomena that are poorly understood or too complex for traditional equations.

The integration of AI into climate science has also accelerated the pace of research. Tasks that previously required months of manual analysis can now be completed in hours or even minutes. This efficiency gain allows scientists to test more hypotheses, explore different scenarios, and refine their models more quickly. Moreover, AI can continuously learn and improve its predictions as new data becomes available, creating a self-improving system that becomes more accurate over time.

Despite these advantages, AI in climate science is not without limitations. Machine learning models are only as good as the data they are trained on, and historical climate records have gaps and inconsistencies. Additionally, AI systems can sometimes make predictions based on spurious correlations – relationships that appear in the training data but have no causal basis. Climate scientists must carefully validate AI predictions against physical understanding to ensure they are scientifically sound.

Another challenge involves computational demands. While AI can speed up certain aspects of climate modeling, training sophisticated neural networks requires substantial computing power and energy. Ironically, this energy consumption contributes to the very problem climate scientists are trying to solve. Researchers are working on developing more energy-efficient algorithms to address this paradox.

Looking ahead, the synergy between human expertise and artificial intelligence promises to transform our understanding of climate change. AI will never replace human scientists, but it serves as a powerful tool that amplifies their capabilities. By combining the pattern-recognition strengths of machine learning with the theoretical knowledge and critical thinking of human researchers, the field of climate science is entering a new era of predictive capability.

Questions 1-13

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

1. According to the passage, what is the main limitation of traditional climate models?
A. They are based on incorrect mathematical principles
B. They require extensive computational resources and time
C. They cannot measure temperature accurately
D. They ignore ocean currents completely

2. Machine learning algorithms are particularly good at:
A. replacing human climate scientists
B. creating new weather patterns
C. identifying patterns in large datasets
D. reducing global temperatures

3. The AI system developed in 2019 could predict hurricane paths:
A. one day in advance
B. three days in advance
C. five days in advance
D. seven days in advance

4. Neural networks are described as being inspired by:
A. computer chips
B. the human brain
C. weather patterns
D. ocean currents

5. One limitation of AI in climate science mentioned in the passage is:
A. it cannot process any data
B. it is too expensive for all countries
C. it may identify spurious correlations
D. it always produces incorrect results

Questions 6-9: True/False/Not Given

Do the following statements agree with the information given in the passage? Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

6. Traditional climate models can account for all variables affecting climate behavior.

7. AI systems have discovered connections between Pacific Ocean temperatures and African rainfall.

8. Machine learning can complete in hours what previously took months for human researchers.

9. All countries have equal access to AI climate prediction technology.

Questions 10-13: Sentence Completion

Complete the sentences below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

10. Climate scientists must validate AI predictions against __ to ensure accuracy.

11. Training sophisticated neural networks requires substantial __ and energy.

12. The energy consumption of AI systems contributes to the problem scientists are trying to __.

13. The combination of AI and human expertise is described as a __ that transforms climate understanding.

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PASSAGE 2 – Revolutionizing Climate Models Through Deep Learning

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The application of deep learning techniques to climate science represents a paradigm shift in how researchers approach the formidable challenge of predicting Earth’s future climate. Unlike conventional statistical methods, deep learning—a sophisticated branch of machine learning employing multi-layered neural networks—can discern intricate patterns within high-dimensional datasets that would be virtually impossible for humans to detect manually. This capability has proven particularly advantageous when dealing with the nonlinear dynamics and chaotic behavior inherent in climate systems.

Học viên luyện thi IELTS Reading về trí tuệ nhân tạo và dự đoán khí hậu với tài liệu chất lượng cao

Contemporary climate modeling faces several fundamental obstacles. The spatial resolution of traditional models—the size of the grid cells used to divide Earth’s surface—typically ranges from 50 to 100 kilometers. At this scale, critical processes such as cloud formation, precipitation dynamics, and localized temperature variations cannot be accurately represented. These sub-grid processes must be approximated through mathematical formulas called parameterizations, which introduce significant uncertainties into predictions. Deep learning offers a potential solution by learning these complex relationships directly from observational data rather than relying on simplified assumptions.

One groundbreaking application involves using convolutional neural networks (CNNs), originally developed for image recognition, to analyze satellite imagery of cloud patterns and atmospheric conditions. CNNs excel at identifying spatial features and can be trained to recognize the visual signatures of developing weather systems. Researchers at Stanford University recently demonstrated that a CNN could predict the formation of tropical cyclones up to three days earlier than conventional methods by analyzing subtle patterns in infrared satellite images that human forecasters typically overlook. This enhanced lead time provides coastal communities with crucial additional hours to prepare for potentially catastrophic events.

Recurrent neural networks (RNNs), particularly a variant called Long Short-Term Memory (LSTM) networks, have proven exceptionally effective for time-series forecasting—predicting how variables change over time. Climate data is inherently sequential, with conditions at any given moment influenced by previous states. LSTM networks possess a unique architecture that allows them to retain information about past conditions while processing current data, making them ideal for capturing the temporal dependencies in climate systems. Scientists at the Max Planck Institute for Meteorology have successfully employed LSTM networks to predict sea surface temperature anomalies six months in advance, significantly outperforming traditional statistical models.

The integration of AI into climate science has also enabled the development of hybrid models that combine the strengths of both physics-based and data-driven approaches. These models use traditional equations to represent well-understood processes while employing neural networks to handle poorly characterized phenomena. For instance, the complicated interactions between clouds and atmospheric radiation—a major source of uncertainty in climate projections—can be learned by AI from high-resolution simulations and observational data. This hybrid approach maintains physical consistency while improving accuracy, addressing one of the key criticisms of purely data-driven models.

Ensemble forecasting, which involves running multiple simulations with slightly different initial conditions to assess prediction uncertainty, has been transformed by AI. Traditional ensemble methods require running complete climate models dozens or hundreds of times, consuming enormous computational resources. AI emulators—simplified models that mimic the behavior of complex climate models—can generate ensemble predictions much more efficiently. A team at Oxford University developed an AI emulator that reproduces the output of a sophisticated climate model with 95% accuracy while running 1,000 times faster, enabling researchers to explore a far wider range of potential scenarios.

Transfer learning, a technique where knowledge gained from one task is applied to a different but related task, is opening new frontiers in climate prediction. An AI model trained to forecast weather in one region can be fine-tuned to make predictions for a different area with relatively little additional training data. This capability is particularly valuable for data-scarce regions, such as parts of Africa and South America, where limited historical observations have traditionally hindered accurate forecasting. By leveraging knowledge from data-rich regions, AI can provide improved predictions even where direct observational records are sparse.

However, the “black box” nature of deep learning presents a significant challenge for climate scientists. Unlike traditional models where each equation has a clear physical interpretation, neural networks make predictions through millions of interconnected weights that are difficult for humans to interpret. This opacity raises concerns about trustworthiness—how can scientists and policymakers rely on predictions they cannot fully understand? The emerging field of explainable AI (XAI) seeks to address this issue by developing techniques to interpret neural network decisions, such as identifying which input features most strongly influence predictions. Several research groups are now working on XAI methods specifically tailored to climate applications, aiming to make AI predictions both accurate and interpretable.

Data quality and availability remain critical concerns. While AI algorithms thrive on large datasets, climate science faces unique data challenges. Historical observations are unevenly distributed across space and time, with far more data available from industrialized regions and recent decades. Satellite observations, while increasingly comprehensive, only extend back to the 1970s. This temporal limitation means AI models cannot be trained on the full range of climate variability, particularly rare extreme events that might occur once per century. Scientists must carefully consider whether models trained on recent data can accurately predict future conditions that may have no historical precedent.

The computational carbon footprint of AI training has also attracted scrutiny. A 2019 study estimated that training a single large neural network can emit as much carbon dioxide as five cars over their entire lifetimes. For climate science—a field dedicated to understanding and mitigating climate change—this creates an ethical dilemma. Researchers are responding by developing more efficient algorithms, using renewable energy to power computing facilities, and carefully weighing the environmental cost of AI training against the potential benefits of improved climate predictions that could inform more effective mitigation strategies.

Questions 14-26

Questions 14-19: Yes/No/Not Given

Do the following statements agree with the claims of the writer in the passage? Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

14. Deep learning can detect patterns in high-dimensional datasets that humans cannot identify manually.

15. Traditional climate models have a spatial resolution of less than 10 kilometers.

16. Convolutional neural networks were originally created specifically for climate science.

17. LSTM networks can remember information about past conditions while processing new data.

18. Hybrid models that combine physics-based and data-driven approaches are less accurate than pure AI models.

19. The training of large neural networks produces significant carbon emissions.

Questions 20-23: Matching Information

Match each statement with the correct research institution (A-D). You may use any letter more than once.

A. Stanford University
B. Max Planck Institute for Meteorology
C. Oxford University
D. None of the above

20. Developed a CNN that could predict tropical cyclone formation earlier than conventional methods

21. Created an AI emulator that runs 1,000 times faster than a full climate model

22. Successfully used LSTM networks to forecast sea surface temperature anomalies

23. First institution to apply transfer learning to climate science

Questions 24-26: Summary Completion

Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

The “black box” nature of deep learning is problematic because neural networks make predictions through millions of 24. __ that are hard to interpret. This lack of transparency raises concerns about 25. __ among scientists and policymakers. The field of 26. __ is developing methods to make AI predictions more interpretable.

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PASSAGE 3 – The Epistemological and Methodological Implications of AI-Driven Climate Science

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The ascendancy of artificial intelligence within climate science precipitates profound epistemological questions regarding the nature of scientific understanding and the methodological foundations upon which predictive knowledge is constructed. This technological transformation extends beyond mere computational efficiency; it fundamentally challenges long-established paradigms about how scientific knowledge is generated, validated, and communicated. The integration of AI-driven predictive analytics into climate research represents not simply an incremental improvement in forecasting capabilities but rather a reconceptualization of the relationship between empirical observation, theoretical comprehension, and prognostic accuracy.

Traditional climate science has operated within a reductionist framework, wherein complex phenomena are decomposed into constituent processes governed by first-principles physics. This approach, rooted in the Enlightenment ideal of mechanistic causation, assumes that understanding emerges from articulating explicit causal mechanisms—the precise sequence of physical processes linking cause to effect. Climate models developed under this paradigm are essentially elaborate sets of differential equations representing conservation laws for mass, energy, and momentum. The epistemic virtue of such models lies in their transparency: each equation corresponds to a well-understood physical principle, rendering the model’s internal logic accessible to scientific scrutiny.

AI methodologies, particularly deep learning, operate on fundamentally different principles. Rather than encoding explicit physical laws, these algorithms inductively infer predictive relationships directly from data through iterative optimization of millions or even billions of parameters. The resulting models are phenomenological rather than mechanistic—they describe what happens without necessarily explaining why. This shift from mechanistic to phenomenological prediction has sparked vigorous debate within the scientific community regarding whether such models constitute genuine understanding or merely sophisticated pattern matching.

Proponents of AI-driven approaches argue that the dichotomy between prediction and understanding is less absolute than traditionally assumed. They point out that many supposedly mechanistic climate models contain numerous parameterizations—essentially empirical formulas fitted to data—for processes that are not fully understood. The representation of cloud microphysics, for instance, involves numerous adjustable parameters whose values are tuned to match observations rather than derived from fundamental theory. From this perspective, contemporary climate models already incorporate substantial phenomenological elements, making the distinction between “physics-based” and “data-driven” models more ambiguous than commonly acknowledged. AI simply makes this empirical tuning more systematic and comprehensive, potentially improving fidelity to observed behavior.

Critics counter that this argument conflates two distinct forms of epistemic inadequacy. While traditional parameterizations represent temporary placeholders for incomplete theoretical understanding—explicitly recognized as such and targeted for improvement through process studies—AI models offer no pathway toward deeper mechanistic insight. A neural network trained to predict precipitation might achieve high accuracy without revealing anything about the underlying condensation dynamics, updraft velocities, or microphysical processes that actually produce rain. This opacity, critics argue, relegates AI to the status of predictive tool rather than explanatory framework, limiting its contribution to scientific progress, which requires not merely accurate prediction but comprehension of causal mechanisms.

The interpretability challenge extends beyond philosophical concerns to practical implications for model trustworthiness and error diagnosis. When a physics-based model produces anomalous results, scientists can trace the problem to specific equations or parameter values, facilitating targeted improvements. Neural networks, by contrast, distribute their predictive power across vast networks of interconnected weights, making it exceedingly difficult to identify the source of errors or understand why a model fails in particular circumstances. This limitation is especially concerning for extrapolation beyond training data—predicting future climates that may differ substantially from historical conditions. Physics-based models, grounded in conservation laws that should hold regardless of climate state, arguably provide more reliable extrapolation than data-driven models that may have learned spurious patterns specific to the training period.

Công nghệ trí tuệ nhân tạo phân tích dữ liệu khí hậu và dự đoán xu hướng môi trường toàn cầu

Recent developments in explainable AI (XAI) offer potential mitigation of these concerns. Techniques such as saliency maps, which identify input features that most strongly influence predictions, and layer-wise relevance propagation, which traces how information flows through a neural network, provide partial windows into the “black box.” Applied to climate models, these methods can reveal, for instance, which atmospheric variables or spatial regions most critically determine temperature forecasts. However, current XAI techniques remain limited in their ability to extract mechanistic understanding from neural networks. They can identify correlations the model relies upon but generally cannot elucidate whether these correlations reflect genuine causal relationships or merely statistical artifacts of the training data.

The data requirements of AI approaches introduce additional epistemological complications. Machine learning models are notoriously sensitive to the statistical properties of training data, with performance often degrading sharply when applied to data from different distributions. Climate science faces a unique version of this problem: we seek to predict future climates that, due to anthropogenic forcing, will likely differ from any historical period. The statistical stationarity assumption—that future data will resemble past data—is explicitly violated in climate applications. This raises a fundamental question: can models trained on historical data accurately predict unprecedented future conditions? Physics-based models, by encoding invariant physical laws, provide some safeguard against this problem, whereas purely data-driven models may extrapolate poorly beyond their training domain.

Hybrid approaches that integrate physical constraints with data-driven learning represent one promising direction. These models can be designed to respect conservation laws and other fundamental principles while using AI to learn representations of poorly understood processes. For example, neural networks can be trained to predict the aggregate behavior of cloud systems in ways that conserve energy and moisture, embedding physical knowledge into the learning architecture. Such physically-informed neural networks potentially combine the flexibility and accuracy of AI with the interpretability and extrapolation reliability of physics-based models, though much research remains to realize this vision fully.

The societal implications of AI-driven climate prediction extend to questions of democratic governance and public trust in science. Climate science already faces significant public skepticism in some quarters, often rooted in misunderstanding of climate models’ capabilities and limitations. The introduction of opaque AI systems risks exacerbating this problem. If scientists cannot clearly explain how their models work, communicating uncertainty and building public confidence becomes increasingly challenging. This concern is amplified by the tendency of media coverage to present AI as oracular technology producing definitive predictions, overlooking the substantial uncertainties that attend all forms of climate projection.

Moreover, the concentration of AI expertise and computational resources in wealthy institutions and nations raises equity concerns. If cutting-edge climate prediction increasingly depends on AI technologies accessible only to well-resourced organizations, this could widen existing disparities in adaptive capacity between developed and developing nations. The most vulnerable communities—those least responsible for historical emissions yet most exposed to climate impacts—may lack access to the sophisticated forecasting tools that could inform adaptation strategies. Addressing this potential inequity requires international cooperation to ensure equitable access to AI climate technologies, as well as continued investment in interpretable methods that can be implemented with more modest computational resources.

The evolution of climate science through AI integration ultimately reflects a broader transformation in scientific methodology. The exponential growth of observational data from satellites, sensors, and simulations has created possibilities and challenges that traditional analytical methods struggle to address. AI offers powerful tools for extracting insight from these vast datasets, but applying these tools thoughtfully requires careful attention to their limitations and appropriate use cases. The future of climate science likely involves not the wholesale replacement of traditional methods with AI but rather a judicious integration that leverages the complementary strengths of physical understanding and data-driven discovery, maintaining the interpretability and mechanistic insight essential to scientific progress while harnessing AI’s pattern-recognition capabilities.

Questions 27-40

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

27. According to the passage, traditional climate science operates within a framework that:
A. rejects all empirical observations
B. decomposes complex phenomena into constituent processes
C. relies entirely on artificial intelligence
D. ignores physical laws completely

28. The “epistemic virtue” of traditional climate models refers to their:
A. ability to use artificial intelligence
B. speed of calculation
C. transparency and accessibility to scientific scrutiny
D. resistance to change

29. Proponents of AI-driven approaches argue that:
A. traditional models contain no empirical elements
B. the distinction between physics-based and data-driven models is more ambiguous than commonly thought
C. physics-based models are completely useless
D. AI has no role in climate science

30. Critics of AI models are concerned that these models:
A. are too expensive to develop
B. provide no pathway toward deeper mechanistic insight
C. are always 100% accurate
D. eliminate the need for scientists

31. The concentration of AI expertise in wealthy nations raises concerns about:
A. environmental protection
B. equity and access to forecasting tools
C. the speed of computers
D. historical temperature records

Questions 32-36: Matching Features

Match each characteristic (32-36) with the correct model type (A, B, or C). You may use any letter more than once.

A. Physics-based models
B. AI-driven models
C. Hybrid models

32. Encode explicit physical laws as differential equations

33. Inductively infer predictive relationships directly from data

34. Combine physical constraints with data-driven learning

35. Distribute predictive power across interconnected weights

36. Designed to respect conservation laws while using AI

Questions 37-40: Short-answer Questions

Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.

37. What type of formulas are parameterizations described as being?

38. What do saliency maps identify in explainable AI?

39. What assumption is explicitly violated in climate applications of machine learning?

40. What two complementary strengths should future climate science leverage according to the passage?

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Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. C
4. B
5. C
6. FALSE
7. TRUE
8. TRUE
9. NOT GIVEN
10. physical understanding
11. computing power
12. solve
13. synergy

PASSAGE 2: Questions 14-26

14. YES
15. NO
16. NO
17. YES
18. NOT GIVEN
19. YES
20. A
21. C
22. B
23. NOT GIVEN
24. interconnected weights
25. trustworthiness
26. explainable AI / XAI

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. B
31. B
32. A
33. B
34. C
35. B
36. C
37. empirical formulas
38. input features
39. statistical stationarity assumption
40. physical understanding (and) data-driven discovery

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Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main limitation, traditional climate models
• Vị trí trong bài: Đoạn 2, dòng 5-7
• Giải thích: Bài đọc nói rõ “they require massive computational resources and often take weeks or even months to produce results” – đây là hạn chế chính được nhấn mạnh. Đáp án B paraphrase ý này thành “require extensive computational resources and time”.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: machine learning algorithms, particularly good at
• Vị trí trong bài: Đoạn 3, dòng 1-2
• Giải thích: Câu đầu tiên của đoạn 3 nói “Machine learning, a subset of AI, excels at identifying patterns in large datasets” – từ “excels at” được paraphrase thành “particularly good at”.

Câu 3: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI system, 2019, predict hurricane paths
• Vị trí trong bài: Đoạn 4, dòng 4-6
• Giải thích: Thông tin cụ thể “could predict hurricane paths up to five days in advance” được đưa ra trong đoạn 4, câu trả lời là C.

Câu 4: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Neural networks, inspired by
• Vị trí trong bài: Đoạn 5, dòng 1-2
• Giải thích: Câu đầu đoạn 5 nói rõ “Neural networks, inspired by the structure of the human brain” – đáp án trực tiếp.

Câu 5: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: limitation, AI in climate science
• Vị trí trong bài: Đoạn 7, dòng 3-5
• Giải thích: Đoạn 7 đề cập “AI systems can sometimes make predictions based on spurious correlations” – đây là một hạn chế rõ ràng được nêu.

Câu 6: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Traditional climate models, account for all variables
• Vị trí trong bài: Đoạn 2, dòng 7-8
• Giải thích: Bài đọc nói “they struggle to account for the countless variables” – mâu thuẫn trực tiếp với “account for all variables” trong câu hỏi.

Câu 7: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI systems, connections, Pacific Ocean, African rainfall
• Vị trí trong bài: Đoạn 3, dòng 5-7
• Giải thích: Bài viết nói rõ “AI systems have identified previously unknown connections between sea surface temperatures in the Pacific Ocean and rainfall patterns in Africa” – khớp hoàn toàn với câu hỏi.

Câu 8: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Machine learning, hours, months, human researchers
• Vị trí trong bài: Đoạn 6, dòng 1-2
• Giải thích: “Tasks that previously required months of manual analysis can now be completed in hours” – tương đồng với câu hỏi.

Câu 9: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: All countries, equal access, AI climate prediction
• Vị trí trong bài: Không được đề cập
• Giải thích: Bài đọc không đề cập đến vấn đề tiếp cận công nghệ AI giữa các quốc gia ở Passage 1.

Câu 10: physical understanding

• Dạng câu hỏi: Sentence Completion
• Từ khóa: validate AI predictions
• Vị trí trong bài: Đoạn 7, dòng 6-7
• Giải thích: “Climate scientists must carefully validate AI predictions against physical understanding” – lấy chính xác hai từ từ bài.

Câu 11: computing power

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Training neural networks, substantial
• Vị trí trong bài: Đoạn 8, dòng 2-3
• Giải thích: “training sophisticated neural networks requires substantial computing power and energy” – đáp án là “computing power”.

Câu 12: solve

• Dạng câu hỏi: Sentence Completion
• Từ khóa: energy consumption, contributes to problem
• Vị trí trong bài: Đoạn 8, dòng 3-4
• Giải thích: “this energy consumption contributes to the very problem climate scientists are trying to solve” – từ “solve” là đáp án.

Câu 13: synergy

• Dạng câu hỏi: Sentence Completion
• Từ khóa: combination, AI, human expertise
• Vị trí trong bài: Đoạn 9, dòng 1-2
• Giải thích: “the synergy between human expertise and artificial intelligence” – từ “synergy” mô tả sự kết hợp này.

Passage 2 – Giải Thích

Câu 14: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Deep learning, detect patterns, humans cannot identify
• Vị trí trong bài: Đoạn 1, dòng 2-4
• Giải thích: Câu văn “can discern intricate patterns within high-dimensional datasets that would be virtually impossible for humans to detect manually” khớp với claim trong câu hỏi.

Câu 15: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Traditional climate models, spatial resolution, less than 10 kilometers
• Vị trí trong bài: Đoạn 2, dòng 2-3
• Giải thích: Bài nói “typically ranges from 50 to 100 kilometers” – mâu thuẫn với “less than 10 kilometers”.

Câu 16: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Convolutional neural networks, originally created, specifically for climate science
• Vị trí trong bài: Đoạn 3, dòng 1-2
• Giải thích: Bài nói “originally developed for image recognition” – không phải cho khoa học khí hậu, nên đáp án là NO.

Câu 17: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: LSTM networks, remember information, past conditions
• Vị trí trong bài: Đoạn 4, dòng 4-6
• Giải thích: “LSTM networks possess a unique architecture that allows them to retain information about past conditions while processing current data” – khớp hoàn toàn.

Câu 18: NOT GIVEN

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Hybrid models, less accurate
• Vị trí trong bài: Đoạn 5
• Giải thích: Bài chỉ nói hybrid models “improving accuracy” nhưng không so sánh trực tiếp với pure AI models về độ chính xác.

Câu 19: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: training large neural networks, carbon emissions
• Vị trí trong bài: Đoạn 10, dòng 1-3
• Giải thích: “training a single large neural network can emit as much carbon dioxide as five cars” – xác nhận rõ ràng về vấn đề phát thải carbon.

Câu 20: A (Stanford University)

• Dạng câu hỏi: Matching Information
• Từ khóa: CNN, predict tropical cyclone formation
• Vị trí trong bài: Đoạn 3, dòng 3-5
• Giải thích: “Researchers at Stanford University recently demonstrated that a CNN could predict the formation of tropical cyclones” – Stanford là đáp án.

Câu 21: C (Oxford University)

• Dạng câu hỏi: Matching Information
• Từ khóa: AI emulator, 1,000 times faster
• Vị trí trong bài: Đoạn 6, dòng 5-7
• Giải thích: “A team at Oxford University developed an AI emulator… running 1,000 times faster” – Oxford là đáp án rõ ràng.

Câu 22: B (Max Planck Institute for Meteorology)

• Dạng câu hỏi: Matching Information
• Từ khóa: LSTM networks, sea surface temperature anomalies
• Vị trí trong bài: Đoạn 4, dòng 7-9
• Giải thích: “Scientists at the Max Planck Institute for Meteorology have successfully employed LSTM networks to predict sea surface temperature anomalies” – Max Planck là đáp án.

Câu 23: NOT GIVEN

• Dạng câu hỏi: Matching Information
• Từ khóa: First institution, transfer learning
• Vị trí trong bài: Đoạn 7
• Giải thích: Đoạn 7 nói về transfer learning nhưng không chỉ rõ tổ chức nào là đầu tiên áp dụng kỹ thuật này.

Câu 24: interconnected weights

• Dạng câu hỏi: Summary Completion
• Từ khóa: neural networks, millions
• Vị trí trong bài: Đoạn 8, dòng 2-3
• Giải thích: “neural networks make predictions through millions of interconnected weights” – lấy chính xác cụm từ này.

Câu 25: trustworthiness

• Dạng câu hỏi: Summary Completion
• Từ khóa: concerns about
• Vị trí trong bài: Đoạn 8, dòng 4-5
• Giải thích: “This opacity raises concerns about trustworthiness” – từ “trustworthiness” là đáp án.

Câu 26: explainable AI / XAI

• Dạng câu hỏi: Summary Completion
• Từ khóa: field, developing methods, interpretable
• Vị trí trong bài: Đoạn 8, dòng 6-7
• Giải thích: “The emerging field of explainable AI (XAI) seeks to address this issue” – có thể viết đầy đủ hoặc viết tắt.

Giải đáp án chi tiết bài thi IELTS Reading về trí tuệ nhân tạo trong phân tích dự đoán khí hậu

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: traditional climate science, framework
• Vị trí trong bài: Đoạn 2, dòng 1-2
• Giải thích: “Traditional climate science has operated within a reductionist framework, wherein complex phenomena are decomposed into constituent processes” – đáp án B paraphrase ý này chính xác.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: epistemic virtue, traditional climate models
• Vị trí trong bài: Đoạn 2, dòng 7-8
• Giải thích: “The epistemic virtue of such models lies in their transparency: each equation corresponds to a well-understood physical principle, rendering the model’s internal logic accessible to scientific scrutiny” – đáp án C tóm tắt chính xác.

Câu 29: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Proponents, AI-driven approaches, argue
• Vị trí trong bài: Đoạn 4, dòng 1-7
• Giải thích: Đoạn 4 nói rõ “the distinction between ‘physics-based’ and ‘data-driven’ models more ambiguous than commonly acknowledged” – đây là luận điểm chính của những người ủng hộ AI.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Critics, concerned, AI models
• Vị trí trong bài: Đoạn 5, dòng 3-5
• Giải thích: “AI models offer no pathway toward deeper mechanistic insight” – đây là mối quan ngại chính của những người phê bình, được nêu rõ trong đoạn 5.

Câu 31: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: concentration, AI expertise, wealthy nations, concerns
• Vị trí trong bài: Đoạn 11, dòng 1-3
• Giải thích: “the concentration of AI expertise and computational resources in wealthy institutions and nations raises equity concerns” – vấn đề công bằng và tiếp cận là mối quan ngại được nêu rõ.

Câu 32: A (Physics-based models)

• Dạng câu hỏi: Matching Features
• Từ khóa: encode explicit physical laws, differential equations
• Vị trí trong bài: Đoạn 2, dòng 5-6
• Giải thích: “Climate models developed under this paradigm are essentially elaborate sets of differential equations” – đặc điểm của physics-based models.

Câu 33: B (AI-driven models)

• Dạng câu hỏi: Matching Features
• Từ khóa: inductively infer, predictive relationships, data
• Vị trí trong bài: Đoạn 3, dòng 1-3
• Giải thích: “these algorithms inductively infer predictive relationships directly from data” – đặc điểm của AI-driven models.

Câu 34: C (Hybrid models)

• Dạng câu hỏi: Matching Features
• Từ khóa: combine, physical constraints, data-driven learning
• Vị trí trong bài: Đoạn 9, dòng 1-2
• Giải thích: “Hybrid approaches that integrate physical constraints with data-driven learning” – định nghĩa của hybrid models.

Câu 35: B (AI-driven models)

• Dạng câu hỏi: Matching Features
• Từ khóa: distribute predictive power, interconnected weights
• Vị trí trong bài: Đoạn 6, dòng 3-4
• Giải thích: “Neural networks… distribute their predictive power across vast networks of interconnected weights” – đặc điểm của AI models.

Câu 36: C (Hybrid models)

• Dạng câu hỏi: Matching Features
• Từ khóa: designed to respect, conservation laws, AI
• Vị trí trong bài: Đoạn 9, dòng 3-5
• Giải thích: “neural networks can be trained to predict… in ways that conserve energy and moisture, embedding physical knowledge” – đặc điểm của hybrid models.

Câu 37: empirical formulas

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: parameterizations, described as
• Vị trí trong bài: Đoạn 4, dòng 5-6
• Giải thích: “many supposedly mechanistic climate models contain numerous parameterizations—essentially empirical formulas fitted to data” – đáp án là “empirical formulas”.

Câu 38: input features

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: saliency maps, identify
• Vị trí trong bài: Đoạn 7, dòng 2-3
• Giải thích: “saliency maps, which identify input features that most strongly influence predictions” – đáp án rõ ràng là “input features”.

Câu 39: statistical stationarity assumption

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: assumption, explicitly violated, climate applications
• Vị trí trong bài: Đoạn 8, dòng 5-6
• Giải thích: “The statistical stationarity assumption… is explicitly violated in climate applications” – lấy chính xác cụm từ này (3 từ).

Câu 40: physical understanding (and) data-driven discovery

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: complementary strengths, future climate science, leverage
• Vị trí trong bài: Đoạn 12, dòng 6-8
• Giải thích: “leverages the complementary strengths of physical understanding and data-driven discovery” – hai thế mạnh bổ trợ là “physical understanding” và “data-driven discovery”.

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Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
pressing	adj	/ˈpresɪŋ/	cấp bách, khẩn thiết	pressing challenges	pressing issue, pressing need
complexity	n	/kəmˈpleksəti/	sự phức tạp	enormous complexity	degree of complexity
predictive analytics	n	/prɪˈdɪktɪv ˌænəˈlɪtɪks/	phân tích dự đoán	field of predictive analytics	predictive analytics tools
algorithms	n	/ˈælɡərɪðəmz/	thuật toán	machine learning algorithms	sophisticated algorithms
subtle	adj	/ˈsʌtl/	tinh tế, khó nhận thấy	subtle relationships	subtle difference, subtle change
meteorological	adj	/ˌmiːtiərəˈlɒdʒɪkl/	thuộc khí tượng học	meteorological data	meteorological conditions
neural networks	n	/ˈnjʊərəl ˈnetwɜːks/	mạng thần kinh (nhân tạo)	sophisticated neural networks	artificial neural networks
breakthrough	n	/ˈbreɪkθruː/	đột phá	represent another breakthrough	major breakthrough
accelerated	v	/əkˈseləreɪtɪd/	đẩy nhanh, tăng tốc	has accelerated the pace	accelerated development
spurious correlations	n	/ˈspjʊəriəs ˌkɒrəˈleɪʃnz/	tương quan giả	based on spurious correlations	identify spurious correlations
amplifies	v	/ˈæmplɪfaɪz/	khuếch đại, tăng cường	amplifies their capabilities	amplify the effect
synergy	n	/ˈsɪnədʒi/	sự cộng hưởng, hiệp lực	the synergy between	create synergy, synergy effect

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigm shift	n	/ˈpærədaɪm ʃɪft/	sự thay đổi mô hình tư duy	represents a paradigm shift	undergo a paradigm shift
discern	v	/dɪˈsɜːn/	nhận biết, phân biệt	can discern intricate patterns	discern the difference
nonlinear dynamics	n	/nɒnˈlɪniə daɪˈnæmɪks/	động lực học phi tuyến	dealing with nonlinear dynamics	study nonlinear dynamics
spatial resolution	n	/ˈspeɪʃl ˌrezəˈluːʃn/	độ phân giải không gian	the spatial resolution	improve spatial resolution
parameterizations	n	/pəˌræmɪtəraɪˈzeɪʃnz/	các phép tham số hóa	through parameterizations	develop parameterizations
convolutional	adj	/ˌkɒnvəˈluːʃənl/	tích chập (trong AI)	convolutional neural networks	convolutional layers
temporal dependencies	n	/ˈtempərəl dɪˈpendənsiz/	sự phụ thuộc theo thời gian	capturing temporal dependencies	model temporal dependencies
hybrid models	n	/ˈhaɪbrɪd ˈmɒdlz/	mô hình lai	development of hybrid models	create hybrid models
ensemble forecasting	n	/ɒnˈsɒmbl ˈfɔːkɑːstɪŋ/	dự báo tổ hợp	ensemble forecasting	ensemble forecasting methods
emulators	n	/ˈemjuleɪtəz/	bộ mô phỏng	AI emulators	climate model emulators
transfer learning	n	/ˈtrænsfɜː ˈlɜːnɪŋ/	học chuyển giao	transfer learning	apply transfer learning
black box nature	n	/blæk bɒks ˈneɪtʃə/	tính chất hộp đen	the black box nature	black box approach
opacity	n	/əʊˈpæsəti/	sự mờ đục, không minh bạch	this opacity raises concerns	reduce opacity
explainable AI	n	/ɪkˈspleɪnəbl eɪ aɪ/	AI có thể giải thích	field of explainable AI	develop explainable AI
carbon footprint	n	/ˈkɑːbən ˈfʊtprɪnt/	dấu chân carbon	computational carbon footprint	reduce carbon footprint

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
ascendancy	n	/əˈsendənsi/	sự vượt trội, ưu thế	the ascendancy of AI	gain ascendancy
epistemological	adj	/ɪˌpɪstɪməˈlɒdʒɪkl/	thuộc nhận thực luận	epistemological questions	epistemological framework
reductionist	adj	/rɪˈdʌkʃənɪst/	theo chủ nghĩa giản lược	reductionist framework	reductionist approach
mechanistic causation	n	/ˌmekəˈnɪstɪk kɔːˈzeɪʃn/	quan hệ nhân quả cơ học	mechanistic causation	theory of mechanistic causation
differential equations	n	/ˌdɪfəˈrenʃl ɪˈkweɪʒnz/	phương trình vi phân	sets of differential equations	solve differential equations
phenomenological	adj	/fɪˌnɒmɪnəˈlɒdʒɪkl/	theo hiện tượng luận	phenomenological rather than mechanistic	phenomenological approach
dichotomy	n	/daɪˈkɒtəmi/	sự phân đôi, đối lập	the dichotomy between	false dichotomy
fidelity	n	/fɪˈdeləti/	độ trung thực, chính xác	improving fidelity	high fidelity
conflates	v	/kənˈfleɪts/	nhầm lẫn, gộp chung	this argument conflates	conflate two concepts
opacity	n	/əʊˈpæsəti/	sự mờ đục	this opacity	reduce opacity
extrapolation	n	/ɪkˌstræpəˈleɪʃn/	sự ngoại suy	reliable extrapolation	extrapolation beyond data
saliency maps	n	/ˈseɪliənsi mæps/	bản đồ độ nổi bật	techniques such as saliency maps	generate saliency maps
layer-wise relevance	n	/ˈleɪə waɪz ˈreləvəns/	mức độ liên quan theo lớp	layer-wise relevance propagation	use layer-wise relevance
statistical artifacts	n	/stəˈtɪstɪkl ˈɑːtɪfækts/	hiện tượng thống kê giả	statistical artifacts	identify statistical artifacts
anthropogenic forcing	n	/ˌænθrəpəˈdʒenɪk ˈfɔːsɪŋ/	tác động do con người	due to anthropogenic forcing	anthropogenic forcing factors
stationarity assumption	n	/ˌsteɪʃəˈnærəti əˈsʌmpʃn/	giả định tính dừng	stationarity assumption	violate stationarity assumption
invariant	adj	/ɪnˈveəriənt/	bất biến	invariant physical laws	invariant properties
oracular	adj	/ɒˈrækjələ/	như lời tiên tri	oracular technology	oracular predictions
equity concerns	n	/ˈekwəti kənˈsɜːnz/	mối quan ngại về công bằng	raises equity concerns	address equity concerns
judicious integration	n	/dʒuːˈdɪʃəs ˌɪntɪˈɡreɪʃn/	sự tích hợp khôn ngoan	judicious integration	achieve judicious integration

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Kết bài

Bài thi IELTS Reading mẫu về tác động của AI đến phân tích dự đoán trong khoa học khí hậu này đã cung cấp cho bạn một trải nghiệm học tập toàn diện với ba passages có độ khó tăng dần. Chủ đề này không chỉ phổ biến trong các kỳ thi IELTS gần đây mà còn phản ánh xu hướng toàn cầu về ứng dụng công nghệ tiên tiến vào giải quyết các vấn đề môi trường cấp bách.

Qua 40 câu hỏi đa dạng từ Multiple Choice, True/False/Not Given, Matching đến Summary Completion, bạn đã được rèn luyện các kỹ năng quan trọng: skimming để nắm ý chính, scanning để tìm thông tin cụ thể, paraphrasing để nhận diện câu trả lời, và critical reading để phân tích thông tin phức tạp. Đặc biệt, việc làm quen với từ vựng học thuật chuyên sâu về AI và khoa học khí hậu sẽ giúp bạn tự tin hơn khi gặp các chủ đề tương tự trong kỳ thi thật.

Đáp án chi tiết kèm giải thích vị trí và cách paraphrase sẽ giúp bạn hiểu rõ phương pháp tiếp cận từng dạng câu hỏi. Hãy dành thời gian xem lại những câu trả lời sai để rút kinh nghiệm và cải thiện kỹ năng đọc hiểu. Bảng từ vựng được phân loại theo từng passage cũng là tài nguyên quý giá để bạn mở rộng vốn từ học thuật, đặc biệt trong lĩnh vực khoa học công nghệ.

Hãy nhớ rằng, thành công trong IELTS Reading không chỉ đến từ việc biết nhiều từ vựng mà còn từ khả năng quản lý thời gian hiệu quả, nhận diện pattern của các dạng câu hỏi, và áp dụng chiến lược làm bài phù hợp. Tiếp tục luyện tập đều đặn với các đề thi mẫu chất lượng cao như thế này, và bạn sẽ từng bước tiến gần hơn đến band điểm mục tiêu của mình!