Lecture Notes: Science, Data, and Climate
Science, Data, and Society: Context and Themes
Science should inform political decision-making, addressing current gaps in geosciences and medicine.
Scientific research funding in the US has decreased significantly (e.g., 0.5 to 0.75 of grants frozen).
Modern medicine has greatly improved lifespan (US average: 47 to 77 years), with vaccine benefits outweighing risks.
Society relies on basic services like electricity and water, requiring constant management.
Citizens, as voters, can influence policy through scientific awareness.
A global perspective is critical; the US is not the world's sole center.
Sustainability (clean air, water, well-being) is a core value.
AI expansion will increase electricity demand and impact policy/infrastructure.
Critical thinking and lifelong learning are essential with evolving technology.
College education's return on investment, employability, and internships will be discussed.
Classroom engagement is encouraged; multitasking reduces learning effectiveness.
Attendance is monitored via an eye-clicker system.
Introduction to Earth system components: lithosphere (focus), hydrosphere, biosphere.
Talk covers science methodology, data interpretation, and policy/society connections.
What is science? Core ideas discussed in the session
Science is data-driven, starting with data collection and observations that lead to hypotheses.
Data interpretation requires broader context and consideration of assumptions for extrapolation.
The scientific method is iterative: collect data\rightarrowinterpret\rightarrowtest\rightarrowrefine hypotheses\rightarrowdraw conclusions\rightarrowpublish\rightarrowpeer review.
Peer review is vital for credibility, ensuring quality against existing knowledge.
The rise of pseudoscience necessitates critical scrutiny.
Expertise is crucial for interpreting and applying complex data.
Data-driven decision-making is essential in policy and industry, moving beyond personal biases.
Scientific thinking aids everyday problem diagnosis.
The Scientific Method: steps and a concrete example
Core steps:
Ask a question.
Form a hypothesis.
Collect reliable data.
Draw insights/conclusions.
Translate findings into a narrative for stakeholders.
(Optional) Publish and undergo peer review.
Example: Rising atmospheric CO₂
Data: Global measurements show increasing CO₂.
Hypotheses: Anthropogenic emissions (fossil fuels, deforestation) vs. natural variability.
Carbon Cycle: Understanding carbon inputs/outputs (photosynthesis, respiration, combustion). Humans primarily affect atmospheric CO₂.
Mass Balance: Atmospheric carbon change is described by the balance of carbon inputs and outputs over time.
Narrative: Connects data to mechanisms (fossil fuel burning increases carbon inputs; sinks like oceans/biosphere increase carbon outputs) and climate responses.
CO₂ as a greenhouse gas: Absorbs infrared (IR) radiation at specific wavelengths, trapping heat. O₂ does not.
Importance of Data: Climate conclusions are based on consistent, multi-decadal data, showing consensus on rising CO₂ and human link.
Prediction: Scientists use data to predict trends with uncertainty, reduced by more data.
Carbon Cycle Framework: Carbon moves between lithosphere, atmosphere, biosphere, hydrosphere; focus on inputs/outputs for atmospheric CO₂ levels.
Earth system and spheres: a quick map
Lithosphere (geosphere): Solid Earth, rocks, soils (course focus).
Hydrosphere: All water (groundwater, surface water); vital for human life and energy.
Biosphere: All living things (humans, plants); involved in carbon exchange.
Interactions among these spheres govern Earth's energy and matter cycles.
The carbon cycle and climate change: deeper dive
CO₂ is a greenhouse gas that traps heat, raising surface temperature.
Mechanism: It absorbs and re-radiates thermal IR from Earth's surface. Not all gases (e.g., O₂) have this property.
Human activities (fossil fuels, deforestation) alter the carbon cycle, increasing atmospheric CO₂ and reducing natural sinks.
The mass balance framework allows us to reason about how emissions andsinks determine atmospheric CO₂ concentrations.
The carbon cycle includes fast (photosynthesis) and slow (rock formation) processes.
Educational takeaway: Credible climate science relies on converging evidence from measurements, models, and carbon cycle understanding.
Data, evidence, and credibility in science
The scientific method stresses data-driven conclusions and bias avoidance.
Peer review acts as a crucial quality-control filter.
Combatting pseudoscience and low-quality publications requires critical evaluation of sources.
Scientists convey findings through data storytelling, maintaining transparency about methods and uncertainties.
Science informs policy via evidence-based risk/benefit assessments.
Science in policy, politics, and society
Science is crucial for shaping policy; political systems must heed scientific evidence.
Regulatory changes impact environmental and public health (water/air quality).
Sustainability is a key policy driver, aiming for clean environment and healthy communities.
Global interconnectedness means national policies influence international outcomes.
AI, the future of work, and marketability of students
Expanding AI centers offer job opportunities for graduates.
AI's impact on jobs is uncertain, but critical thinking and transferable skills remain valuable.
Education must equip students with foundational critical thinking, data literacy, and complex information interpretation.
Switzerland's CHEPT-like platform exemplifies sustainability integrated with tech/policy.
Education, critical thinking, and lifelong learning
Education develops critical thinking, data literacy, and evidence judgment, not just memorization.
Lifelong learning is necessary due to changing technologies and societal needs.
The instructor acts as a career adviser, connecting students to recruiters and internships.
Classroom dynamics: engagement, distractions, and learning efficacy
Phones and laptops are distractions; multitasking diminishes learning.
Active engagement enhances learning outcomes; disengagement is discouraged.
Eye-clicker system monitors attendance and participation.
Data interpretation exercises and visualization: learning by looking
Visual data (e.g., photos) spark hypotheses before formal data. Students analyze observations to form and test hypotheses.
Example: Interpreting a photo with orange/reddish precipitation to deduce location/activities.
This exercise highlights: observation as a start, iterative hypothesizing/testing, and context's role in interpretation.
Observations, hypotheses, and the practical takeaway
Photo analysis uses color/landscape cues for initial hypotheses.
Emphasizes gathering more data before conclusions.
Broader implication: Investing in people and systems (education, health, infrastructure) globally elevates living standards.
Connections to previous and real-world relevance
Foundational principles: Scientific method, carbon cycle, data quality, critical thinking.
Real-world relevance: Climate policy, energy/infrastructure planning, public health strategies.
Quick reference: key equations and numerical notes
Carbon balance (atmosphere): Understanding atmospheric carbon relies on the balance of carbon inputs and outputs.
Per-person GDP example for the United States (illustrative): The notes mentioned illustrative examples of per-person and total US GDP figures.
Greenhouse gas concept (qualitative): CO₂ absorbs infrared radiation at select wavelengths; this causes heat retention and surface warming. O₂ does not interact with those infrared wavelengths in the same way.
Data-driven decision-making benefits: Reduces bias, improves accuracy, fosters critical thinking, supports efficient allocation.
Summary of takeaways
Science is an iterative, data-driven process, validated by peer review.
The carbon cycle and CO₂ are central to climate; human activities disturb this balance, increasing atmospheric CO₂ and causing warming.
Credible science demands careful data, transparent methods, and evidence evaluation; avoid pseudoscience.
AI, technology, and globalization shape the job market; critical thinking and data literacy are essential.
Investment in education, health, infrastructure, and sustainable policy has significant human impact.
Active classroom engagement