week 7 Flashcards
(15 cards)
Why is 1.5°C important, and why avoid 2°C?
1.5°C limit reduces climate risks significantly compared to 2°C or more.
Risks increase rapidly with temperature: e.g., more heatwaves, heavy rainfall, extreme weather.
Current policies project a ~3°C rise, with RCP 8.5 leading to 4.1–4.8°C by 2100 — very high risk.
RCP 2.6 aims for a 2°C limit; achieving 1.5°C requires very aggressive emission cuts.
Exceeding 1.5°C increases natural disasters, flooding, drought, and wildfires.
Why is coupling different model types beneficial in GCMs?
Early models ran atmosphere and ocean separately, missing key interactions.
Coupled models capture feedbacks (e.g., El Niño effects on climate).
Modern GCMs link atmosphere, ocean, biosphere, chemistry, and cryosphere using a Coupler.
Benefits of coupling:
Simulates Earth’s systems more realistically.
Improves climate projections and understanding of climate change impacts.
Supports better policy decisions and mitigation planning.
Example models: biosphere (carbon cycle), chemistry (ozone, air quality), cryosphere (ice, permafrost).
How are GCMs used to attribute warming to human activity?
Run model with and without anthropogenic forcing.
Compare model output to observed data:
Without forcing: only natural variability.
With forcing: matches observed warming trends.
This shows how much warming is human-caused (e.g., GHGs, land use).
Key for separating natural vs. human-driven climate change.
How do component lifetimes affect climate and model accuracy?
Lifetimes determine how long a component impacts climate.
E.g., CO₂ lasts centuries, long-term warming.
Aerosols in troposphere last days, short-term cooling.
Stratospheric aerosols (e.g., volcanic) last longer, significant temporary cooling.
Accurate modelling of lifetimes affects:
Climate projections
Radiative forcing
Uncertainty levels, especially for aerosols.
Short-lived species with complex chemistry are harder to model, affecting reliability.
How to test if a GCM is accurate for predicting future change?
Hindcasting: Run model for past periods, compare output to real data.
If past trends are reproduced, model is validated.
Nowcasting: Compare model predictions of current conditions to observations.
Model intercomparison: Check how older models performed over time.
Good alignment with real data boosts confidence in future projections.
Continuous observations and refinements improve accuracy over time.
Describe the errors associated with running GCMs
Despite significant progress, Global Climate Models (GCMs) still contain uncertainties and limitations:
Grid Size and Resolution: Coarse grid sizes limit the ability to model small-scale phenomena and regional climates. Higher resolution reduces these errors but is constrained by computational resources.
Atmospheric Layer Representation: Limited vertical observational data reduces accuracy in simulating processes at different atmospheric levels.
Regional Performance: GCMs often perform poorly at predicting regional climate variability and extreme events compared to global trends.
Clouds and Precipitation: Cloud processes remain one of the largest uncertainties. Rainfall parameterisation is still less accurate than temperature modelling.
Aerosol Effects: The cooling effects of aerosols, their sources, and interactions are complex and not fully understood.
Natural Variability and Incomplete Processes: Internal variability and missing components (e.g., the nitrogen cycle) contribute to model errors.
Validation Challenges: Historical temperature records, especially those from thousands of years ago, have uncertainties which impact validation.
Overall, while GCMs simulate climate at global and continental scales well, they are less reliable at finer temporal and spatial scales.
How might we improve GCMs into the future?
Several developments can enhance GCM performance and reliability:
Higher Spatial and Temporal Resolution: Smaller grid sizes improve local-scale accuracy, though they demand significantly greater computational resources.
Enhanced Observational Data: Improved satellite coverage, ocean buoys, and vertical atmospheric profiles aid in model validation and development.
Better Parameterisations: Refining representations of clouds, aerosols, and rainfall processes is critical for reducing uncertainties.
Inclusion of Missing Processes: Integrating overlooked processes (e.g., nitrogen cycling, land-use dynamics) improves model comprehensiveness.
Advanced Computing: Faster processors and parallel computing allow for more detailed and numerous simulations.
Machine Learning and AI: These tools can help optimise model tuning and detect complex patterns not easily captured through traditional methods.
Continual improvement of these areas will produce more accurate climate projections for informing policy and adaptation strategies.
Why do scientists want to study climate change?
The study of climate change through modelling and observation serves multiple purposes:
Understanding the Earth System: To grasp the interactions between land, ocean, atmosphere, and ice.
Reconstructing Past Climate: Hindcasting helps scientists interpret natural climate variability and past climate transitions.
Projecting Future Changes: Forecasts under different emission scenarios inform risk assessments and adaptation planning.
Attribution Studies: Identifying the causes of observed changes, especially distinguishing anthropogenic from natural influences.
Impact Assessments: Understanding consequences for ecosystems, human health, agriculture, and infrastructure.
Supporting Policy: Models inform international climate agreements and national strategies for mitigation and adaptation.
Evaluating Drivers: Examining how greenhouse gases, aerosols, land use, and deforestation impact climate.
What are the major uncertainties in climate models?
Key uncertainties in climate modelling include:
Clouds: Their complex feedbacks and fine-scale processes are difficult to model accurately.
Aerosols: Their sources, lifetimes, and climate impacts (e.g., cooling) remain poorly constrained.
Regional Predictions: Limited resolution hampers accurate forecasting of regional extremes and precipitation.
Atmospheric and Ocean Layering: Sparse vertical data reduces model accuracy for upper-atmosphere or deep-ocean dynamics.
Incomplete Process Representation: Missing feedbacks and cycles (e.g., nitrogen, vegetation dynamics) hinder realism.
How can major uncertainties in climate models be improved?
Develop higher-resolution models to capture fine-scale interactions.
Expand observational networks, especially in data-sparse regions like the oceans and polar areas.
Improve physical parameterisations for clouds, aerosols, and rainfall.
Leverage advanced computing and AI to refine predictions and analyse feedbacks.
What is the difference between natural variability and human-induced climate change? How do we model their effects in climate studies?
Natural Variability arises from internal and external processes such as:
Orbital variations (Milankovitch cycles)
Solar output changes
Volcanic eruptions
Ocean-atmosphere interactions (e.g., ENSO)
Human-induced Climate Change stems from:
Greenhouse gas emissions from fossil fuel combustion
Land-use changes (e.g., deforestation)
Industrial processes and agriculture (e.g., methane and nitrous oxide emissions)
Modelling the Effects:
Models are run under scenarios with only natural forcings and compared to runs including anthropogenic forcings.
Observed trends, especially post-1950s warming, align with models that include human activities, but not with those including only natural variability.
This approach confirms that recent warming is primarily human-driven.
What are the key components of a Global Climate Model, and why is coupling different models important?
Core GCM Components:
Atmosphere – Simulates temperature, wind, and humidity dynamics.
Ocean – Models heat and carbon transport via currents and mixing.
Land – Includes soil moisture, vegetation, and energy fluxes.
Sea Ice – Captures albedo effects and energy exchanges.
Cryosphere, Chemistry, and Biosphere – Integrated in modern Earth System Models for added realism.
Importance of Coupling:
Feedbacks: Coupling allows key feedbacks like ice-albedo and carbon cycle feedbacks to emerge.
Interactions: Ocean-atmosphere interactions, such as ENSO, require coupling for accurate simulation.
Realism: Each component affects the others; uncoupled models fail to capture the full complexity of the climate system.
How can volcanic eruptions serve as a “natural experiment” for studying climate response to aerosol forcing?
Volcanic eruptions offer insights into aerosol-climate interactions:
Aerosol Injection: Eruptions like Mt. Pinatubo (1991) inject sulphate aerosols into the stratosphere.
Radiative Forcing: These aerosols reflect solar radiation, causing short-term cooling.
Stratospheric Persistence: Stratospheric aerosols remain longer than tropospheric ones, extending their climatic impact.
Model Validation: Comparing observed cooling with model simulations post-eruption helps validate aerosol parameterisations.
Can natural variability explain observed trends in global warming? Why or why not?
No. Natural variability alone cannot account for the observed global warming trends, especially since the mid-20th century:
Natural Drivers Insufficient: Factors like solar output and volcanoes cannot explain the magnitude or rate of recent warming.
Paleoclimate Records: Show that current warming is unprecedented over the last 2000 years.
GCM Comparisons: Simulations that exclude anthropogenic forcings fail to replicate observed warming trends; only those including human influences match observed data.
Therefore, the overwhelming scientific consensus attributes modern warming primarily to human activities.
What do we need to improve future GCMs?
Improving future GCMs requires progress in the following areas:
Resolution: Finer spatial and vertical resolution improves the simulation of regional processes and extremes.
Observations: Expanded and improved data coverage (especially oceans and upper atmosphere) aids model calibration and validation.
Process Representation: Better parameterisation of clouds, aerosols, and hydrological processes reduces uncertainty.
Component Integration: More comprehensive coupling of Earth system components, such as biogeochemical and ecological processes.
Computational Resources: Increased computing power enables more complex, high-resolution simulations.
Innovative Techniques: AI and machine learning offer tools to enhance model tuning and analyse complex interactions.
