GEO 130 Earth’s Physical Environments

Introduction to Physical Geography Define physical geography Explain how we study physical geography List several types of models Describe and define models List the factors that complicate the study of pattern and process and provide examples Give examples of how scale influence the perception of pattern and process Explain how contingent historic events complicate the interpretation of pattern and process Explain the difference between non-linear and linear change Explain why models are limited in describing patterns in the environment Define and give examples of positive and negative feedback Define/explain: steady state, dynamic equilibrium, phase shifts, and transient states

What is Physical Geography? Integrated study of three subdisciplines Climatology Biogeography Geomorphology Holistic and reductionist understanding

What is Physical Geography? Includes the role of humans and their influence on natural processes Anthropocene Mudflow is the result of natural and human processes which we will talk about later.

Why study physical geography? The natural world is as complex as the human world Consider these facets of the Earth’s surface Its extent (size) and resolution (detail) History and time since origin (4.5 billion years)

Why study physical geography? To understand human-environment interaction, you need to understand how the non-human world works. Its important to know the politics of oil, but also how the natural world might respond to climate change.

How do we do physical geography? We document pattern and process

Turf bank terraces

Erosion at base of terrace landform creates protected mineral soil needed by germinating conifers.

We use models to study pattern and process Models are a simplified, often idealized representation of reality. Intent of a model is to recreate patterns by capturing the underlying explanatory processes Your role is to learn models, recognize they are idealized, and try to identify why they are incomplete.

What are the types of models? Graphical conceptual models

Types of models Maps

Types of models Data visualizations and simulations Mathematical in essence, but enhanced through computational power of the computer.

Types of models Dynamical models Use field-derived conditions to model the behavior of a phenomena Can be used to predict or forecast the future

http://moe.met.fsu.edu/tcgengifs/

Types of models Dynamical models can also be used to hindcast the past and study events that have already happened

Types of models Statistical models Use the record of the past to predict the future

What complicates the study of pattern-process?

1. Multiple driving variables Driving variables are most directly responsible for the observed patterns. They have to be identified, but are not always readily apparent or easily separated from each other

Proximate and ultimate causation A proximate cause is an event which is closest, or immediately responsible, for causing some observed result. This exists in contrast to a higher-level ultimate cause which is usually thought of as the "real" reason something occurred. Registered for this class versus my mom and dad meeting Requirements for graduation versus evolution of humans in Africa

Why did the ship hit the rock? Proximate cause: Because the ship failed to change course to avoid it. Ultimate cause: Because the ship was under autopilot and the autopilot received bad data from the GPS. Separating proximate from ultimate causations frequently leads to better understandings of multiple driving variables So, not useful to always say we have the same history – we evolved out of the primordial soup. Nor is it useful to think of only the most recent cause. We have to disregard some patterns and choose others to look at.

2. Contingent events What contingencies complicate the prediction of wildfires?

3. Feedbacks Feedbacks make prediction of the outcome of interactions difficult Positive feedback: externally generated change is reinforced Negative feedback: externally generated change is minimized

Example of positive and negative feedbacks associated with global warming

Thermokarst lakes have positive and negative feedbacks associated with global warming.

4. Spatial and temporal scale Scale determines how we understand pattern and process Example: controls on temperature: what makes it warm or cold?

Answer depends upon temporal and spatial scale Cloud cover and humidity (minutes to hours) Diurnal (day-night) cycles Seasonal cycles (1 year) Cyclical fluctuations due to sunspots (10-50 years) Anthropogenic contribution of greenhouse gases (10-100 years) Milankovitch orbital cycles (10,000 yrs)

5. Thresholds and time lags Melting of ice sheets and glaciers A photo taken in 1906 shows the calving terminus of Carroll Glacier at the head of Queen Inlet. No vegetation is visible. Photo courtesy of U.S. Geological Survey A 2004 photograph shows that the terminus of Carroll Glacier has changed to a stagnant, debris-covered glacier. U.S. Geological Survey photo by Bruce Molnia

https://www.youtube.com/watch?v=kYKKgn0GaY0

6. Teleconnections

Global monthly temperatures illustrating the effect of the June 1991 volcanic eruption of Mount Pinatubo in the Philippines. The temperature effect apparently lasted to the end of 1992, as indicated by the shaded area.

7. The earth is constantly changing Earth is an open system Change is the norm Change itself, however, has different types Steady state equilibrium Dynamic equilibrium Phase shift once tipping point (threshold) is reached Transitional states

Steady state equilibrium Inputs = outputs, balance achieved. Same value maintained.

Dynamic equilibrium Constantly changing

Phase shifts Systems may shift to another state once a threshold is reached Can be natural or caused by humans Like a treadmill, when suddenly a cliff appears…..

No analog states Novel assemblages of species, climates, and human influences give rise to states that have not been seen before and their relative degree of stability is uncertain.

Transitory states Systems do not settle down to any dynamical equilibrium. Like a treadmill on random, with no pattern