What Are The 5 Causes Of Climate Change

What Are The 5 Causes Of Climate Change – EBook Order #: CCP5769 Print Book Order #: CC5769 Both eBook and Print Book Order #: CC+P5769 ISBN13: 978-1-55319-411-8 Grades: 5, 6, 7, 8 Reading Level: 3-4 Complete pages : 60 Author: Erika Gasper

• Earth’s atmosphere • Water vapor • Carbon dioxide • Methane • Ozone • Nitrous oxide • Synthetic gases

What Are The 5 Causes Of Climate Change

Provide students with insight into the science of our atmosphere and the effects of humanity’s actions on the Earth system.

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Our resource provides a scientific perspective on climate change that will help students separate fact from fiction. Explore the different layers of the atmosphere. Conduct an experiment to see exactly how the color of an object affects how much radiation it absorbs. Find out what effect rising temperatures are having on Earth’s oceans. Create your own model of the carbon cycle. Explain how the residence time of methane in the atmosphere can help people fight climate change. Learn what effects ozone has on human health. See firsthand how nitrogen-fixing bacteria can replace nitrogen fertilizers. Find out why synthetic gases are banned, and how long their effects will remain in the atmosphere. Written to Bloom’s Taxonomy and STEAM initiatives, additional hands-on activities, crossword, word search, comprehension quiz and answer key are also included.

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Your eBook or digital lesson will be emailed to you within 15 seconds. Note: Please check your spam folder if you cannot find the download email. This document uses the interactive IPython notebook format (now also called Jupiter). The notes can be accessed in several different ways:

Climate system: “The climate system is the highly complex system that consists of five main components: the atmosphere, the hydrosphere, the cryosphere, the lithosphere and the biosphere, and the interactions between them. The climate system evolves over time under the influence of Its own internal dynamics and because of external forcing such as volcanic eruptions, solar variations and anthropogenic forcing such as the changing composition of the fir and land use changes.

The Greenhouse Effect

Figure 1.1 | Major drivers of climate change. The radiative balance between incoming solar shortwave radiation (SWR) and outgoing longwave radiation (OLR) is influenced by global climate ‘drivers’. Natural fluctuations in solar production (solar cycles) can cause changes in the energy balance (through fluctuations in the amount of incoming SVR) (Section 2.3). Human activity changes the emissions of gases and aerosols, which are involved in ferric chemical reactions, resulting in modified O3 and aerosol amounts (Section 2.2). O3 and aerosol particles absorb, scatter and reflect SWR, changing the energy balance. Some aerosols act as cloud condensation nuclei modifying the properties of cloud droplets and possibly affecting precipitation (section 7.4). Because cloud interactions with SWR and LWR are large, small changes in the properties of clouds have important implications for the radiative budget (section 7.4). Anthropogenic changes in GHGs (e.g., CO2, CH4, N2O, O3, CFCs) and large aerosols (>2.5 μm in size) modify the amount of outgoing LRV by absorbing outgoing LRV and re-emitting less energy at a lower temperature ( Section 2.2) ). Surface albedo is altered by changes in vegetation or land surface properties, snow or ice cover, and ocean color (Section 2.3). These changes are driven by natural seasonal and diurnal changes (eg snow cover), as well as human influence (eg changes in vegetation types) (Forster et al., 2007).

Figure 1.2 | Climate feedbacks and time scales. The climate feedbacks associated with increasing CO2 and rising temperature include negative feedbacks (-) such as LWR, flow rate (see glossary in Annex III), and air-sea carbon exchange and positive feedbacks (+) such as water vapor and snow/ice . Albedo comments. Some feedbacks can be positive or negative (±): clouds, ocean circulation changes, air-land CO2 exchange, and emissions of non-GHGs and aerosols from natural systems. In the smaller box, the large difference in timescales for the various feedbacks is highlighted.

Note that the IPCC figure only goes to centuries – deep ocean circulation – but there are many even longer time scales in the climate system. e.g. Growth and decay of ice sheets, geological processes such as chemical weathering, continental drift

The choice of which processes to include in a model should therefore be guided by the timescales of interest. For example, the IPCC process is primarily concerned with the turn of the century—because it is of particular concern to human affairs. So we don’t tend to include ice sheet and geological feedback – although coupled ice sheet modeling is becoming more important.

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Almost every climate model needs to account for this transfer and the effect on the energy budgets in any climate model (unless we are only dealing with global averages).

Solve the time-dependent equations of motion on a grid of sufficient spatial resolution to represent the growth and decay of synoptic-scale weather systems.

Essentially a simulation involves representing (at least some aspects of) the underlying rules that govern the process. It is a chain of causality linking input to output.

Parameterization involves making assumptions about the statistical properties of the process – so we can calculate some relevant statistical properties of the output given the input, without needing to explicitly model the actual events.

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Figure 1.13 | The development of climate models over the past 35 years showing how these different components are coupled into comprehensive climate models over time. In each aspect (for example, the fer, which comprises a wide range of ferric processes) the complexity and range of processes has increased over time (illustrated by growing cylinders). Note that at the same time, the horizontal and vertical resolution has increased significantly, for example, for spectral models from T21L9 (approximately 500 km horizontal resolution and 9 vertical levels) in the 1970s to T95L95 (approximately 100 km horizontal resolution and 95 vertical levels ) now, and that now ensembles with at least three independent experiments can be considered normal.

Figure 1.14 | Horizontal resolutions considered in today’s higher resolution models and in the very high resolution models currently being tested: (a) illustration of the European topography at a resolution of 87.5 × 87.5 km; (b) Same as (a) but for a resolution of 30.0 × 30.0 km.

One goal of all this complexity is to do more simulation and less parameterization in order to get a more accurate forecast of climate change.

In terms of our simple view of the planetary energy budget, we are trying to represent the net climate feedback $lambda$ correctly, and thus get the correct climate sensitivity.

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However, it doesn’t always work that way. In many cases we know that a feedback operates in nature, but we cannot represent it in terms of first principles.

Exchanges of energy, water and carbon between the earth and the fir are biologically mediated. We must (to a certain extent) rely on empirical relationships. A bit like economic modeling.

I use CESM in my own research. We will use CESM in this course. Everyone should visit this website and learn about it.

The software is somewhat modular, so different submodels can be combined together depending on the nature of the scientific problem and the available computer power.

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E.g. We can run the model in “only” mode. Surface conditions over land and ocean will be prescribed from observations. It’s hard to learn much about climate change this way.

Let’s say we want to use our model to estimate equilibrium climate sensitivity. Recall $Delta T_$ is the warming we get from doubling ferric CO2, once the planetary energy budget is adjusted back to equilibrium.

The key is that we allow the sea surface temperature to change, but we fix (prescribe) the net effect of ocean currents on the transfer of energy.

But experience with coupled models (meaning interactive ocean circulation) has shown that the circulation does not change radically below $2times CO_2$.

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So the Plate Ocean Model gives us a decent first guess at climate sensitivity. And it makes it possible to do a lot of experimentation that we wouldn’t be able to do otherwise.

We perform a control run to obtain a baseline simulation, and take averages of several years (because the model has internal variability – each year is slightly different)

And allow the model to adjust to a new equilibrium, just as we did with the toy energy balance model.

Once it was close to its new equilibrium, we ran it for several more years to get the new climatology.

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The following animation shows contours of sea level pressure in the control simulation. It is based on 6-hourly output of the numerical model.

The figures above are reproduced from Chapter 1 of the IPCC AR5 Working Group 1 report. The report and images can be found online at http://www.climatechange2013.org/report/full-report/

Cubasch, U., D. Wuebbles, D. Chen, M.C. Facchini, D. Frame, N. Mahowald and J.-G. Winther, 2013: Introduction. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Keen, G.-K. Plattner, M. Tignor, Sk. Allen,

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