Climate Change Impact
Part 1. Background
It is widely accepted that the climate is changing, and will change more in the future, as a result of human activity. I have carried out many studies where I have quantified the impact of changes to climate. These have been in Europe, Asia, the Pacific and Africa. This posting is an introduction. Other postings will examine specific studies.
There is widespread acceptance the climate is changing and that humans are driving, at last part of, the change. As a consequence, it is normal for infrastructure projects to examine the potential impact of climate change and then adjust the design to take account of it. This involves developing a quantified timeline of the changes.
So, in this and a series of following posts I am going to describe how to quantify the impact of climate change based on my experience in many parts of the world: Europe, Asia, the Pacific and Africa.
The purpose of these posts is two-fold:
- Firstly, to pass on my experience to others who are required to quantify climate change.
- Secondly, and unashamedly, to advertise my skills and experience.
This post is introductory. Following posts will be more detailed and specific.
Impact of Climate Change
NASA lists the projected impacts of climate change
- Change will continue through this century and beyond
- Temperatures will continue to rise
- Frost-free season (and growing season) will lengthen
- Changes in precipitation patterns
- More droughts and heat waves
- Hurricanes will become stronger and more intense
- Sea level will rise 1-4 feet [0.3 to 1.2 m] by 2100
I have examined all these types of impact – and a few more.
A climate model represents the earth as a series of cells (or boxes).
- These cells are of the order of 100 km by 150 km horizontally and have around ten levels of atmosphere and a similar number of levels of the ocean.
- The models simulate the interaction between each of the model cells about once every hour.
- The execution time of climate models is of the order of 1 minute of computer simulation for one day of simulation. Typically, a model will simulate the climate for a period of more than 200 years and the execution time will be a few months.
The above figures are a generalisation for global climate models and individual models will have different values for the above parameters. In particular, regional models, which on represent part of the earth’s area, will have a finer grid.
Representative Concentration Pathways (RCPs)
The whole purpose of climate models is to calculate the changes in climate due to human activity and if these are found to have negative consequences, to evaluate mitigation options. The changes in human activity can lead to an energy imbalance – with more energy being absorbed by the earth than is radiated back into space. The best-known factor is the production of Carbon Dioxide, which allows more energy in to the earth’s atmosphere than out of it, but others include the effect of soot particles in the atmosphere and changes to the reflection of radiation.
Exactly what humans will do to the atmosphere in the coming century is unknowable so four possible trends have been considered. These are known as Representative Concentration Pathways (RCPs). They are labelled by the associated energy imbalance in watts per square metre at the end of this century: RCP 2.6, RCP 4.5, RCP6.0 and RCP 8.5. The first of these would occur if humans severely curtailed their emission of greenhouse gases. The last of the four assumes a future with little or any limitation of emissions.
The use of RCP values was introduced in 2013. Before that the equivalent was SRES (Special Report on Emissions Scenarios) values. Some of the studies I worked on used SRES values.
As stated above, global climate models work at a grid size of the order of 100 km side. (The phrase ‘of the order of’ is used as there is variety of scales between different models.) However, it is sometimes necessary to consider areas that are smaller than this, for example a specific length of proposed road. Going from a model cell to the specific area is known as ‘downscaling’. In theory, there are two methods: dynamic and statistical. However, the ‘dynamic method’ effectively requires a climate model with a reduced grid size developed for a specific study which in all but a few cases is impracticable.
The alternative, known as the ‘statistical’ method or the ‘delta method’, assumes that the changes in climate projected for a model cell apply uniformly over the whole cell. For example, assume that a model cell projects a temperature increase of 2°C but that the observed temperature within the area of the cell is from 9°C to 15°C. The projection will be that that in all locations the increase will be 2°C.
This method places reliance on observed climate data. Source of such data will be discussed later.
Source of climate projections
For climate projections, I use almost exclusively the Climate Explorer site (https://climexp.knmi.nl
) run by the Netherland’s Meteorological Service. The only exception has been in a few cases when ‘pre-digested’ projections were provided by the client.
Use of the web site is free and if you sign up it facilitates use by ‘remembering’ your previous selections.
In terms of projections I use mostly two sets of projections:
- Monthly CMIP5 scenario runs
- Annual CMIP5 extremes
The acronym ‘CMIP5’ refers to the ‘Coupled Model Inter-comparison Project Phase 5’.
The ‘scenario runs’ part of the site has output from climate models under four groups: Surface variables, Radiation variables, Ocean, Ice & Upper Air variables, and Emissions. In most cases for impact analysis it is the variables in the first group that are important. These include temperature and precipitation.
The ‘extremes’ part of the site has a second set of projections. These were developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Values are provided for 31 variables. These include maximum daily precipitation, number of frost days (when the minimum was zero or below), number of ice days (when the maximum was zero or below) and growing season length.
Selection of climate projections
The climate explorer site has projections for more than 20 climate models. In addition, some models are run for multiple ‘experiments’ in which slightly different but credible model parameters are used. So, which one to use?
In some cases, there might be guidance on the choice of climate models, for example from previous studies. Often however a decision has to be made on which models to use. What I have often done is to compare the simulated climate model output with observed values. This is rarely simple. For example how do you choose between a model which is biased (with values consistently higher or lower than observed) but which represent the inter-annual variation with a different model which is less biased but does not represent annual variations?
Sources of observed climate data
The best source, if available, is from the meteorological and hydrological services in the country you are working in. For various reasons that is not always possible. Sometimes, for example, the meteorological service requires payment which the project has no funds for. Other sources of data include:
Climate change impact
Quantifying how the climate will change is but the first step to estimating the impact of climate stage. For example, for the impact on water resources it necessary to run a hydrological model with, firstly, observed climate data and, secondly, projected climate data.
Climate change impact studies
The following is a list of the climate change impact studies to be covered in other posts.
- Southern Bangladesh. The impact of climate change on rural communities including temperature and rainfall changes and the effect of sea level rise.
- Tonle Sap is a shallow lake/wetland in Cambodia. The hydrology is complicated as at times the lake receives water from the Mekong river and at times discharges to the river. A model of lake levels was developed which calculated changes in level due to climate change.
- The Mekong River Basin. A hydrological model was developed for the whole of the Mekong basin from the Himalayas in China down to the final flow measuring station in Cambodia. A hydrological model was used to estimate changes in flow due to climate change.
- Great African Lakes. The three ‘Great’ lakes (Lakes Victoria, Malawi and Tanganyika) are important for their fisheries. Data on lake temperature was decoded and the impact of climate change on water temperature was estimated.
- Hydrology of the Tagus river basin. The Tagus (Tejo/Teju) is one the most developed major river basins in Europe. A water resources/hydrological model of the basin was developed and the impact of climate change evaluated.
- Road flooding in Vanuatu. The impact of climate change on road flooding and rural economy was studied.
- Road flooding in Samoa. Data from different sources were combined to estimate flooding at different elevations. The impact of climate change was also studied.
- Road flooding in Kyrgyzstan. In this case flooding was but one of the potential problems the other one being icing during winter months. Again, the impact of climate change was studied.
- Variation of climate change in Zambia.
- The Yesilirmak Basin in Northern Turkey is highly developed for hydropower and irrigation. It was projected that average flows would decrease and, equally importantly, the seasonal distribution would change. At present, as a result of snow melt, the peak flow is in early summer at the start of the irrigation season; in future the peak flow will be in December.
- The Kagera Basin flows through 4 countries (Rwanda, Burundi, Uganda and Tanzania) before entering Lake Victoria. An extensive data base of flow, rainfall and climate was available this was sufficient for a hydrological model, HYSIM, to be calibrated. It was concluded that the increase in evaporation and in precipitation would to some extent cancel each other out.