MEng Civil Engineering

Year of entry: 2024

Course unit details:
Climate Data Application

Course unit fact file
Unit code CIVL40411
Credit rating 15
Unit level Level 4
Teaching period(s) Semester 1
Available as a free choice unit? No


Engineers are expected to develop infrastructure systems with true longevity; however, the conditions of the future are by their nature undetermined. The impact of anthropogenic climate change is exacerbating the issues of understanding future conditions, both the direct impacts and the societal changes associated with responses to climate change. Engineers will need to understand the impacts of climate change and what they mean for the people using the systems that are being built, maintained, operated and eventually decommissioned. This unit will give students hands on experience with research quality climate data and models. The methods used also introduce data science tools to the students. 


The unit aims to: 

Give students practical experience using state of the art climate change projections data 

Teach students to critically analyse the suitability of models of climate impacts 

Teach students the difference between sources of uncertainty and how to treat them appropriately 

Introduce some basic data science techniques to the students 

Learning outcomes

ILO 1. Understand the tools used to determine climate impacts and the issues engineers face when dealing with climate impacts.

ILO 2. Application of climate data for use in simple impacts models. Strengths and weaknesses of climate data as a tool. 

ILO 3. Data science methods for analysis of large datasets (typical climate model outputs are ~Tb). 

ILO 4. Presentation skills for conveying the importance of their work to non-specialists.



The course is built of two related sections, lectures and practical analysis. The latter of which will also contribute to the coursework. 


Lecture topics:  

Responses to climate change - How people react to climate change and what steps they take. Discussion of which bodies should lead the response. No correct answer but the students will need to defend their decision. – Mitigation and adaptation 

What are climate MIPs and why do they matter. Model intercomparison projects are common in environmental sciences, this lecture will discuss their strengths and weaknesses along with how to use them properly. 

Uncertainty analysis and proper treatment of uncertainty in climate projections. With multiple scenarios, timelines and models along with high resolution projections on restricted areas, there is a lot of data, students will be tasked with understanding uncertainties and their sources, along with the implications of these uncertainties 

Climate injustice - Discussion of the issue, specifically that poorer countries are suffering more from climate change and that their responses are therefore more expensive 

Invasive species and their influence on infrastructure - Climate change will cause migrations, typically in the UK we don't worry about mosquitoes but that will need to change with warming 

Changes to the pdf of weather - Why extremes are more common, what this means for structures or resilience. Describe the difference between a 10 year storm in 2000 and 2050 


Practical topics (Two per year, option to cycle projects):

One case study will be used to teach relevant skills and will last from W2:W5. The other case study will be from W6:W11 and will be assessed.  

Change in wind farm available energy from observed data or climate simulations - requires data analysis and post processing to estimate power from a given farm 

Change in solar farm energy from observed data or climate simulations - requires data analysis and post processing to estimate power from a given farm 

Heat waves in observed data or climate simulations - multiple definitions of heat wave, choosing correct definition for a given use is important. Related topics are heat stress and heating or cooling demand 

Response of a hydrological system to climate change 

Determine deviation of a monsoon system under climate change - also investigate the issues with GCM data compared to high resolution observations 

Teaching and learning methods

Large group lectures 

Practical skills in computer laboratories 

Assessment methods

Method Weight
Report 90%
Oral assessment/presentation 10%

Feedback methods

Coursework Assignments - Feedback provided via written comments through Turnitin 2 weeks after submission. Collective feedback provided within lecture and uploaded to Blackboard.

Recommended reading - Abstract and Section 3  

For Information and advice on Link2Lists reading list software, see:  

Study hours

Scheduled activity hours
Demonstration 3
Lectures 13
Practical classes & workshops 20
Independent study hours
Independent study 114

Teaching staff

Staff member Role
Benjamin Parkes Unit coordinator

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