Appendix A: Further detail & projections
As with most of the UK, the climate in the WMCA
is projected to experience seasonal changes in weather, and this can be broken down by each climate parameter (e.g. precipitation, temperature).
This summary note has examined what a changing climate might look like for the area during the 21st century, with a particular focus on shifting weather patterns of precipitation levels and air temperature, and reference to levels of humidity, cloud cover, and wind speed.
In 2018, the Met Office published headline figures for the UK, noting the expected changes in rainfall and winter precipitation. This summary provides an idea of the potential changes, and a foundation for more detailed analysis of impacts.
For this summary note, the climate projections have been developed for the wider West Midlands region, not just the Combined Authority geography (due to available dataset spatial scale). Analysis of impacts is confined to the WMCA boundary. The climate projections are based on a series of datasets provided by the Met Office and there are a number of detailed data processes that have been used to develop the projections.
The summary is derived from the following information:
- Datasets have been manipulated for the region, for those climate parameters where there is the most robust climate science available.
- 25km projection datasets have been used across the region.
- Where available a 1981-2000 baseline has been used, for example by 2065 the average air temperature will see a 4.5°C increase compared to the baseline (this is known as an anomaly value).
- Each climate parameter uses data extracted for each season, and for each RCP scenario if possible. This is done on an annual basis to plot a time series to highlight change over time.
Introduction to Climate Projections
The UK Climate Projections (UKCP) are a series of datasets that act as a tool to demonstrate how the UK climate may change in the future, based on scientific modelling.
These datasets provide a future view of changing weather patterns, so that we can better understand how climate change may impact the UK.
In 2018 the projections were updated, and this study uses this information (referred to as UKCP18) as the basis for the assessment. UKCP18 shows climate change projections up until the year 2100.
The UKCP18 data is split up into four different scenarios, ranging from ‘best’ to ‘worst’.
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In the best scenario immediate and impactful action is taken to reduce impacts to the climate.
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The worst scenario is where changes to the climate are severe, as the least action is taken to limit this change.
Scientifically these scenarios are called Representative Concentration Pathways (RCP), and the four scenarios are set at RCP 2.6 (the best case), 4.5, 6.0, and 8.5 (the worst case). Each scenario refers to different assumptions, based on economic, social and physical changes to the environment that will influence climate change and the expected global temperature change.
- RCP 2.6 (best case scenario) assumes immediate action is taken and results in a global temperature change of 1.6°C by 2081-2100.
- RCP4.5, less action is taken compared to RCP2.6, which results in a global temperature change of 2.4°C by 2081-2100.
- RCP6.0, less action is taken compared to RCP4.5, which results in a global temperature change of 2.8°C by 2081-2100.
- RCP8.5 (worst case scenario), least action is taken and results in a global temperature change of 4.3°C by 2081-2100.
In this summary the best and worst scenarios have been used to give a range of future climate changes within the West Midlands Combined Authority (WMCA) area.
To increase the accuracy of the projections, the UKCP18 information uses percentiles which presents a range of estimates and the probability of that estimate occurring.
Figure 6 shows how percentiles are used to predict the probability of different maximum temperatures across the WMCA by the year 2099. For the purposes of
the projections in this summary note, the ‘most likely’ outcome, the 50th percentile (the centre line), has been used.
The Baseline Period
The WMCA is predicted to experience changes in air temperature during this century.
Using historic data from the UKCP dataset, actual temperature values are available from the late 19th century (Figure 7). The historical data shows that the average temperature across all seasons in 1900 was 12.8°C.
By 2020, that figure was at 14.9°C, demonstrating a clear increase in temperature. For the baseline period in 1981, the average temperature for the whole year was 13.1°C, and by 2000 it was 13.9°C. This baseline period is used to compare against the future projections where the baseline figure is referenced, this refers to the average across the WMCA, and does not reflect absolute values. For example, the average maximum temperature across the baseline period was 20.2°C, but the highest recorded temperature across the West Midlands in this period was 37.1°C.
This is because the average is taken across all days within a month and across the average of different stations within the WMCA.
With the Midlands region at some distance from the sea, with its moderating effects on temperature, the annual range is more pronounced than in most parts of the UK.
Maximum Temperatures
The lines fluctuate on a yearly basis due to the variance in the projections. Whilst the overall trend is an increase, some years will experience higher temperatures, but that is not necessarily cumulative. As an example, 2022 was an extreme year for high temperatures, and while 2023 is likely to have higher temperatures than the baseline period on average, it may still not meet the peak heights of 2022.
On average for the baseline period, the maximum air temperature was 13.3°C, with a summer maximum air temperature of 20.2°C.
In the best-case scenario projection (RCP 2.6), summer is expected to have a maximum temperature 2.9°C warmer than the baseline period.
In the worst-case scenario (RCP 8.5) summer is expected to have a maximum temperature 7.5°C warmer than the baseline period.
In the best-case scenario projection (RCP 2.6), winter is expected to have a maximum temperature 2.9°C warmer than the baseline period.
In the worst-case scenario (RCP 8.5) winter is expected to have a maximum temperature 3.8°C warmer than the baseline period.
Minimum Temperatures
On average for the baseline period, the minimum air temperature was 5.5°C.
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Across all scenarios, the average minimum air temperature is expected to increase by 2099.
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In the best-case scenario, it is projected to increase by 1.4°C by 2099 on average across all seasons.
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In the worst-case scenario, summer is projected to see the greatest change. Minimum air temperature is set to increase by 5.8°C in 2099 from the baseline average: 10.6°C to 14.5°C.
Average Temperatures
The average air temperature across all scenarios and seasons will increase.
In the best-case scenario, the average temperature by 2099 will have increased by 1.4°C. In the worst-case scenario, this increases to 4.6°C.
In the summer season, the worst-case scenario would see the average temperature increase by 6.5°C. The average mean air temperature in summer for the baseline period was 15.4°C, therefore we could see average air temperatures of about 22°C by 20991.
Precipitation
One of the main changes to predicted precipitation patterns will be drier summers.
The UKCP forecast sets out that there will be on average 31% less rainfall compared to the baseline period in the summer seasons. This means that whilst previously the WMCA received on average 171mm of rainfall in summer, this could decrease to 118mm. Autumn is likely to see a 10% increase in rainfall, and winter is expected to see rainfall increase by 15%. The average rainfall in winter during the baseline period was 195mm, so this could increase up to 224mm. A typical ‘wet day’ would be one with rainfall totals of 1mm or more. So 224mm across winter months (90 days in a common year) is a significant increase.
Changes to precipitation patterns during this century will not only be seen in the form of drier summers and wetter winters, but also in the form of extreme weather events such as flash floods that, alongside other parameters such as increased wind speed, can lead to more intense and frequent storms.
There is a change in rainfall levels across each season for the West Midlands in the best-case scenario, against the historic baseline.
In the best-case scenario, summers will be 25% drier than the baseline period. Winters are likely to be 7% wetter, as is autumn, whilst spring remains mostly consistent with the baseline period.
There is a change in rainfall levels for the West Midlands in the worst-case scenario, broken down by season. Summers are anticipated to have 42% less rainfall by 2099, a decrease of 72mm down to just 99mm of rainfall in summer. During winter, rainfall could increase by 24% by 2099, amounting to 242mm. Given increased temperatures, this is not likely to lead to an increased amount of snowfall.
Humidity
Across all scenarios, the projections show a more humid environment by 2099. Compared to the baseline, humidity levels in autumn are expected to increase by 24%, the highest % change across all the seasons on average, and, looking at the worst-case scenario, this could increase to 36%.
The higher the humidity the greater the water vapour and the more rain we are likely to experience. This will affect health and comfort levels, but increased humidity levels can cause several issues in an urban environment, as noted in the main summary.
Cloud Cover
Cloud cover is also likely to change, although the projections are similar to that of rainfall, in that they show significant seasonal variations. The most significant projection shows that 15.4% less cloud is expected across summer on average. For the other seasons, there is no significant deviation to the norm. A decrease in cloud cover means an increase in sun exposure, which contributes to lower precipitation levels and in some instances higher temperatures.
Wind Speed
Finally, wind speed patterns may see some changes, however the projections are quite sporadic and therefore not relied on in this summary report. Understanding there are potential changes to wind speed patterns is important in identifying potential impacts that may be experienced.
Technical Aspects of the Impact Assessment Process
The climate impact assessment process is shown in Figure 13 and helps to highlight some of the most measurable and expected impacts from Climate Change.
- Literature Review
- Collation of Impacts
- Data Collection
- Data Manipulation
- Risk Assessment
- Climate Vulnerability Hotspots
To help inform the assessment, literature was reviewed to identify impacts, and then collated per climate parameter and focus area.
Data to help measure these impacts was collated, gathering datasets that could be mapped using GIS software.
Using the list of impacts, those that could be measured using the GIS datasets were assessed, producing climate vulnerability hotspots where clusters of impacts were greatest.
Datasets
The datasets were mapped using a Geographical Information System (GIS) to help highlight areas of impact. Different datasets were layered over each other to identify potential impacts. For example, showing hospitals within Flood Zone 3 when combined with the projected increases in precipitation can help identify vulnerable areas. The quantifiable impacts identified
from the literature review were grouped to help undertake the assessment. It important to note that these impacts and mapped groups are not exhaustive and are limited by data available.
This approach was taken to provide a way to rank where risks are across the area. Each grid has been assigned a reference value, which has been used both as a unique identifier and to help with the visual assessment. If there are different risk levels between two grid hexagons it doesn’t mean there is a drastic drop off in vulnerability, instead it shows that it is relatively less vulnerable than the adjacent hexagon.
Datasets used to develop the ‘People’ assessment
Dataset |
Associated Metric Group(s) For Impact Assessment |
|
Projected population aged 0-4 (2043), Projected population 65 and over (2043) |
2 - Heatwaves |
|
VectorMap district, Built-up areas, Open greenspace layer, National Forest Inventory, Priority habitat inventory |
3 - Access to green space |
|
Mean Domestic Electricity kWh Usage Per Household (2020), Mean Domestic kWh Per Meter Gas Consumption (2020) |
4 - Increased utility demand |
|
Health Assets (included in Public Buildings) |
5 - Accessibility to healthcare |
|
Aged 65 & over who are single, widowed, divorced or separated (%) (2011), Rate of referrals to children’s social care (2020/21) |
6 - Displacement from flooding |
|
Risk of flooding from surface water (1 in 30 year/ 1 in 100 year) |
5 - Accessibility to healthcare |
|
Rivers and Sea Flood Zones 2/ 3 |
5 - Accessibility to healthcare |
|
IMD Decile |
1 - Water scarcity |
Table A.1: Datasets used to inform the people assessment (Figure 5)
Datasets used to develop the ‘Infrastructure’ assessment
Dataset |
Associated Metric Group(s) For Impact Assessment |
|
New Housing developments 2018-19 |
8 - Flood risk to housing |
|
Public Buildings (excluding Health Assets), Schools/ universities and colleges, Health Assets |
9a, 9b, 9c - Flood risk to public buildings |
|
WPD Substations |
10 - Flood risk to energy infrastructure assets |
|
National Grid Transmission Assets |
10 - Flood risk to energy infrastructure assets |
|
Risk of flooding from surface water (1 in 30 year/ 1 in 100 year), Rivers and Sea Flood Zones 2/ 3 |
8 - Flood risk to housing
11b - Flood risk to transport infrastructure assets (Rail Network) 11c - Flood risk to transport infrastructure assets (Service Points) |
|
HS2 – Phase 1, Railway Network, OS MasterMap Highways, TfWM Key Route Network, Bus Lanes, Cycle Routes, |
11a/b - Flood risk to transport infrastructure assets (Rail/ Road Network) |
|
Petrol Stations, Electric Charging Points, Airports, Transport Stations |
11c - Flood risk to transport infrastructure assets (Service Points) |
Table A.2: Datasets used to inform the infrastructure assessment (Figure 7)
Datasets used to develop the ‘Natural Environment’ assessment
Dataset |
Associated Metric Group(s) For Impact Assessment |
|
Local Nature Reserves, National Nature Reserves, Sites of Specific Scientific Interest (SSSIs), Special Areas of Conservation |
12 - Biodiversity vulnerability |
|
Climate Change Vulnerability |
12 - Biodiversity vulnerability
|
|
VectorMap district, Built-up areas, Open greenspace layer, National Forest Inventory, Priority habitat inventory |
13 - Loss of urban green space habitats |
|
IMD Decile |
14 - Effect of reductions in agricultural productivity |
|
Agricultural Land Classification |
15 - Risk to soil health |
|
Habitat Networks Combined |
16 - Flood risk to natural assets |
|
Risk of flooding from surface water (1 in 30 year/ 1 in 100 year) |
15 - Risk to soil health
|
|
Rivers and Sea Flood Zones 2/ 3 |
15 - Risk to soil health
|
Table A.3: Datasets used to inform the Natural Environment assessment (Figure 9)