D422Lot1.SMHI.5.1.1B: Detailed workflows of each case ... · Results: We have evaluated the...

C3S_422_Lot1_SMHI – D5.1.1B |

Copernicus Climate Change Service

D422Lot1.SMHI.5.1.1B:

Detailed workflows of each case-study

on how to use the CDS for CII

production and climate adaptation

Full Technical Report:

Heat Waves causing Health Stress in

Australia

Aurel Moise and John Nairn

Australian Bureau of Meteorology

REF.: C3S_422_Lot1_SMHI D5.1.1B

C3S_422_Lot1_SMHI – D5.1.1B |


Summary This technical report documents the description and findings of the case study entitled “Heat Waves causing Health Stress in Australia” implemented by the

Australian Bureau of Meteorology (BOM), Melbourne, Australia. This report is divided into five main sections: 1) description of the case study, 2) potential

adaptation measures, 3) contact, 4) data production and results, and 5) conclusions.

Taking advantage of the high-quality model data provided by the C3S_422_Lot1_SMHI contract of Copernicus Climate Change Service (C3S), this

study employed the Australian Bureau of Meteorology operational heatwave metric Excessive Heat Factor - EHF to these model simulations in order to evaluate future changes to heatwaves across Australia.

The results showed that future heatwave severity is strongly increasing with

increasing CO2 emission strength (e.g. from RCP45 to RCP85 of selected CMIP5 models), but not homogenous across the continent. The strongest increases in severity are along the east coast and southern Australia. Furthermore,

heatwaves will occur more often in the off-seasons (e.g. spring and autumn) under either emission scenario, but most pronounced in the tropical regions of

Australia. The Copernicus Climate Change Service provides access to climate model data, first applications to analyse data with the associated toolbox. The

interactive climate atlas produced within the C3S_422_Lot1_SMHI contract allows our local stakeholder to have easy access to this vital information.

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Contents 1- Case study description 4

1.1 Issue to be addressed 4

1.2 Decision support to client 4

1.3 Temporal and spatial scale 4

1.4 Knowledge brokering 4

2- Potential adaptation measures 4

2.1 Lessons learnt 4

2.2 Importance and relevance of adaptation 5

2.3 Pros and cons or cost-benefit analysis of climate adaptation 5

2.4 Policy aspects 5

3- Contact 5

3.1 Purveyors 5

3.2 Clients/users 5

4- Data production and results 6

4.1 Step 1: Access data 6

4.2 Step 2: Evaluate global simulations 7

4.3 Step 3: Sub-select model simulations 9

4.4 Step 4: Calculate EHF for historical period 9

4.5 Step 5: Calculate CII metric for local data 12

4.6 Step 6: Calculate CII metric for future scenarios 12

4.7 Step 7: Show results using communication tool 15

5- Conclusion of full technical report 18

References 19

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1- Case study description

1.1 Issue to be addressed

Heatwaves and their projected changes under enhanced greenhouse warming

are of particular interest in Australia, where they affect many sectors (health, agriculture, transport, energy, tourism, etc.). Recent results warrant concern over the potential for increased incidence of heatwaves in Australia, noting that

further research would be needed to extend this work nationally and refine it to enhance our understanding of the likely extent of future heat events.

1.2 Decision support to client

State Health Services and State Emergency Services (and others) are seeking to implement these results into their strategies for mitigating increased number and duration of heatwaves in particular.

1.3 Temporal and spatial scale

The temporal scale of foremost interest is in the 5-20 year range with long-term

planning also needing to understand uncertainties on the 50+ year time scale.

The special scale is significantly large because the state of New South Wales (NSW) covers a large area. The main urban area is Sydney and surrounding

areas, although there are several other cities in NSW.

1.4 Knowledge brokering

We have presented the project objectives to the client. Our meetings with the clients are predominantly over telecom and we had 3 meetings. We took their

feedback on the usefulness of the results into account and their suggestions.

2- Potential adaptation measures

2.1 Lessons learnt

There are a couple of important lessons we learnt along the way. Firstly, the

face-to-face sessions with our stakeholders were vital in the process of understanding their needs. Secondly, managing expectations about what the

results can deliver was also crucial: especially with respect to spatial and

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temporal resolution and the confidence in the projected changes. With regard to

the selected CMIP5 models we have found that they are simulating the heatwave metric reasonably well and therefore can be used to evaluate the future changes in the EHF-derived metrics

2.2 Importance and relevance of adaptation

Clearly, recent strong heatwaves across Australia highlighted the importance of the strong impact these extreme events can have to the Australian community

and human health especially. Current longer-term strategic planning of management practices around heatwaves is done without taking into account climate change estimates. After the end of this project, we are keen to hear

feedback from our client(s) on the effect on their strategic planning using this service.

2.3 Pros and cons or cost-benefit analysis of climate adaptation

Not adapting to climate change would result in significant damages to

infrastructure, environment and human lives. Our case study addresses adaptation in the health sector with potential for loss of life from more frequent

and intense heatwaves under enhanced greenhouse warming conditions. Unfortunately, a thorough cost benefit analysis of climate adaptation is not available yet.

2.4 Policy aspects

After the completion of this case study, we are envisaging organizing a stakeholder/user interaction workshop, in which user groups, stakeholders and

policy makers will be invited. Policy aspects will be discussed and any policy related issues can be identified after interactions and discussions among these groups. We are particular interested in evaluating the link with the Copernicus

Climate Data Store (CDS) and the added value of the Copernicus Climate Change Service to the assessment of climate adaptation measures with respect to

Australian heatwaves.

3- Contact

3.1 Purveyors

Aurel Moise, Climate Research Section, Bureau of Meteorology (BOM) , Melbourne, Australia

3.2 Clients/users

http://www.bom.gov.au/research/

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Kerry Chant, Population and Public Health, NSW Ministry of Health, North Sydney,

Australia

4- Data production and results This analysis makes use of the large data holding of CMIP5 (Taylor et al. 2012)

data and ERA5 reanalysis data (C3S 2017) at the Copernicus CDS. Additionally, we employed the high quality observational data set for maximum and minimum temperature for Australia (AWAP; Jones et al. 2009). Below is a workflow of the

entire analysis and each step is being described in more detail.

4.1 Step 1: Access data

Description: Collection of global observations and simulations of daily minimum and maximum temperature available in the CDS catalogue.

Results: Using our scripts, we extracted the relevant data sets from Copernicus CDS and other international archives as appropriate (such as the Earth System

Grid – ESGF). These were daily data field from 19 CMIP5 models for minimum and maximum surface air temperature for the HISTORICAL and RCP45 and

RCP85 scenarios. The CMIP5 used are shown in the table below.

Table 1: List of 19 CMIP5 models

ID GCM Model Institute, Country

BCC bcc-csm1-1 Beijing Climate Centre, China

BCC bcc-csm1-1m Beijing Climate Centre, China

BNU BNU-ESM Beijing Normal University, China

CMCC CMCC-CMS Euro-Mediterranean Center on Climate Change, Italy

CMCC CMCC-CM Euro-Mediterranean Center on Climate Change, Italy

CNRM CNRM-CM5 National Centre for Meteorological Research, France CSIRO-BOM

ACCESS1-0 Centre for Australian Weather and Climate Research (CAWCR), Australia

GFDL GFDL-CM3 Geophysical Fluid Dynamics Laboratory, NOAA, USA

GFDL GFDL-ESM2G Geophysical Fluid Dynamics Laboratory, NOAA, USA

GFDL GFDL-ESM2M Geophysical Fluid Dynamics Laboratory, NOAA, USA

MOHC HadGEM2-CC Met Office Hadley Centre, Hadley Global Environment Model, UK

http://www.health.nsw.gov.au/

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MOHC HadGEM2-ES Met Office Hadley Centre, Hadley Global Environment Model, UK

INM INMCM4 Russian Institute for Numerical Mathematics Climate Model, Russia

IPSL IPSL-CM5A-LR Institute Pierre Simon Laplace, France

IPSL IPSL-CM5A-MR Institute Pierre Simon Laplace, France

IPSL IPSL-CM5B-LR Institute Pierre Simon Laplace, France

MPI MPI-ESM-LR Max Planck Institute, Germany (Low resolution)

MPI MPI-ESM-MR Max Planck Institute, Germany (Medium resolution)

NCC NorESM1-M Norwegian Climate Centre Earth System Model, Norway

While downloading entire centuries, our main interest was focused on the last 30 years of the HISTORICAL simulations (e.g. 1976 – 2005) and two time periods

for the future RCP45 and RCP85 simulations (e.g. 2030 – 2070 and 2070 – 2099). These two time periods describe the mid-century and late-century cases.

We also made use of the Copernicus CDS toolbox functionality in order to check on climatologies of the extracted fields. Figure 1 below is a typical example of

plotting the mean climatology for daily tmax for Australia for the period 2000 – 2017 using data from the Copernicus CDS. In this case it is the ERA5 reanalysis

data representing the observed climatology.

Figure 1: Climatological mean surface air temperature from ERA5 for the period 2000 – 2017.

4.2 Step 2: Evaluate global simulations

Description: Evaluation of global simulations of mean surface air temperature for Australia over historical period using the observations.

Results: We have evaluated the selected CMIP5 model's annual and seasonal surface air temperature fields over Australia against (a) the reference high

quality Australian gridded data set (AWAP) and (b) multiple observations and reanalysis data sets in order to assess the 'observational uncertainty' from these

data sets. The comparison is shown in Figure 2 below.

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The various observational and reanalysis data sets shown in the middle column

in Figure 2 include surface air temperature from HadCRU (HadCRUT3; Brohan et al. 2006), GISS (GISTEMP; Hansen et al. 2010), CFSR (Saha et al. 2010), Merra (Rienecker et al. 2011), ERA-Interim (Dee et al. 2011) and JRA55 (Kobayashi et

al. 2015).

On average, the CMIP5 models capture the climatological temperature distribution across the continent very well. The north-south gradient in temperature is correctly simulated as well as the coastal versus inland

differences during summer (middle row in Figure 2). Pattern correlations are generally very high for the mean model distribution of temperature. There is a

substantial spread in the Australia-averaged temperature amongst the CMIP5 models, as indicated by the spread in the box-whiskers in Figure 2. While 50 per

cent of the models are within ±1 °C of the AWAP reference data, some of the models are several degrees warmer or colder. The box-whiskers belonging to the middle column in Figure 2 additionally indicate that there is some discrepancy

amongst the other observational data sets and reanalysis data sets with respect to temperature across Australia. However, this discrepancy is generally less than

half of the spread seen in the CMIP5 models.

Figure 2: Climatological mean surface air temperature from AWAP (a, d, g, the reference data set), the average of a selection of other observational data sets (b, e, h, including CRU) and the CMIP5 mean model (c, f, i) for annual (top row), summer (DEC-FEB, middle row) and winter (JUN-AUG, bottom row) surface air temperature. The averaging period is 1976–2005 and the units are degrees Celsius (°C). The contours highlight the 9, 15, 21, 27 and 33 °C thresholds for better comparison. The number in the top right corner indicates the spatial correlation between the corresponding data and AWAP. The spread in the data sets is indicated by the box-whisker to the right of each subplot: each shows the Australia-averaged surface air temperature where the grey box refers to the middle 50 % of the data and the whiskers show the spread from minimum to maximum. The thick black line is the median of the underlying data and the red line is AWAP.

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4.3 Step 3: Sub-select model simulations

Description: Sub-select a group of model simulations that failed the evaluation

and will not be used further

Results: ECMWF/Copernicus CDS applied their separate Quality Assurance/Quality Control procedure on CMIP5 data uploaded to the CDS and thereby restricted the analysis to a list of 19 CMIP5 models as listed in Table 1.

We have identified through a model evaluation (evaluating climatological biases and important processes and teleconnections for the Australian region) that out

of the 19 CMIP5 models BNU-ESM is not suitable for the temperature analysis in this project because of its larger biases over the Australia. This model is not part of the EHF analysis further below.

4.4 Step 4: Calculate EHF for historical period

Description: Apply the methodology used for calculating the Excessive Heat Factor (EHF) to both observations and simulations over the historical period

Results: What is the Excessive Heat Factor – EHF?

The EHF (Nairn and Fawcett, 2015) combines two important temporal characteristics of excess heat intensity: the significant deviation from the long term mean (EHIsig; the detection term) and the short term anomaly (EHIaccl; the

amplifying term):

EHF = EHIsig × max(1, EHIaccl) This measure of excess heat intensity will be location-dependent because of the

dependence on local climatologies. In order to derive a 'normalised' value that describes the severity across all locations, an analysis using the Generalised

Pareto distribution showed that the 85th percentile of the EHF at each location as a suitable measure to set the SEVERITY thresholds:

0 – 1 Low Heatwave Severity = p85(EHF) = 1 – 3 Severe Heatwave

>3 Extreme Heatwave

Figure 3 shows the Bureau of Meteorology's operational use of the EHF and severity metric for the summer 2014 heatwave (January 2 -4).

An extreme heatwave will see the peak amplitude above three times the severity

threshold. One of the main questions in this analysis is if and how these heatwave characteristics change under future warming conditions.

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Figure 3: Spatial extent of the heatwave severity for the January 2014 heatwave across northeastern Australia as published by the Bureau of Meteorology.

The processing of the observation and CMIP5 model data involved several steps

in order to get to the final heatwave severity metric:

- Calculation of the daily mean temperature from tmin and tmax, called DMT - Run Python script "pctile.py" to calculate the 95th percentile of the DMT

data with reference to the 30 year climatology. This builds the threshold

for the significant deviation from the long term mean - Run Python script "loc-ehi-sig-accl-ehf.py" to calculate the EHIsig, EHIaccl,

EHF and EHFp85 variables

Figure 4 is an example of the comparison of the average annual number of heatwave days (LOW, SEVERE and EXTREME separately) during the period 1976

– 2005 between observations (using the Australian AWAP high-quality station data set) and the mean of the selected CMIP5 models. It shows the strong severe heatwave in Western Australia as well as several days near the top end of

Australia from observations. The models on average simulate the spatial patterns of the heatwave durations well (e.g. over Western Australia and the East coast)

with slightly longer durations for all categories.

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Figure 4: Average annual heatwave duration, e.g. number of LOW (top), SEVERE (middle) and EXTREME (bottom) heatwave days during the 1976 – 2005 period from observations (AWAP, left) and the average of the 19 CMIP5 models (right).

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4.5 Step 5: Calculate CII metric for local data

Description: Sub-select the Australian region and check/compare heat wave metrics against the Bureau of Meteorology high-quality observational data set (AWAP).

4.6 Step 6: Calculate CII metric for future scenarios

Description: Using the remaining model simulations, calculate future EHF characteristics under high and low emission scenario.

Results:

We combined the analysis for Step-5 and Step-6 in order to more effectively analyse the large data sets. Our initial analysis focussed on the areas around some of the capital cities within Australia. Figure 5 shows their location (red frames) against the

backdrop of observed mean surface temperature anomalies for the entire year 2017 compare to the reference period (1971-2000).

Figure 5: Location of Australian cities where our initial analysis focusses on (red frames): Darwin, Perth, Adelaide, Melbourne, Hobart, Sydney and Brisbane. The background colours show the observed mean surface temperature anomaly using AWAP data for the entire year 2017 compared to the reference period (1971 – 2000).

From the processed data files, we calculated the severity metrics for initially one

of the CMIP5 models to see the results against the observations as well as under future warming scenarios. We chose the models recommended by Copernicus

and accessible at the CDS. The locations we selected represents where more than 2/3 of the Australian population lives and therefore our initial analysis

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already provides strongly relevant impact value to stakeholders in the health

sector in these regions Figure 6 is a typical representation of the changes in heatwave severity from our

analysis. It shows the observed severity for summer (left column) and the changes at different periods but also under two different emission scenarios.

While the mean change in severity is around 2-3 (e.g. pushing these locations by 2-3 further into higher severity), Figure 6 particular emphasises the fact that a significant number of models simulate stronger changes so that the 90th

percentile of change exceeds 5 of more (in both medium and higher emission scenarios).

Figure 6: Australian summer (DJF) heatwave severity from observations (LEFT column) and its change (RIGHT columns) as simulated by the selected CMIP5 models. The second and third column show the change under the medium scenario (RCP45) for the mid-century period and at the end of this century respectively. The last two columns are the same but for the high emission scenario (RCP85). The first row shows the MEAN heatwave severity (Observation and changes), while the second and third row show the 10th and 90th percentile of the severity in observations and also the change. The last row then shows the spread (e.g. P90 – P10).

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Figure 7: Same as Figure 6, but for Australian spring season (SON).

Figure 7 then shows the same picture, but for Australian spring season. Here we see the next significant result from this analysis: some of the strong changes in

heatwave severity are in the two off-seasons (spring and autumn) with strong changes simulated by the chosen CMIP5 models.

The second metric we analysed is the heatwave duration and Figure 8 shows the annual change in heatwave duration days for the two scenarios and time periods.

Figure 8: Australian annual change in heatwave days under two different emission scenarios (RCP85 top and RCP45 bottom) and for two different time periods (mid-century left and end-century right) based on 18 CMIP5 models.

There is a straight forward relationship in increased heatwave days with increasing emission scenario and duration of this emission (e.g. RCP85 and end-

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century shows the strongest increase in number of heatwave days). We also see

that the tropical regions will see in general larger increases in heatwave days than mid-latitude regions of Australia.

4.7 Step 7: Show results using communication tool

Description: Provide the data/results locally using the communication tools from the C3S Global Impacts provider.

Results: This section described the results with a focus on urban regions where most of the Australian population resides. We have calculated the heatwave

metrics for Hobart, Adelaide, Perth, Brisbane, Melbourne, Sydney and Darwin. Future analysis will add more centres such as Canberra and Cairns.

Figure 9 shows the large impact of different emission scenario pathways on the

number of heatwave days (of any severity) in Brisbane with an ~50% increase from the medium scenario (RCP45) to the high emission scenario (RCP85) by the end of this century. This is similar for all urban regions because of the strong

relationship between emission scenario strength and temperature increases into the future. For Brisbane this number increases from currently around 12 days to

40 (under RCP45) or 50 (under RCP85) by mid-century and 58 (under RCP45) or 107 (under RCP85) by the end of this century.

Furthermore, Figure 9 (RIGHT) shows the strong increases in heatwave severity

especially in the off-seasons (spring and autumn) and by the end of the century heatwaves will also occur in winter in Brisbane.

Figure 9: (LEFT) Example of increase in heatwave days of any severity for an urban region (here: Brisbane) between now and the end of the century. Shown are the mean increases for the medium and high emission scenario (orange lines). (RIGHT) Change in heatwave severity (y-axis) for all seasons. Shown is the maximum seasonal heatwave severity for current climate (light yellow) and the two future emission scenarios for mid-century and late-century periods (lighter and darker colours respectively). Both sub-figures were calculated using results from the selected 18 CMIP5 models.

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Looking more closely into the severity changes by the end of this century, Figures 10 and 11 show the time series of heatwave severity under future emission scenarios, but also including the current climate observations for

comparison.

Figure 10: 30 years of monthly average heatwave severity for Sydney (LEFT) and Brisbane (RIGHT) from recent climate [observations (GREEN)] to the future climate [Model Mean (RED) from selected CMIP5 models] under the high emission scenario (RCP85) during middle of the 21st century. The YELLOW shading indicates the spread in the model ensemble (e.g. within the selected CMIP5 models) for the future scenario (e.g. p10 and p90 of the 20 models).

We can see in Figure 10 that observed heatwaves don't occur every year in

Sydney and Brisbane and are confined to mid-summer. Future projected heatwaves show some increases in intensity (e.g. yellow area exceeds observed)

but not necessarily on average. There is a strong effect of heat waves occurring during the transition seasons (e.g. spring and autumn) which can be seen as both the red lines and the yellow areas are 'broader' compared to the green

observations. Please note that Figure 10 shows the mid-century situation (e.g. for years 2040 – 2070).

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Figure 11: 30 years of monthly average heatwave severity for Darwin (LEFT) and Sydney (RIGHT) from recent climate [observations (GREEN)] to the future climate [Model Mean (RED) from selected CMIP5 models] under the high emission scenario (RCP85) during end of the 21st century. The YELLOW shading indicates the spread in the model ensemble for the future scenario (e.g. p10 and p90 of the 20 models).

Towards the end of this century, the heatwave severity time series shows some remarkable features, especially in tropical regions. Figure 11 shows the heatwave severity for the last 30 years in this century for Darwin and Sydney under the

high emission scenario (note: these are monthly averaged values in both Figure 10 and 11 and can't be directly compared to the daily values elsewhere; but

within these figures they are internally consistent). Firstly, we see that the observations show heatwaves occuring typically prior to the Australian monsoon rainfall onset (often in December). Apart from the higher severity values towards

the end of the century (e.g. compare Figure 10 left with Figure 11 right), tropical locations show a very strong widening of the heatwave severity (both in mean

and model spread) into the off-seasons (Figure 11 left for Darwin). In fact, for Darwin there are not many non-heatwave days simulated by these models by the end of the century.

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5- Conclusion of full technical report

This project utilized the capabilities of the Copernicus Climate Change Service to create an important series of outcomes which will add value to the strategic

plans of state health services in Australia. The key findings of our study are that (a) future heatwave severity is strongly

increasing with increasing CO2 emission strength (e.g. from RCP45 to RCP85 based on selected CMIP5 models), but not homogenous across the continent. The

strongest increases in severity are along the east coast; (b) heatwaves will occur more often in the off-seasons (e.g. spring and autumn) under either emission scenario, but most pronounced in the tropical regions of Australia.

The C3S_422_Lot1_SMHI contract allows for our local stakeholder to have easy

access to this vital information through the creation of an interactive climate atlas.

A future potential implementation of the heatwave indicator in the Copernicus CDS toolbox, together with the access of data, as e.g. the climate projections of

CMIP5 models, and the data processing would be a useful tool not only for Australian stakeholders but user around the globe who tackle with heat waves in a changing climate.

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http://dx.doi.org/doi:10.1175/BAMS-D-11-00094.1

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