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CLIMATE PROJECTIONS FOR NSW AND THE ACT

About the NARCliM climate projections

The New South Wales (NSW) and Australian Capital Territory (ACT) Regional Climate Modelling (‘NARCliM’) Project began in 2011. The project was undertaken because regional decision-makers and impact assessment researchers needed high-resolution climate change projections. Previously, climate change information had been at a scale that could not be used for localised, regional decisions.

NARCliM has produced an ensemble of robust regional climate projections for south-eastern Australia that can, for example, be used by the NSW and ACT community to plan for a range of likely future changes in climate1.

NARCliM is a research partnership between the NSW, ACT and South Australian governments and the Climate Change Research Centre at the University of New South Wales.

These partnerships have provided oversight and technical guidance for the development and quality management of NARCliM outputs.

The first NARCliM climate projections (‘NARCliM1.0’) were first released in 2014. The next generation of climate projections (‘NARCliM1.5’) was released in late 2020.

Modelling future climate on a regional scale: NARCliM and CORDEX domains

The NARCliM data provides dynamically downscaled climate projections for two domains:

1. The whole of Australasia. The extent and spatial resolution of this domain was chosen to be consistent with the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework2, and hence is called the ‘CORDEX domain’ (see black box on map below). It was modelled at a 50-kilometre resolution.
2. A region of south-eastern Australia, called the ‘NARCliM domain’ (see red box on the map) was modelled at a 10-kilometre resolution3.

The CORDEX and NARCliM domains

The extent and spatial resolution of the NARCliM domain was chosen to provide robust simulations of NSW and ACT climate, particularly as this section of the eastern seaboard contains almost half the population of Australia. The domain covers a large area of ocean and the Tasman Sea. This was specifically chosen to capture offshore climate events such as storms generated from east coast lows that bring important rainfall to New South Wales. In the map, Australian capital cities in the inner 10-kilometre domain are shown with red dots and those outside the 10-kilometre domain are shown with black dots.

NARCLIM CLIMATE MODEL SELECTION

Global and regional climate models

The NARCliM projections have been generated from four global climate models (GCMs) and dynamically downscaled by three regional climate models (RCMs). This creates a 12 model ensemble.

The four selected GCMs (MIROC, ECHAM, CCCMA and CSIRO Mk3.0) form part of the World Climate Research Program's (WCRP) Coupled Model Inter-comparison Project phase 3 (CMIP3) models, which were used by the Intergovernmental Panel on Climate Change (IPCC) for its Fourth Assessment Report. The Weather Research and Forecasting (WRF) regional climate model (RCM) has been used to dynamically downscale projections from the GCMs.

This ensemble approach is used to ensure robust climate projections that are at the regional scale span the likely future climate. The 12 models were run using a single, representative emissions scenario: the IPCC high emissions scenario A2 from the Special Report on Emissions Scenarios. The 12 models were run for three time periods: 1990 to 2009 (base), 2020 to 2039 (near future), and 2060 to 2079 (far future). A reanalysis dataset is also available for the period 1950 to 2009.

GCM selection

Regional climate modelling requires substantial computing and data storage facilities, restricting the number of GCMs that could be used for NARCliM. This meant that a systematic approach was required to ensure the best choices of GCMs for NARCliM.

The GCMs were chosen such that the full range of uncertainty within the CMIP3 GCMs was captured by models that simulated the NSW climate well. Even if a GCM can simulate the current climate, it may not necessarily be able to simulate the future climate well. Choosing GCMs that span the range of projected future climate reduces the uncertainty of the projections.(1)

The NARCliM GCMs were selected by using a three-stage process:(4)

1. The performance of a total of 23 CMIP3 GCMs was evaluated, and models that did not adequately simulate the historical climate were removed.
2. The set of GCMs that performed well was then ranked on the basis of a measure of their independence (i.e. whether they exhibited the same strengths and weaknesses in their representation of the climate).
3. The GCMs were then evaluated on the basis of their projections of future climate change. The most independent models that spanned the largest range of plausible future climates were chosen.

The selection process referred to the published scientific literature to identify GCMs that performed poorly over south-eastern Australia. The performance of each of the 23 CMIP3 models was ranked on the basis of the findings of 12 independent, peer reviewed studies. Further information on individual model performance and study references is provided in Technical Note 1, Table 1: Summary of model assessments. The six worst-performing models were removed from further analysis.

The remaining GCMs were then ranked on the basis of independence,(5) focusing on daily mean temperature and precipitation, as these variables are of the most interest to users of climate change information. Further information on individual model performance is provided in Technical Note 2, Table 2: The absolute GCM independence coefficient for each model.

Each of the models’ projected changes in future temperature and rainfall were then mapped and the four most independent models spanning the extent of future climate change were chosen. Further information is provided in Technical Note 1, Figure 1: Future change space for the GCMs.

The GCMs selected for NARCliM were MIROC3.2 (medres), ECHAM5, CCCM3.1 and CSIRO-Mk3.0.

RCM selection

To further ensure that the NARCliM projections spanned the range of uncertainty in regional climate models, a process similar to that used for GCM selection was used to choose the best regional climate models.(6)

1. The performance of RCMs was evaluated over south-eastern Australia and models that did not adequately simulate the climate were removed.
2. From the set of RCMs that performed well over south-eastern Australia, a subset was chosen so that each RCM was as independent as possible from the others.

The WRF modelling system has been demonstrated to be effective in simulating temperature and rainfall in NSW(7,8) and provides good representations of local topography and coastal processes. This model was jointly developed by several major research centres in the US and is widely used internationally.(9,10)

The WRF modelling system has different settings that reflect uncertainties in our understanding of some physical processes. Varying these settings among model runs and using several different settings with each GCM provides a more realistic set of projections. Performing multiple model runs also captures more reliable information on rare, extreme weather events, such as heatwaves, heavy rain and drought.

For evaluation, the WRF model was run with 36 different physics scheme settings using combinations of the Cumulus convection scheme, the cloud microphysics scheme, radiation schemes and the Planetary Boundary Layer scheme.

Because of computational limitations, not all RCM combinations could be included in the full NARCliM project. The performance and independence of each RCM were evaluated and used to simulate a range of storm types that affect the state, such as East Coast Lows, with performance testing for precipitation, minimum and maximum temperatures, mean sea level pressure and wind.(1,11,12)

The RCM ensemble was evaluated over south-eastern Australia in order to remove any models that were not able to adequately simulate the climate. From the set of RCMs that performed well, a subset was chosen, so that each was as independent as possible from the others. On the basis of model performance and independence, three WRF model configurations were selected for use in the downscaling.

NARCliM PERFORMANCE AND BIASES

Performance of NARCliM GCMs and RCMs

Climate models provide projections of future climate changes derived from fundamental principles of the climate system. An advantage of this approach is that future changes in all climate variables are derived together in a physically consistent way. A disadvantage is that models cannot perfectly represent the Earth’s climate – some regions of the modelled climate may be slightly hotter or drier, for example, than the climate we experience in reality. These differences are known as error or bias.

Despite climate modelling representing the best available information on plausible future climates, no climate model provides a perfect representation of the real climate system. For this reason there are differences between the output of the NARCliM simulations and observations of the climate.(13)

The performance of the global climate models (GCMs) and regional climate models (RCMs) selected for NARCliM are discussed below, and preliminary findings on the performance of the NARCliM modelling presented.

Known biases and issues in NARCliM

Climate models are not perfect and often display errors or biases in their modelled climates. For some applications these biases may present significant difficulties when attempting to use the projected climates, particularly if the impact or application is sensitive to non-linearities in the system such as threshold effects (e.g. the number of days with temperatures above 35°C).

The chosen RCMs were found to act independently but do have a tendency towards overestimating precipitation and underestimating maximum temperature. Therefore, in addition to making available the actual NARCliM output data set, statistical methods were used to correct NARCliM model bias and produce a bias-corrected data set. Only rainfall and temperature (minimum and maximum) were bias corrected as they are the only variables with long, reliable observational time series on which to base the bias correction.

The method used to produce bias-corrected data is outlined in Technical Note 3. Given that bias correction removes the physical consistency between climate variables, this technical noted also present guidelines to consider when deciding whether to use actual model output or bias-corrected data.

NARCliM CCCMA3.1 simulations

There is no daily data for any variable for 29 February on any year for the NARCliM CCCMA3.1 simulations. This is because the CCCMA3.1 Global Climate Model that is being downscaled works on a calendar that has no leap years.

Evaporation data

At present, no evaporation data are available from the NSW Climate Data Portal. A dataset for potential evaporation has been generated from other climate variables and will be made available through the data portal in the future.

Mean surface air temperature (tasmean)

The daily tasmean datasets contain spatial discontinuities/gaps over the ocean. This is likely due to discontinuities in data from the Global Climate Models that are being downscaled. These discontinuities coincide with discontinuities in the daily hussmean (mean surface specific humidity) datasets. Monthly data and data over the land are not affected.

Precipitation accumulation (pracc_fl)

A known issue with the Regional Climate Model's convection scheme resulted in unrealistically large values for isolated gridpoints for some time steps in the NARCliM simulations. The NARCliM 50 kilometre resolution data is most impacted. The pracc_fl files contain data in which these unrealistic values of daily precipitation above 1200 millimetres have been filtered out. Over the entire domain a very small number of data points are affected. To correct this the average rainfall of four surrounding grids was used to replace the unrealistic value of daily rainfall.

Precipitation accumulation - bias corrected (pracc_bc)

There are some spatial discontinuities in this dataset in some remote inland areas of the Northern Territory, Queensland and South Australia. This is due to the scarcity of observational climate data in these areas. In these areas, the Australian Water Availability Project (AWAP) gridded dataset used for bias correction relies heavily on interpolation of data between a small number of meteorological observation stations. Therefore, an alternative bias-correction approach that used regional-average data, rather than data for individual grid points, was used in these areas. For further information, please see NARCliM Technical Note 3.

Mean surface specific humidity (hussmean)

The daily hussmean datasets contain spatial discontinuities/gaps over the ocean. This is likely due to discontinuities in data from the Global Climate Models that are being downscaled. These discontinuities coincide with discontinuities in the daily tasmean (mean surface air temperature) datasets. Data over the land are not affected.

Mean sea surface temperature (sstmean)

The daily sstmean datasets for the NARCliM MIROC3.2 simulations contain spatial discontinuities in December 1991. This is due to discontinuities in data from the MIROC3.2 Global Climate Model that is being downscaled. Monthly data are not affected.

Statistical robustness

Because the state of the atmosphere, land, ocean and cryosphere changes from year to year, climate scientists do not analyse individual years from climate projections but use averages of multiple decades to describe the state of the climate. This is why base periods are multi-decadal in length (see above). Thus, when users are interested in a particular future year in NARCliM they should use an average of the surrounding years. For example, instead of looking at the climate in 2070, use the 20 year average from 1960 to 1979. Generally, the more years you include in your analyses the more reliably you can determine statistically significant changes.

The climate projections in NARCliM are based on different global climate model inputs and different configurations of the Weather Research and Forecasting (WRF) regional climate model. Using multiple climate projections, like those provided in NARCliM, as opposed to single projections also improves confidence in your analysis, since uncertainty in our knowledge of the climate is being accounted for. For example, knowing that multiple models agree on the direction of change for a variable (e.g. that the daily temperature range decreases into the future over Sydney) provides high confidence in the result, whereas if some models show an increase and others show a decrease there is less confidence in the result, even if the average over all projections happens to be a large number.

Certainty and uncertainty

What are the sources of uncertainty in climate projections?

Uncertainty about future climate projections comes from several sources. Here these sources of uncertainty are broken down into three main categories (17). The first source of uncertainty, and one of the largest unknowns, is the future emissions of greenhouse gases and aerosols. Since this uncertainty is impossible to quantify mathematically, it is presented as a series of possible emission scenarios or projections. These scenarios are then used in GCM simulations to study the impact on climate.

The second and the third sources of uncertainty deal with the response of the physical system to the increase in greenhouse gases and aerosols. Specifically, the second source is a large scale response to changes in atmospheric constituents. It can be sampled by using different GCM (“model structural uncertainty”), and different parametrisations within a single GCM (“model parametric uncertainty”). The third source is a local response given a large-scale response. In the case of RCMs this includes the uncertainty in model physics and structure similar to issues associated with GCMs, while for statistical downscaling this includes uncertainties associated with the statistical technique used. In combination these sources of uncertainty provide a limit to the confidence that can be placed in any particular projection of future regional climate.

Please see the Data Disclaimer below.

How is uncertainty addressed in regional climate modelling?

Quantifying uncertainty is done by creating a collection, or ensemble, of climate simulations that sample various parts of the uncertainty described above. Emission scenario uncertainty is addressed by running simulations from more than one scenario. To quantify the structural uncertainty associated with GCMs an ensemble of many GCMs should be used and similarly for RCMs (or dynamical downscaling) many RCMs should also be used. Ideally these GCMs and RCMs would be independent of each other ensuring they are sampling different parts of the plausible future climate space. Once an ensemble sampling these uncertainties has been established there are multiple methods for combining the information to establish a probabilistic future climate change prediction. Déqué and Somot (2010)(18) used a technique that weighs a frequency distribution based on model performance. Bayesian analysis has also been used in a number of ways (Tebaldi et al., 2004(19), 2005(20); Buser et al., 2010)(21) and is still an area of active research.

How was uncertainty addressed in the NARCliM modelling?

Due to the many uncertainties involved in producing climate projections, NARCliM is providing an ensemble of 12 simulations (rather than a single projection). Model independence however means that the chosen models do not exhibit the same strengths and weaknesses in their representation of the climate.

The WRF modelling system was run with different settings which reflect uncertainties in our understanding of some physical processes. Varying these settings between model runs and using several different settings with each GCM provides a more realistic set of projections. Performing multiple model runs also captures more reliable information on rare, extreme weather events, such as heatwaves, heavy rain and drought. Initially the WRF model was run with 36 different physics scheme settings, with model performance tested for five climate variables. On the basis of model performance and independence, three WRF model configurations were selected for use in the downscaling.(4)

Interpreting Future Climate Projections

Using ensemble projections

Due to the uncertainties involved in producing climate projections, NARCliM is providing an ensemble of 12 simulations (rather than a single projection). Each of these simulations represents an “equally plausible” future climate, with the range of future changes within the ensemble providing a measure of the confidence in any particular change.

The use of all 12 ensemble members is recommended to estimate climate change impacts ensuring an “equally plausible” range of possible impacts. If this is not possible (due to logistical constraints etc.), you could choose a smaller number of simulations (two or four) that span the range of the climate variable for which your system is most sensitive.

You could also use the central estimate (multi-model mean), keeping in mind the confidence range of the full ensemble.

What is the central estimate and how should it be used?

Central estimates of future climate change are used to summarise changes projected by the 12 modelling simulations undertaken for NARCliM. Multi-model median and multi-model mean are commonly used internationally to provide a reasonable “middle” projection.

The multi-model mean method was used to provide a central estimate for the NARCliM projections because of the model independent measure used to select the four GCMs. GCMs that are most independent contribute the most independent information in calculating a multi-model mean. This means that the multi-model mean of a selection of highly independent GCMs from a larger ensemble of GCMs (e.g. the CMIP3 ensemble) should be closer to the multi-model mean of the larger ensemble than the multi-model mean of a selection of GCMs that are less independent.

When using central estimates it is important to note the level of model agreement on the direction of change. If models are projecting both increases and decreases, the multi-model mean is likely to be within the range of model uncertainty and needs to be interpreted with caution. In such cases, reference should be made to maps showing the suite of individual simulation projection results and to location-specific bar graphs showing the span of plausible changes.

What does model agreement mean?

Model agreement refers to the number of individual model simulations within the ensemble that agree on the direction of change (e.g. increase or decrease in rainfall) and is used to determine the level of confidence in the change projected. The more models agree, the greater the confidence in that direction of change.

For example, if all simulations project a rainfall decrease for an area then this decrease has high confidence. If nine of twelve simulations project a rainfall decrease for an area then this decrease has medium confidence.

Selection of CMIP3 models

NARCliM utilises an ensemble of GCMs that were developed as part of the Coupled Model Intercomparison Project (CMIP). CMIPs are gathered and reviewed by the Intergovernmental Panel on Climate Change (IPCC) for the international climate change Assessment Reports.

• CMIP3 is the model ensemble for the IPCC’s Fourth Assessment Report (AR4) and was released in 2010. The NARCliM project uses models from this ensemble as input into the Weather Research and Forecasting regional climate model.
• CMIP5 is the model ensemble for the IPCC’s Fifth Assessment Report (AR5) and was released in 2013. The next set of climate projections for NSW will use models from this ensemble as input into the Weather Research and Forecasting regional climate model.
• CMIP6 is the model ensemble for the IPCC’s yet-to-be-released Sixth Assessment Report (AR6). CMIP6 models are currently being evaluated.

The following section includes a comparison of CMIP3 and CMIP5. CMIP6 has only recently been released and as results from these models become published a brief summary of the differences to previous generations of CMIP will be evaluated.

EMISSIONS SCENARIOS

What is an emission scenario?

An emissions scenario is an estimate of future emissions based on our understanding of natural sources of greenhouse gases and on assumptions about future socio-economic trends, i.e. how much greenhouses gases will be released into the atmosphere by humans.

Because it isn’t clear exactly how global social and economic systems will respond to emissions reduction programs a range of scenarios are used to describe possible future trends in emissions of greenhouse gases into the atmosphere.

In 2010 the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) outlined emission scenarios that were used in the IPCC’s ensemble of climate models (CMIP3) for the IPCC’s Fourth Assessment Report (AR4).These ranged from A1FI (a future world of rapid economic growth with fossil-intensive technologies) to B1 (rapid transformation to a service and information economy with clean and resource-efficient technologies).

These scenarios provide a range of changes to the climate in response to the emissions: the A2 scenario is projected to result in warming by approximately 3.4°C by 2100. The more optimistic B1 scenario projected a warming of the planet by 1.8°C by 2100.

Why did we use the SRES A2 emissions scenario for NARCliM?

The NARCliM project uses a single emissions scenario, the SRES A2 scenario. At the time that NARCLIM started, in 2010, the SRES emission scenarios underpinning the IPCC’s Third Assessment Report (2001) and Fourth Assessment Report (2007) were the only set of emission scenarios available at the time.

The SRES A2 emission scenario was selected for the NARCliM climate projections because it reflected trends in global emissions.(22,23) Recent publications have confirmed that we are tracking at the higher end of the A2 scenario.(24)

What is a representative concentration pathway (RCP)?

In 2013 the Intergovernmental Panel on Climate Change released the Fifth Assessment Report which was based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which incorporates the latest versions of climate models and focuses on a new set of scenarios. These scenarios span a range of plausible radiative forcing scenarios called representative concentration pathways (RCPs). Radiative forcing, measured in watts per square metre (W/m²), is the extra heat the lower atmosphere will retain because of the presence of additional greenhouse gases and aerosols.

Representative concentration pathways take into account the impact of atmospheric concentrations of greenhouse gases and aerosols (such as sulfate and soot), along with the uncertainty in possible future emissions.

The RCPs span a wider range of possibilities than the SRES emission scenarios. The RCPs include a very low forcing level (RCP2.6), two medium stabilization scenarios (RCP4.5 and RCP6), and one very high emission scenario (RCP8.5). The RCP8.5 pathway, which arises from little effort to reduce emissions and represents a failure to mitigate warming by 2100, is similar to the highest SRES scenario. The next iteration of NARCliM will utilise CMIP5 models as their input and hence will use RCP’s for their emission scenarios.

How does SRES A2 compare with the RCP future projections?

Although the IPCC’s Fifth Assessment Report (AR5) is based primarily on results from the CMIP5 modelling using RCPs, the report also uses results from the SRES CMIP3 modelling and therefore identifies similar scenarios from each set.

For the latter half of the 21st century, SRES A2 has a similar trajectory to that of RCP8.5, with both reaching about 8 W/m² by 2100 (Figure a). SRES A2 is also similar to RCP8.5 in terms of changes in global mean temperature (Figure b).

Figure (a) Projected radiative forcing (W/m²) and (b) global mean surface temperature change (°C) over the 21st century, as determined by using the Special Report on Emissions Scenarios (SRES) and Representative Concentration Pathway (RCP) scenarios (ref) (temperature changes are decadal averages based on the model ensemble mean). Figure (b)

For RCP8.5, global mean surface temperatures for 2081 to 2100, compared with those for 1986 to 2005, are likely to be in the range of 2.6 C to 4.8°C higher, with a multi-model mean increase of 3.7°C4. The projected warming for SRES A2 for the 2090 to 2099 period, relative to 1980 to 1999, is given by IPCC AR4 as 2.0°C to 5.9°C, with a best estimate of 3.4°C.(25)

ACCESSING DATA ON THE PORTAL

Accessing data on the Climate Data Portal is a simple process of creating a user account and searching the data. There are resources available to assist in this process, including our team of climate researchers. Please review and consider the terms of use provided on this webpage, including how the Department protects your privacy and the need for users to appropriately acknowledge the data.

Types of user accounts

Currently, there are two levels of registered user accounts on the Climate Data Portal: ‘read only’ and ‘technical user.’ When an account is created, it is initially designated read only access. Read only users can see all datasets and read metadata and descriptions, but are unable to download data. Technical users can download all datasets.

Creating a user account

Select the Login button (top right) to create an account. Once an account is created, you will receive an email confirmation. To be granted technical user status, you will need to complete a short survey so we can better understand your data needs.

Searching the data

From the homepage, select the Data tab. Search by keyword or use the filters on the left to focus your choices.

Note: certain search criteria will remove some search options. For example, by selecting NARCliM, you will only be able to see options for NARCliM data. You will not see options for CORDEX data. As you filter, datasets that meet the criteria will be visible on the right side. Basic descriptions are provided with each returned dataset to assist searching.

Selecting and downloading data

Once you have searched, filtered and located your dataset of interest, click on the name. You’ll be redirected to a page describing the dataset. From here you can download the dataset. If multiple datasets meet your criteria, you have the option of downloading a single dataset, multiple datasets individually, or you can download them all at once.

Data licensing

This NSW Climate Data Portal website contains information, data, documents, pages and images prepared by the NSW Government Department of Planning, Industry and Environment (‘the Department’) and third parties. The Department supports and encourages the dissemination and exchange of publicly funded information and endorses the use of the Australian Governments Open Access and Licensing Framework (AusGOAL). Unless otherwise stated, all Department material available on the NSW Climate Data Portal is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). The license terms can be viewed at Creative Commons.

Data disclaimer

Data available on the NSW Climate Data Portal has been prepared in good faith, exercising all due care and attention, but no representation, express warranty or implied warranty, is made to the relevance, accuracy, completeness or fitness for purpose of this information in respect of any particular user circumstances. The climate data projections include an inherent degree of uncertainty. You are responsible for assessing the relevance of the data for your intended purposes.

The Department endeavours to make sure that information provided on this website is correct at the time of its publication. However, as necessary you should obtain independent advice before making any decision based on the information on this site. The information on this site is made available on the understanding that the State of New South Wales and the Department accept no responsibility or liability for any damage, cost, loss or expense incurred by you as a result of:

• any error, omission or misrepresentation on this site
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• without limiting the above, any delay, failure or error in recording, displaying or updating information, including but not limited to, data relating to credit holdings.

Where there are known limitations to the information provided online, we will include an explanation of these limitations wherever possible. If there is any material that you consider to be misleading or inaccurate, please let us know and it will be corrected as soon as possible.

Your privacy

The Department will handle information you enter to access the NSW Climate Data Portal in accordance with the NSW Government’s and the Department’s privacy and security policies. The Department’s privacy practices are regulated by the Privacy and Personal Information Protection Act 1998 (NSW) and the Department’s privacy management plan. We will not disclose your personal information to anyone without your consent, unless legally required to do so. Your consent to disclose information may be sought by us or given by yourself for a specific purpose. For more information, please visit the Department’s privacy webpage.

Acknowledging the use of NARCliM data

Written work of any form, based in whole or in part on data provided by the NSW Climate Data Portal will acknowledge the data has been provided by the Government of New South Wales, Australia. This written work will include the following acknowledgement:

For NARCliM Project data released in 2014, ‘NARCliM1.0’:

This work used data derived from the NSW and ACT Regional Climate Modelling (NARCliM)1.0 project and provided by the Government of New South Wales, Australia.

Citation: Evans JP, Ji F, Lee C, Smith P, Argueso D and Fita L 2014, Design of a regional climate modelling projection ensemble experiment, Geoscientific Model Development, 7, pp.621–629. Data accessed via the NSW Climate Data Portal [on date].

For NARCliM Project data released in 2020, ‘NARCliM1.5’:

This work used data derived from the NSW and ACT Regional Climate Modelling (NARCliM) Expansion (1.5) project and provided by the Government of New South Wales, Australia.

Citation: Nishant N, Evans JP, DiVirgilio G, Downes S, Cheung KW, Tam E, Miller J, Beyer K and Riley M, Introducing NARCliM1.5: evaluating the performance of regional climate projections for southeast Australia for 1950–2100 (Submitted for publication). Data accessed via the NSW Climate Data Portal [on date].

ABOUT US

The DPIE Climate Research Team

The NARCliM project was developed, led and delivered by the Climate Research Team at the NSW Department of Planning, Industry and Environment (DPIE), in partnership with the University of New South Wales (UNSW). The Climate Research Team has collaborative research relationships with various research partners including universities, other jurisdictions and institutions such as CSIRO and the Bureau of Meteorology.

We are a multi-disciplinary team that delivers complex and integrated climate impacts and adaptation research. Our key research areas include understanding the past, present and future climate of NSW and the impacts of climate change on NSW communities and environment. Our specific research areas include:

• Providing up-to-date, fit-for-purpose and target climate data to NSW communities
• Designing and delivering enhanced regional climate projections
• Extreme weather and climate events
• Climate change impacts on biophysical systems, such as hydrology, bushfire, biodiversity, etc.
• Natural hazard mapping for future climates
• Climate change impacts in NSW’s Alpine region
• Urban heat and green cover research and evaluation
• Climate change impacts on socio-economic systems, including vulnerable communities, economic sectors of NSW and integrated regional impacts
• Human Health & Social Impacts of Climate Change (the fourth research node of the NSW Adaptation Research Hub; established in 2017)

The Climate & Atmospheric Sciences Branch

The Climate Research Team works within the Climate & Atmospheric Science Branch, which is a part of the Science Division in the Energy, Environment & Science Directorate. The Branch is responsible for delivery of the priority climate and atmospheric science needs of DPIE and the Environment Protection Authority (EPA) which are outlined in two of the agency’s Knowledge Strategy theme areas - Pollution, Resource Efficiency and Climate Change Impacts and Adaptation.

The Branch supports DPIE to meet its goals to “Support economic development without devaluing the environment” and “Build resilience to climate change and environmental hazards and risks”. See the DPIE website for more information.

The Branch delivers NSW Government priorities in climate and air quality science, identifies emerging research needs, and provides the public with information on climate change and air quality in their region.

Other datasets managed by the Climate Research Team

In addition to our regional climate simulations, the Climate Research Team led the Urban Heat and Green Cover (UHGC) project, which delivered the following datasets:

• NSW Urban Vegetation Cover to Modified Mesh Block 2016
• Greater Sydney Region Urban Vegetation Cover to Modified Mesh Block 2016
• NSW Heat Vulnerability Index to ABS SA1 Level 2016
• NSW Urban Heat Island to Modified Mesh Block 2016

Further datasets will be released in the coming months. These are:

• NSW Urban Vegetation Cover 2016 imagery
• Land surface temperature and urban heat imagery
• 2014 data for selected priority precincts

Data from the UHGC Project is available through the NSW Sharing and Enabling Environmental Data portal (SEED). Please visit the SEED portal to access datasets on urban vegetation cover, land surface temperatures and areas of heat vulnerability.

Future work

CONTACT US

For comments, questions or to explore research collaborations please contact us at climate.research@environment.nsw.gov.au

CITATIONS

1. Evans JP, Ji F, Lee C, Smith P, Argüeso D and Fita L (2014) Design of a regional climate modelling projection ensemble experiment – NARCliM (PDF 1.43 MB). Geosci. Model Dev., 7, 621–629.
2. NARCliM 10-km domain covers south-eastern Australia and much of the Tasman Sea (135-165E, 23-40S).
3. Giorgi F, Jones C and Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework (PDF 504 KB), WMO Bulletin, 58(3), 175–183.
4. Evans JP and Ji F (2012). Choosing GCMs, NARCliM Technical Note 1, NARCliM Consortium, Sydney, Australia.
5. Bishop CH and Abramowitz G (2013). Climate model dependence and the replicate earth paradigm. Clim. Dynam., 41, 885–900, doi:10.1007/s00382-012-1610-y.
6. Evans JP and Ji F (2012). Choosing the RCMs to perform the downscaling (PDF 298 KB). NARCliM Technical Note 2, 8 pp., NARCliM Consortium, Sydney, Australia.
7. Evans JP and McCabe MF (2010) Regional climate simulation over Australia’s Murray-Darling Basin: a multi-temporal assessment, J. Geophys. Res.-Atmos., 115, D14114, doi:10.1029/2010JD013816.
8. Evans JP and McCabe MF (2013) Effect of model resolution on a regional climate model simulation over south-east Australia, Clim. Res., 56, 131–145, doi:10.3354/cr01151.
9. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang XY, Wang W, and Powers JG (2008). A Description of the Advanced Research WRF Version 3, NCAR Technical Note, National Center for Atmospheric Research, Boulder, CO, USA.
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Using NARCliM 1.5

Reference list for NARCliM data

The following reference list represents many key publications addressing the performance and application of the NARCliM data.

Bao J, Sherwood S, Alexander L and Evans 2017, Future increases in extreme precipitation exceed observed scaling rates, Nature Climate Change, DOI: 10.1038/nclimate3201. (PDF 2.2MB)

Clarke H and Evans JP 2018, Exploring the future change space for fire weather in southeast Australia, Theoretical and Applied Climatology, DOI: 10.1007/s00704-018-pp.2507-4. (PDF 3.8MB)

Clarke H, Pitman AJ, Kala J, Carouge C, Haverd V and Evans JP 2016, An investigation of future fuel load and fire weather in Australia, Climatic Change, 139(3), pp.59–605, DOI: 10.1007/s10584-016-1808-9. (PDF 1.7MB)

Cortés-Hernández VE, Zheng F, Evans JP, Lambert M, Sharma A, and Westra S 2015, Evaluating regional climate models for simulating sub-daily rainfall extremes, Climate Dynamics, DOI: 10.1007/s00382-015-2923-4.

Evans JP, Argueso D, Olson R and Di Luca A 2016, Bias-corrected regional climate projections of extreme rainfall in south-east Australia, Theoretical and Applied Climatology, DOI: 10.1007/s00704-016-1949-9. (PDF 6.0MB)

Di Virgilio G, Evans JP, Di Luca A, Olson R, Argüeso D, Kala J, Andrys J, Hoffmann P, Katzfey JJ and Rockel B 2019, Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors, Climate Dynamics, vol.53, pp.2985–3005, DOI: 10.1007/s00382-019-04672-w.

Di Virgilio G, Evans JP, Di Luca A, Grose MR, Round V and Thatcher M 2020, Realised added value in dynamical downscaling of Australian climate change, Climate Dynamics, vol.54, pp.4675–4692, DOI:10.1007/s00382-020-05250-1.

Evans JP, Ekström M and Ji F 2012, Evaluating the performance of a WRF physics ensemble over south-east Australia, Climate Dynamics, 39(6), pp.1241–1258, DOI: 10.1007/s00382-011-1244-5. (PDF 1.6MB)

Evans JP, Ji F, Abramowitz G and Ekström M 2013, Optimally choosing small ensemble members to produce robust climate simulations, Environmental Research Letters 8, 044050, DOI: 10.1088/1748-9326/8/4/044050. (PDF 0.5MB)

Evans JP, Ji F, Lee C, Smith P, Argüeso D and Fita L 2014, Design of a regional climate modelling projection ensemble experiment – NARCliM, Geoscientific Model Development, 7(2), pp.621–629, DOI: 10.5194/gmd-7-621-2014. (PDF 1.4MB)

Evans JP, Kay M, Abhnil P and Pitman A 2017, The resilience of Australian wind energy to climate change, Environmental Research Letters, 13(2), 024014, DOI: 10.1088/1748-9326/aaa632. (PDF 1.5MB)

Evans JP, Di Virgilio G, Hirsch AL, Hoffmann P, Remedio AR, Ji F, Rockel B and Coppola E 2020, The CORDEX-Australasia ensemble: evaluation and future projections, Climate Dynamics, DOI: 10.1007/s00382-020-05459-0.

Di Luca A, Argueso D, Evans JP, de Elia R and Laprise R 2016, Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales, Journal of Geophysical Research – Atmospheres, 121(4), pp.1575–1590, DOI: 10.1002/2015JD024009. (PDF 1.4MB)

Di Luca A, Evans JP and Ji F 2017, Australian snowpack in the NARCliM ensemble: evaluation, bias correction and future projections, Climate Dynamics, DOI: 10.1007/s00382-017-3946-9. (PDF 10MB)

Di Luca A, Evans JP, Pepler A, Alexander LV and Argueso D 2016, Evaluating the representation of Australian east coast lows in a regional climate model ensemble, Journal of Southern Hemisphere Earth Systems Science, 66(2), pp.108–124. (PDF 8.0MB)

Fan Y, Olson R and Evans JP 2017, A Bayesian posterior predictive framework for weighting ensemble regional climate models, Geoscientific Model Development, 10(6):p, pp.2321–2332, DOI: 10.5194/gmd-10-2321-2017. (PDF 1.7MB)

Fita L, Evans JP, Argueso D, King AD and Liu Y 2016, Evaluation of the regional climate response to large-scale modes in the historical NARCliM simulations, Climate Dynamics, DOI: 10.1007/s00382-016-3484-x. (PDF 6.0MB)

Grose MR, Bhend J, Argueso D, Ekström M, Dowdy A, Hoffman P, Evans JP, Timbal B 2015, Comparison of various climate change projections of eastern Australian rainfall, Australian Meteorological and Oceanographic Journal, 65(1), pp.72–89. (PDF 1.6MB)

Gross MH, Alexander LV, Macadam I, Green D and Evans JP 2016, The representation of health-relevant heatwave characteristics in a regional climate model ensemble for New South Wales and the Australian Capital Territory, Australia, International Journal of Climatology, DOI: 10.1002/joc.4769. (PDF 1.1MB)

Herold N, Ekstrom M, Kala J, Goldie J and Evans JP 2018, Australian climate extremes in the 21st century according to a regional climate model ensemble: implications for health and agriculture, Weather and Climate Extremes, DOI: 10.1016/j.wace.2018.01.001. (PDF 6.3MB)

Holgate C, Evans J, Dijk A, Pitman A and Virgilio, G 2020, Australian Precipitation Recycling and Evaporative Source Regions, Journal of Climate, vol.33, pp.1-40, DOI: 10.1175/JCLI-D-19-0926.1.

Ji F, Evans JP, Argueso D, Fita L and Di Luca A 2015, Using large-scale diagnostic quantities to investigate change in east coast lows, Climate Dynamics, DOI: 10.1007/s00382-015-2481-9. (PDF 1.1MB)

Ji F, Evans JP, Di Luca A, Jiang N, Olson R, Fita L, Argüeso D, T-C Chang L, Scorgie Y, Riley M 2018, Projected change in characteristics of near surface temperature inversions for southeast Australia, Climate Dynamics, DOI: 10.1007/s00382-018-4214-3. (PDF 9.9MB)

Ji F, Evans JP, Ekström M and Teng J 2014, Evaluating rainfall patterns using physics scheme ensembles from a regional atmospheric model, Theoretical and Applied Climatology, 115, pp.297–304, DOI: 10.1007/s00704-013-0904-2. (PDF 1.3MB)

Ji F, Evans JP, Teng J, Scorgie Y, Argueso D, Di Luca A and Olson R 2016, Evaluation of long-term precipitation and temperature WRF simulations for southeast Australia, Climate Research, 67, pp.99–115, DOI:10.3354/cr01366. (PDF 4.5MB)

Ji F, Evans JP, Di Virgilio G, Nishant N, Di Luca A, Herold N, Downes SM, Tam E and Beyer K 2020, Projected changes in vertical temperature profiles for Australasia, Climate Dynamics, vol.55, pp.2453–2468, DOI: 10.1007/s00382-020-05392-2.

Macadam I, Argüeso D, Evans PJ, Liu DL, Pitman AJ 2016, The effect of bias correction and climate model resolution on wheat simulations forced with a regional climate model ensemble, International Journal of Climatology, DOI: 10.1002/joc.4653. (PDF 0.7MB)

Olson R, Evans JP, Di Luca A and Argueso D 2016, The NARCliM Project: model agreement and significance of climate projections, Climate Research, 69(3), pp.209–227, DOI: 10.3354/cr01403. (PDF 9.5MB)

Olson R, Fan Y and Evans JP 2016, A simple method for Bayesian model averaging of regional climate model projections: application to south-east Australian temperatures, Geophysical Research Letters, DOI: 10.1002/2016GL069704. [1.5MB]

Pepler A, Alexander L and Evans JP 2016, Zonal winds and southeast Australian rainfall in climate models, Climate Dynamics, DOI: 10.1007/s00382-015-2573-6. (PDF 3.7MB)

Pepler AS, Di Luca A, Ji F, Alexander LV, Evans JP and Sherwood SC 2016, Projected changes in east Australian midlatitude cyclones during the 21st century, Geophysical Research Letters, 43(1), DOI:10.1002/2015GL067267. (PDF 2.5MB)

Perkins-Kirkpatrick S, White C, Alexander L, Argueso D, Boschat G, Cowan T, Evans JP, Ekstrom M, Oliver E, Phatak A and Purich A 2016, Natural hazards in Australia: heatwaves, Climatic Change, DOI: 10.1007/s10584-016-1650-0. (PDF 0.7MB)

Sharples JJ, Cary G, Fox-Hughes P, Mooney S, Evans JP, Fletcher M, Fromm M, Baker P, Grierson P and McRae R 2016, Natural hazards in Australia: extreme bushfire, Climatic Change, 139(1), pp.85–99, DOI: 10.1007/s10584-016-1811-1. (PDF 0.75MB)

Walsh K, White CJ, McInnes K, Holmes J, Schuster S, Richter H, Evans JP, Di Luca A and Warren RA 2016, Natural hazards in Australia: storms, wind and hail, Climatic Change, 139(1), pp.55–67, DOI: 10.1007/s10584-016-1737-7. (PDF 0.9MB)

USEFUL LINKS

NSW Climate Change Fund (CCF), from which NARCliM has been funded

Climate Change in Australia

Bureau of Meteorology – Influences on the Australian climate

Australian Capital Territory Climate Change Strategy

Queensland Future Climate

Climate Futures for Tasmania

Victorian Climate Projections 2019

Western Australia Climate Change

NARCliM information from the University of New South Wales

Explaining climate science and concepts and reducing misinformation