Steve Taitoko

Dr Tomas Remenyi

Paul Stegeman

Data61

Monica Liu

“Climate models are one of the primary means for scientists to understand how the climate has changed in the past and may change in the future. These models simulate the physics, chemistry and biology of the atmosphere, land and oceans in great detail, and require some of the largest supercomputers in the world to generate their climate projections.”

CMIP stands for Coupled Model Intercomparison Projects and has been operating since 1995. As part of the World Climate Research Programme (WCRP), CMIP is currently in its 6th iteration and has coordinated climate model experiments involving multiple international modelling teams worldwide. The main goal of CMIP is to advance the scientific understanding of Earth which the team at Eratos could not be more on board for!

However as is the case with many large and complex data models, accessing them is not always simple let alone applying them. That’s why a small group at Eratos, led by Monica Liu decided to map CMIP6’s ontology to Eratos thereby making the access and use of CMIP6 climate models simpler and easier in order to help us transform the world by understanding it. This article explains the process Monica and the team undertook.

To begin creating the underlying ontology for CMIP datasets in Eratos, our team had to create a high-level mapping of the important CMIP6 labels into core Eratos concepts:

It was important to do an initial high-level mapping to ensure that we included all of the Earth System Grid Federation (ESGF) CMIP6 labels in our discovery to cover every label type that’s used in the CMIP6 datasets. This step allowed us to map or create any required concept in Eratos to be sure we were covering every detail that was necessary to fully describe the datasets. It also encouraged us to complete preliminary research into each CMIP6 label and gather the necessary disparate resources.

Unit and variable creation: building context from the ground up

In order to build out the CMIP6 ontology, the base units and variables were first analysed and created as Eratos ‘units’ and ‘variables’. The CMIP6-specific variables are available through a 

platform provided by the ‘World Climate Research Programme’

 shown in the image below. This tool allowed us to export the list of CMIP6 variable objects.

Each variable contains an ID, CMIP6 specific key, the 

 (used across a majority of climate datasets), the unit of measurement (or scale factor/ratio) and a description. Additionally, some of the CMIP6 specific keys mapped directly to Coordinated Regional Climate Downscaling Experiment (

) variables which is a WCRP framework that many climate scientists adhere to. Following these observations, we realised we would also need to link the CMIP6 variables to their CF standard names and descriptions, as well as possible CORDEX variables for extra context.

Our research then led us to create the below dependency mapping for the CMIP6 variables.

Eratos allows for intelligent linking of resources through the use of unique ‘Eratos Resource Names’ (ERNs), and so we began to create the individual units needed for each variable. Through interrogating the units included in the CMIP6 variable’s export, we concluded there were 67 unique units or scale factors used for measurement. Many resources were used to define the Standard International (SI) base and derived units (see 'Unit References' at the bottom of the page).

After creating the dependent units in Eratos, we created the CMIP6 variables, linking them back to their CF standard name, possible CORDEX key and descriptions using the 

By linking contextual data we built a knowledge base agnostic of naming convention

In order to begin building out the ontology in Eratos we needed to start with the lowest level of dependent data for the CMIP6 datasets, which are the units and variables and work our way up.

As a result, we were able to identify all of the described CMIP6 variables and their corresponding units which are required to be able to describe the datasets and what types of measurement or scaling factors they use.

This step also allows us to build further knowledge and context into the provided CMIP6 variables by incorporating additional resources.

The CF standard name conventions and the CORDEX variables are both highly utilised in the climate sector, and so it’s important to link these with context so that no matter which naming convention a climate scientist uses, through Eratos they only need to know one and they can find the dataset they’re looking for.

Dependency mapping of CMIP6 Institution ID, Source ID, Experiment ID and Sub-experiment ID

Following the creation of CMIP6 variables in Eratos, we proceeded to create a dependency map from the CMIP6 source and experiment IDs found under the 

CMIP6 controlled vocabularies GitHub repo

. (See screenshot below of the dependency map.)

(For a more detailed and interactive view of the above image, see our mapping 

From the diagram, we were able to create the dependent context for each source and experiment in CMIP6.

We then began to build out the CMIP6 source ID ontology. Conscious that CMIP is a collaboration of multiple organisations, in order to create the CMIP6 source ID’s as Eratos ‘models’ we had to create and map the CMIP6 institutions in Eratos as ‘creators’, as well as the licences, which were mapped into Eratos ‘licence’ objects. The CMIP6 institution IDs are available via 

. After generating the unique ERN’s for the CMIP6 institutions and licences, we were able to begin creating the source IDs.

We used the string denoting the activity ID’s, and created CMIP-specific fields in our model schema for the cohort, licence exceptions contact, release year and model components including the realm and nominal resolutions. To generate the CMIP6 source ID’s we used the 

 and mapped them back to the unique ERN’s created for the institutions and licences.

Next, we moved on to create the ontology for the CMIP6 experiment IDs.

To create the experiments as Eratos ‘scenarios’, we first had to create the 

 which we also created as Eratos ‘scenarios’. We saved the unique ERN mapping for the sub-experiments and then moved on to create the experiments.

Once again, we used the string denoting the activity ID’s and then created CMIP-specific fields in our scenario schema to capture the start year, end year, tier, the minimum number of years per simulation and the required and additional model components to include the source types. To generate the CMIP6 experiment ID’s we used 

 and linked the unique ERN’s for the sub-experiments and parent experiments.

In this step, we were able to highlight any dependencies amongst the remaining CMIP6 labels to ensure they were all included in our Eratos ontology. Not only is this context powerful in its own right, but it also allowed us to fill in the final pieces of important information needed to best describe the CMIP6 datasets.

So far our focus has been on the CMIP6 ontology and there's still a way to go to map the rest of the CMIP versions but we are not the experts in this field. We want to facilitate engagement and collaboration around the use of CMIP which is currently occurring with the Australian Federated Climate Data Initiative (FCDI). 

So if you are an expert and would like to contribute to our Community by helping create more utility of CMIP, CORDEX and other Climate Science standards please get in touch, we welcome any chance to collaborate and contribute to the furthering of Climate Science.

This process has allowed us to build the knowledge and context required to fully describe the ESGF CMIP6 datasets and make them accessible via the Eratos platform.

When describing datasets, it is important to build out the base ontology so that as much relevant information as possible is available to potential users. This provides a more complete understanding of the dataset they are dealing with in an efficient and effective manner.

As we described, there are many disparate resources that describe the CMIP6 core labels used in the datasets, which would take an inexperienced user an enormous amount of time and effort to find and understand. By creating a high context ontology in Eratos, we are able to provide that information to users, ready to use and in one location. The end result is a much more comprehensive and efficient understanding of the desired data and therefore a greatly improved user experience when utilising the datasets, as well as expediting the user's journey into analysis and solutions.

The process so far has been an exciting challenge and we’re only just getting started. In the future, we plan on moving the CMIP6 activities and ‘mip eras’ into Eratos collections and creating unique linking for the source types, which is still under investigation. This will be another step forward in creating even greater context and knowledge surrounding the CMIP6 datasets, and possibly even link to the earlier CMIP projects.

Through this exercise, we found that the CMIP6 variable export was missing 66 variables that are used throughout the CMIP6 datasets, and so we’ll continue researching to bring context to those missing variables and possible units. However, now that the majority of the initial ontology has been created on Eratos, we are ready to make the CMIP6 datasets accessible for preliminary use, which will link back to all of the underlying information and data.

To experiment with the CMIP6 ontology via Eratos please 

. Or if you already have an account and want to suggest other datasets or models that would be useful to our Community please let our support team know.

Eratos recently mapped CMIP6’s ontology to Eratos, thereby making the access and use of CMIP6 climate models simpler and easier in order to provide a one-stop shop for our climate science community to access global climate models.

Cover

World Climate Research Programme CMIP6 Controlled Vocabularies Documents

CMIP6 Specific References

Guide for the Use of the International System of Units (SI)

International System of Units (SI) Catalogue (explanations & tables)

Lists of SI base, derived and other units:

Ibiblio list of SI Derived & Compatible Units

US National Institute of Standards and Technology

Unit References

NetCDF Climate and Forecast (CF) Metadata Conventions

Variable (Non-CMIP6) References

Making the World Climate Research Programme’s CMIP6 accessible via Eratos

Long before FCDI came into being, CSIRO was leading the way in climate science and projection modelling. FCDI has always had CSIRO as a key partner and right from the start, well before we finally got funded. Together, we work to improve climate decisions using better climate science.

Without any exaggeration, CSIRO is one of FCDI’s most important partners in furthering Australia’s contribution to global climate science and answering climate questions.

CSIRO spearhead much of the research in this space and FDCI is proud to fuel the engine of climate forecasting and modelling they use to peer into the future. In this edition, we’ll be exploring the origins and purpose of FCDI’s relationship to CSIRO and how through our partnership, public and private sector organisations are able to forecast the climate with more trustworthy tools and greater confidence than ever. 

Read our interview with John Clarke (Research Team Leader, CSIRO Climate Science Centre) and discover how FCDI and CSIRO work together to empower Australians with better climate science.

Iron Sharpens Iron – CSIRO’s partnership with FCDI

With CSIRO (Climate Science Centre) Research Team Leader, John Clarke.

Long before joining CSIRO’s Climate Science Centre, John was working in the television industry, lugging hefty, pre-digital (one imagines Beta-Max) video recording equipment around the wilderness of northern Queensland and NT.

While working in a conservation management role in the national parks service as part of his television career, John became discouraged about how the traditional conservation paradigms were struggling to adapt to the new climate change narrative.

And in a most extreme example of a divergent career change, John took off the Merrell’s and turned in the TV gear for a stack of books on data modelling. Specifically, climate data.

John now leads a team of researchers specialising in developing climate change projections. John and his team’s work focuses on the near future of the climate: decadal and multidecadal timescales. And their objective is to draw on multiple lines of the best quality evidence in the form of data, to generate answers to some of the most urgent climate questions being posed today.

Scaling from Continent-Sized to A Bit Bigger Than Vatican City

LWhen hearing the nomenclature surrounding John’s wheelhouse, Regional Projections, one might be fooled into thinking his focus was on areas the size of a local government area (LGA), or a district like Riverina, for instance. This is only partly right. 

"In that context, regional means anything smaller than the whole planet”, shared John. “So, a region could mean a whole country, like Australia, or at the opposite end of the scale, a small Pacific island like Niue, which is tiny”.

John’s work, he explained, is projecting into the future a variety of plausible climate scenarios expected to occur within a specific region.

“What we’re projecting is how the main state of the climate might change in the future”, John continued, “so when we’re trying to gain an understanding of that future, we use very sophisticated computer models, which are in their essence, very similar to weather forecast models. However, they are tuned in different ways to look at larger-scale systems over longer periods”.

Take greenhouse gas emissions, for example. “So we have to use a set of scenarios that isolate fossil fuel burning,” John says, “and we can then ask what happens if they continue at the current level, or if there is a reduction in greenhouse gas emissions?”

We quickly find that the weather metaphor runs dry for comparison when scaled up to continent level climate projections several decades into the future. Unlike the weather forecasts we’re all used to, explained John, the possible futures being investigated are both more complex, integrating many different types of data, and offering few opportunities for a feedback loop:

“To look into the future, one might predict it’s 30 degrees and sunny, but it might be 29 with patchy cloud when tomorrow comes. We can then look at those differences and use them to inform the model and tweak it accordingly. We’re not afforded that because we’re projecting decades into the future when it comes to climate projection.”

Asking complex climate questions using FAIR data

The ability to perform these kinds of feats of data science inevitably rests on the availability of evidence. That evidence is the growing pool of real-time (or close to real-time) data constantly uploading and refreshing itself from myriad data sources harvested from millions, perhaps even trillions, of observational instruments that generate petabytes of data.

When observations match with projections, particularly those from the 90s, we can be confident that the models are doing a good job.

“Our view on the best way of doing climate projections is to draw on as many sources of information as you can. That increases your confidence that collectively, all of the information used together has a much better chance of covering the range of possible futures, including the one that is going to happen”.

What makes this almost science-fiction sounding endeavour possible is the marriage of CSIRO’s climate modelling expertise with sources of FAIR Data. What is FAIR DATA?

“Findable, Accessible, Interoperable, and Reproducible, that's what the acronym stands for. Where we expend public money to produce data, it should be easy to find it should be easy to use, and we should set it up so that multiple people across multiple platforms can use it. And you ought to be able to find enough information that you could then yourself go off and make your version of it and get the same results. That's reproducible. And systems like the Federated Climate Data Initiative are right in that space. They're ticking all those boxes”.

“Where the Federated Climate Data Initiative comes in,” John explains, “Is by making a system that allows the open sharing of data that’s been produced by all the data modelling people around the world. It makes it much easier for everyone to draw on that data, wherever they are. Then, if users want to drill down deeper and look at a particular part of that plausible future, they can find the best data that best fits the kinds of questions they want to ask”.

Connecting the Climate Science Dots - a podcast from the FCDI

Get the inside scoop and hear how our amazing team is overcoming obstacles to create the FCDI with Connecting the Climate Science Dots.

Hosted by former SBS and ABC journalist, Sam Ikin, Connecting the Climate Science Dots is available wherever you get podcasts.

FCDI has always had CSIRO as a key partner and right from the start, well before we finally got funded. Together, we work to improve climate decisions using better climate science.

Cover Image

Celebrating Our CSIRO & FCDI Partnership

A new data platform will help industries better manage and use insights from sensing and spatial data for a more competitive edge in the domestic and international markets.

A collaboration between Australia’s national science agency 

 and Eratos will help software engineers and researchers develop more informed, effective and scalable digital solutions for owners and managers of natural and built world assets.

Steve Taitoko, Co-Founder and CEO of Eratos said the partnership responded to a growing market demand.

“More and more industries across Australia including agriculture, water supply and energy now rely on the constant analysis of large sets of data to not only pinpoint current trends, but to also develop a future-gazing outlook for potential issues in the years ahead to inform strategy,” Mr Taitoko said.

“This will be crucial to future-proofing a number of industries globally, and this new partnership will see CSIRO developed Senaps fully integrated into Eratos, and both organisations working together to make this a reality.”

Under the partnership, CSIRO’s data analytics platform 

 will be incorporated with Eratos’ platform to create a distributed data management technology with cloud based IoT and scientific workflow management.

The platform will integrate huge volumes of data from Internet of Things (IoT) sensors, satellite images, and weather projections to help create highly accurate predictions and simulations for better decision making. 

Application specific APIs, databases and authorisation models do not need to be built by the user, and the platform will be equipped with secure hosting and custom analytics automation.

This level of oversight for weather-dependent activities such as crop yields and anticipated rainfall patterns will aid industries like agriculture and water supply forecasting.

Chris Sharman, principal engineer at CSIRO, said with data-driven technologies fundamentally changing the economic landscape and extreme weather becoming more common, these industries must utilise data-led predictive insights to future-proof themselves.

“Seeing research and technology lead to the development of real-world solutions that can save businesses critical resources and enhance their productivity and longevity is something we strive to accomplish at CSIRO, and Eratos is the perfect partner for us to ensure Senaps reaches that point,” Mr Sharman said.

Senaps is already a sophisticated platform, having been in development for over five years and applied across industries including environmental governance, smart cities, and livestock management.

A collaboration between Australia’s national science agency CSIRO and Eratos will help software engineers and researchers develop more informed, effective and scalable digital solutions for owners and managers of natural and built world assets.

Reference

CSIRO, Eratos partner to help the world turn data into opportunity

CSIRO spearhead much of the research in this space, and FDCI is proud to fuel the engine of climate forecasting and modelling they use to peer into the future. In this edition, we’ll be exploring the origins and purpose of FCDI’s relationship to CSIRO and how, through our partnership, public and private sector organisations are able to forecast the climate with more trustworthy tools and greater confidence than ever.

In this episode, we speak with John Clarke, Research Team Leader, CSIRO Climate Science Centre, and FCDI Chief Investigator, Tom Remenyi to find out how FCDI and CSIRO work together to empower Australians with better climate science.

Connecting the climate science dots podcast episode 3 - Find out how FCDI and CSIRO work together to empower Australians with better climate science.

As iron sharpens iron

One of the most important jobs of the Federated Climate Data Initiative (FCDI) is to help existing data delivery platforms reach their full potential. By making large quantities of data available to them very quickly they can do what they do with far more efficiency. But how does it do that?


In this edition, we to give you some insight into what this project actually is. If we were to compare it to other climate-related projects currently underway, the FCDI is a small fish in a pond full of sea monsters. But, its significance is not related to its comparative size. It’s a hugely significant first step in allowing our entire climate science and services sector to collaborate on a national scale.


Paul Stegeman is one of the co-founders of Eratos and is the brains behind the build. Paul joins host Sam Ikin and Project Lead Tom Rememyi to unpack the nuts and bolts of Federated Climate Data Initiative.

Connecting the climate science dots podcast episode 2 - One of the most important jobs FCDI is to help existing data delivery platforms reach their full potential.

How does it work?

I hope you’ve had a great start to 2022. We are excited about what is in store for the Federated Climate Data Initiative. This will be the year when the project is rolled out completely and climate scientists will reap the benefits. This, of course, will have significant flow-on effects for other sectors.

In this edition, I’d like to give you some insight into what this project actually is. If we were to compare it to other climate-related projects currently underway, the FCDI is a small fish in a pond full of sea monsters. But, its significance is not related to its comparative size. It’s a hugely significant first step in allowing our entire climate science and services sector to collaborate on a national scale.

To help us get our heads around exactly how it all fits together, we’re going to call on the man at the coal face of the construction, Eratos CTO and co-founder, Paul Stegeman.

Existing platforms to be enhanced by the FCDI?

One of the most important jobs of the FCDI is to help existing data delivery platforms reach their full potential. By making large quantities of data available to them very quickly they can do what they do with far more efficiency. Some of the programs planned to be getting an FCDI upgrade are:


National Environmental Science Program 2 (NESP2)

This program essentially helps on the ground users deliver real results on the goals of their environmental projects. It’s a one-stop shop for scientific knowledge, decision tools and options for practical management to support. It basically allows environmental scientists to ask more specific questions about the data. Having quick and easy access to all of the country’s climate data is a huge advantage for this program. The FCDI could dramatically enhance the NESP2’s performance and the quality of the product it delivers to its users.


Farmers are among the most reliant on climate forecasts and seasonal predictions. They can mean the difference between a bumper crop and a bust year. The CSA is a tool designed specifically for farmers and the agricultural industry. Having quick and easy access to the latest climate data as soon as it’s available, makes the CSA’s forecasts and predictions more accurate. The FCDI could make that data available to our farmers so they can make the best decision for their crops and their environment.


The ACS is like Australia’s climate early warning system. It helps people to understand the threats posed by a changing climate and how to limit the damage it could cause; threats posed by natural hazards like floods, bushfires, drought or severe storms. The service uses the most up to date climate data to help its customers prevent, prepare and respond to incoming threats. Obviously, having large amounts of data available as quickly as possible is a top priority for the ACS. This is where the FCDI would come in, providing public-facing natural hazard information as it becomes available.

CCiA is aimed at top-level decision-makers, providing them with the climate information, projections tools and data to help them make better decisions for Australia’s future. For example, the CCiA is working with Australia's Electricity sector which is being increasingly impacted by the weather. The CCiA provides the sector with better climate and weather information to help build resilience in the power grid. The resources available through the CCiA website are currently being integrated into the FCDI. Essentially, we are helping Australians make better decisions by making more data available to them.

Eratos CTO Paul Stegeman in the spotlight

Paul Stegeman is one of the co-founders of Eratos and is the brains behind the build. Paul has an academic background in aerospace and computational physics from Monash University. He’s currently the Chief Technology Officer and one of the four co-founders of Eratos along with John Chung, Sasha McMonagle-Ihasz and Steve Taitoko (who we met in the last newsletter). He’s also the guy who’s overseeing the build of the FCDI.

“The FCDI is all about providing a single source of truth for all climate data in Australia,” he says. “Actually, not just climate data, but climate models. It allows scientists to onboard these data and the models that produce them, regardless of where it's sitting in the country as well as sharing access to that with other scientists or even industry.”

Paul says that one of the biggest issues with climate data is the sheer volume of it. “You don't generally want to have to move it… it can be quite difficult to give access to external scientists.”

“We have about one and a half petabytes worth of climate data, in total across just Australia, In terms of providing access to that data [one way] is to get a scientist to copy it. Moving petabytes of data takes a seriously long time, it's often generally easier to ship it. Amazon has this service which is literally a truck that allows them to ship petabytes of data sitting on the back of a Mack truck. Then they drive it to the next location, and they dump the data there. We solve that by not moving the data.”

Paul and his team allow scientists to access the subset of the data they need without moving the data. 

“Ninety-nine per cent of requests aren't going to be accessing the entire data set, but just a subset of that data. So why don't we pull out that subset? Or why don't we run the models alongside the data? So the [FCDI] allows scientists to run scripts where the data is and not have to worry about moving the data, and then they get the result back.”

“We've got these concepts called a primary node, which you can think of as a piece of cloud-managed software. Then we have the gateway nodes which form a peer to peer network. They can talk to each other but can also sit wherever the data is. So it doesn't matter where it's sitting on your desktop, whether it's sitting in a compute cluster, in the cloud, or even on an embedded device. The Gateway node sits there to act as a translator to the data sitting on that system regardless of the format.”

The FCDI sounds like a system that most of us won’t be affected by. You may think it’ll mostly be scientists and engineers in white lab coats, which to a certain degree is true, but Paul says the information produced as a direct result of the FCDI will affect almost everyone.

“Say we want to calculate over the next century, the number of days that a particular location is going to be above 40 degrees for a year on average; the scientists would access the Eratos system and connect to the FCDI [which] will provide them access to the data and any available underlying models.

“They basically go and ask ‘Okay give me all the climate data for surface temperature for a given location.’ The system then basically goes off and finds all the available data that fits that criteria”

There are dozens of existing data platforms all around the world holding on to high value information. The problem is, there is no easy way to search for the data you need that may exist in separate data platforms.


“If you're looking for climate data in the US, you might go to NOAA,”

“If you're looking for the weather data globally over the last year, you may go to use data from European agencies, or if your interest is historically over Australia you'd go to the Bureau of Meteorology. Then there are various other platforms like the Earth System Grid infrastructure. However, those platforms, more or less, have non-uniform ways of accessing the data or even querying the data. So you have to do the implementation for each of them separately."

“There are some standards out there, however, those standards aren't uniformly accessible. That data is just sitting just on maybe, someone's desktop or on their computer infrastructure at their university. We provide a tool that allows them to effectively spin up the gateway node there, and then make all the data sitting on those systems available in a uniform manner.”

Get the inside scoop on the latest FCDI developments, here how our amazing team is overcoming obstacles and learn about some of the key players in our new podcast, Connecting the Climate Science Dots. Hosted by former SBS and ABC journalist, Sam Ikin, Connecting the Climate Science Dots is available wherever you get podcasts.

We are excited about what is in store for the Federated Climate Data Initiative. This will be the year when the project is rolled out completely and climate scientists will reap the benefits. This, of course, will have significant flow-on effects for other sectors.

The New Year of the Federated Climate Data Initiative

This is the story about the creation of one of the most significant climate initiatives in Australia’s history.


Over the next few month’s we’ll be following Dr Tom Remenyi and his team of dedicated scientists and engineers through the project currently known as the Federated Climate Data Initiative (FCDI).


In this episode, Tom takes us into the world of climate science where data is the currency and easy access to that data is essential to keep the information flowing.


We also meet Steve Taitoko, the CEO of Eratos, the tech company that's building the FCDI. Steve tells us how he had a serendipitous meeting with Tom a few years ago where they realised they were working at separate ends of the same project.


Jump into the first episode of Connecting the Climate Science Dots to find out how the data climate scientists are producing is being used to improve the future of the human race.

Connecting the climate science dots podcast episode 1 - This is the story about the creation of one of the most significant climate initiatives in Australia’s history.

Petabytes of Climate Data

As you probably already know, we are solving a complex problem. Around the country, hundreds of scientists produce petabytes (PB) of the most up-to-date data about the world’s climate. Our scientists and researchers are creating a wealth of high-quality, high-resolution climate model outputs. This data can help researchers, government, industry, or the general public make better decisions about Australia’s future.

The problem is that the quantity of information they produce is hard to share between researchers or other people who need it. Until now, the supply of this information has not been keeping up with its growing demand.



The solution is the Federated Climate Data Initiative (FCDI).



We are creating a single, united virtual collection from the enormous amounts of information currently stored around the country. The aim is to make the data much easier to find, access and use, dramatically enhancing the quality of future research and the services built this new knowledge, particularly around future risk.



Australia’s many regional climate projections are vital for understanding what impacts we must adapt to, for example, an increased bushfire risk. We can then determine which risks we can reduce, which could involve changing how we do things, such as what we grow and where we grow it. And, importantly, we can work out which impacts we want to avoid, for example, losing precious ecosystems if we allow climate change to increase by 2°C. By accelerating how we create products and services from these projections, change-makers will be able to act on the findings sooner.

Although the Federated Climate Data Initiative has been in the planning since 2018, as of April 2021, the Department of Industry, Science, Energy and Resources Disaster Management and Risk Reduction Fund has officially funded it with initial seed funding of $500,000. Development of contractual arrangements with partners is ongoing.

Since April, we have been busy setting up data access nodes across the country. We have installed nodes in Tasmania, Western Australia, Queensland and Victoria. They are steadily adding to the data available through the network.

We are building nodes in South Australia and within the National Computing Infrastructure are well underway. We are also working with the Australian Climate Service, the Climate Services for Agriculture Program and the National Environmental Science Program on how the FCDI can support them best. We will keep you updated on these developments through future newsletters.

All state and territory governments have now joined the project. The CSIRO, the Bureau of Meteorology and the Department of Home Affairs are also contributors. We’ve also welcomed some of Australia’s leading universities to the team including:

And welcoming our new implementation partner, Eratos, who is helping the project come to life.

If you’d like to part of this historic project, we would love to hear from you. To get involved or if you want to promote complimentary activities through our newsletter, please contact the Chief Investigator, 

In each issue of the newsletter, we will put the spotlight on each team contributing to this important collaboration.

The first spotlight is on the newest player in this field, Eratos, with CEO Steve Taitoko Eratos is one of the key players in the FCDI. The Melbourne based startup is responsible for building the infrastructure that the FCDI is based on. Tom Remenyi met with Steve Taitoko for the first time about two years ago, and they immediately knew they needed to work together to make the project happen.

What does Eratos hope to achieve through the FCDI?

Steve describes Eratos as a company “that’s looking to change how we make decisions about the world we live in.” They do that by creating the technology to allow people to make decisions from data that hasn’t been available before, data based on real-time information about the world we live in.



“Our purpose is how we can help humans survive on this ever increasingly heating rock that’s hurtling through space,” Steve says. “We can use a lot more information to create shared and collaborative knowledge to make better decisions about the world we live in. Decisions like what we plant in our soils, how we build our cities, how we can ensure that humans and our ecosystems survive within a changing climate, how we adapt to more extreme climate events that are going to be happening.”

Steve says that the Federated Climate Data Initiative and Eratos fit together like two pieces of a puzzle. “I was going to Hobart for a couple of days holiday, and I organised some meetings while I was down there. One of those meetings happened to be with Dr Tom Remenyi.”



“He showed me a diagram of an ARC linkage proposal… and I said to him, that diagram you’ve just drawn, that’s what we’re building, so don’t worry about that part; we will take care of that.” And through that meeting, a serendipitous partnership was born. Tom would focus on climate science, and Eratos would support him with the platform build. “That’s how it all started,” Steve says. “It was really random, just a random meeting.”



To hear more from Steve Taitoko and other key players in the project; keep an eye out for our new podcast. We will keep you updated on that. 

We are solving a complex problem. Our scientists and researchers are creating a wealth of high-quality, high-resolution climate model outputs. This data can help researchers, government, industry, or the general public make better decisions about the future.

Federated Climate Data Initiative Newsletter

People regularly ask what the meaning of Eratos is. In short, we named the company after 

, the Greek philosopher, mathematician, and scholar who lived around 200 BC. Among his many important achievements, he was the chief librarian at the Great Library of Alexandria, and it was at that institution that the earliest recorded examples of metadata can be found.

Metadata simply put, is data about data. It was applied to scrolls at the library by attaching small tags containing the title, subject and author, allowing library users to assume the content, without having to unroll each scroll and so they could be returned to their rightful location after use. Metadata today still apply that simple principle, but with a little more detail.

The use of metadata is not just a convenient ‘nice to have’, it is a ‘must-have’, particularly as we design more advanced analytics relying upon machine-to-machine communications. Mixing structured and unstructured data into automated analytic processes, mandates rapid access to data about the data, to retrieve precise, relevant and quality data.

, we discussed the power of the FAIR principles and how they are foundational in the design of Eratos. The key to achieving a data system based on FAIR principles is robust metadata. In this context:

Metadata makes data findable by allowing search on the elements of metadata that tell the story of the data.

Metadata makes data accessible by creating elements within metadata that provide access control and permissions management.

Metadata makes data interoperable through the standardisation of the metadata’s vocabularies and formats.

Metadata makes data reusable by providing rich contextual provenance about the data’s origins, use, licence, and governance.

In making data findable and accessible, the task is relatively straight forward, relying on the addition of descriptive elements to metadata. However, making data interoperable and reusable requires a high level of coordination to ensure metadata is created consistently, regardless of how it was generated or by who. To ensure standardisation, frameworks called schemas are required.

A schema provides the structure of a metadata collection to a specific domain, such as published research. They establish a set of rules that define required information, describing the semantics and relationships of the elements, for example 

 (DOIs) which are persistent interoperable identifiers for use on digital networks, adopted by scholars as a means to describe published research.

Much effort has been made to create standard schemas through international validation by organisations such as the International Standards Organisation. One of the most commonly used standards is the 

 (DCMI). Used often on webpages, it is simple and generic with just 15 elements, containing information such as title, date, type, format, creator, and rights.

Eratos utilises standard schemas to ensure metadata across all fields of cyber-physical data is interoperable and reusable. As they are machine-readable, schemas provide scalability, particularly as we move to more data generated and stored at the edge and are a vital component as we build the world’s semantic web of cyber-physical data.

Without standardised schemas and metadata, it will be impossible to extract needles from a rapidly expanding data haystack at speed and scale. Thankfully, the groundwork laid by our academic community allows us to bring robust and uniform metadata into the mainstream and for that, we have better data.

Metadata simply put, is data about data. The use of metadata is not just a convenient ‘nice to have’, it is a ‘must-have’, particularly as we design more advanced analytics relying upon machine-to-machine communications.

Better Data With Metadata

Data is generated and captured in every aspect of our daily lives these days. As individuals, the phones in our pockets, watches on our wrists, our televisions, and even the cars we drive. On the industrial scale, we have the burgeoning Internet of Things (IoT), with myriad sensors deployed on everything from 

This data is being generated from what is broadly called, cyber-physical systems (CPS), the intersection of the physical world with digital devices, sensors, computation, control and networking.

Like the internet connected people to information, these CPS are connecting information to the physical world. CPS are what underpins our transition to the 

Founding executive editor, Kevin Kelly wrote in 

 of a world where “someday soon, every place and thing in the real world—every street, lamppost, building, and room—will have its full-size digital twin in the mirrorworld.”

This mirrorworld “will reflect not just what something looks like but its context, meaning, and function. We will interact with it, manipulate it, and experience it like we do the real world.”

Technologies such as digital twins will form the basis of the mirrorworld, creating a living 3D metaverse that mimic the real-world environment. Loaded with sensors streaming data from everywhere, these digital avatars will provide infinite analytical possibilities, allowing humans and machines to diagnose, optimise, and predict in ways never before dreamt of.

Much of the data generated by CPS has never existed before, providing many exciting opportunities to gain fresh insights about our natural and built worlds. However, transforming data at the scale required comes with many challenges.

Of most concern is our inability to cope with the data flowing from CPS. A typical data scientist will lay awake at night worried about the three V’s: Volume (amount of data); Velocity (the speed at which data is generated); and, Variability (differences in data types, quality, programming languages). With the onslaught of data coming down the pipe from CPS, the size of problems caused by the three V’s are beyond most data scientists’ capability to control.

It seems incongruent to say that the existence of CPS is the culmination of centuries worth of research and technological innovation, yet they appear to have snuck up on us, given our unpreparedness to cope with the data explosion they have caused .

Fortunately, there are many researchers, data scientists, and organisations collaborating to harness CPS to generate quantum leaps in the way in which we interact with our natural and built worlds.

Take for example the groundbreaking work undertaken by Australia’s preeminent research organisation the CSIRO. Within their cyber-physical systems unit, they are 

 to better understand what factors are affecting colonies and pollination to reverse the unprecedented decline in bee populations globally.

One of the largest scale CPS programs in the world is 

. Along with digitising all government services, the program is focussed on creating interconnected systems for urban living, mobility, and health. For example, town planning is linked to transport design to plan for the seamless integration of autonomous vehicles into their society.

We believe in the potential that CPS has to positively reshape our world, and are committed to solving the many data problems inherent in translating raw sensor data into meaningful, actionable insights about our natural and built worlds.

The idea of a mirrorworld is interesting, but right now, we’re not quite sure what’s looking back at us. If we have anything to do with it, that’s about to change.

Like the internet connected people to information, Cyber-physical systems are connecting information to the physical world. They underpin our transition to the 4th Industrial Revolution.

Scientific advances, real world benefits

Journal entries