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Clean Technology Meets Data Science: News and Articles
News From The IPCC - Data Science And A Changing Climate Part II
Last time, we looked at how models and data science are used in measuring, monitoring, predicting and responding to a changing climate in the latest IPCC reports. Today, let's look at the results from the reports.
First, we've now reached an average warming level of 1.10C [0.950C - 1.200C] compared to pre-industrial levels (1850-1900). This result is based on satellite and sensor observations taken from the land and the oceans. Further, when compared to paleoclimate data (for e.g. data from ice core samples existing millions of years ago), the key indicators of the climate system are increasingly at levels that have not been seen for centuries and are changing at rates that are unprecedented for the last 2000 years. Also, several studies have shown that the ocean absorbed a significant amount of heat between 1998-2012, a process that resulted in a smaller rate of increase in land surface temperatures. However, this effect appears to be temporary, with strong warming seen since 2012 and with the past 5 years (2016-2020) being the hottest 5 years since 1850.
Second, there have been significant improvements in our understanding of different drivers of the climate system (clouds, the water cycle, aerosols) and their interactions. This, in turn, means that model predictions have become more certain and we are better able to estimate the extent to which current events are influenced by climate change as well as potential impacts in the future. The impacts in the future include estimating the extent of sea level rise, impacts on air quality and the water cycle, ice loss from glaciers and the behavior of land and ocean carbon sinks. Additionally, as we've improved our understanding the the processes, we're seeing greater agreement between the different methods used to monitor climate change - direct measurements, regional models and paleoclimate evidence.
Third, we're already seeing significant, widespread impacts from climate change. The extent of sea ice in the Arctic is lower than seen the last 1000 years; sea levels have risen to a degree not seen in the last 3000 years; glaciers are retreating to levels not seen in the last 2000 years; and ocean acidification is at the most in the last 26,000 years. Additionally, we can now connect many extreme events to climate change as a result of more observational data, paleoclimate reconstructions, higher resolution models, and new analytical techniques. Research suggests that increases in the number of hurricanes and heat waves can be attributed to climate change. Events such as the extreme heat seen in the Pacific Northwest of the United States and Canada, the extreme bushfires in Australia, and floods in Germany are unlikely to have occurred without climate change.
The results from the first and second reports are based primarily on observational data and results from model simulations of the climate system. The degree of uncertainty and the range of the results are usually a function of the resolution of the data and the models - i.e how close are the sensors and the data spatially and how large are the model time periods. In other words, are we able to get results at a city level or state level? Are we able to see the results of the model simulations every 2 years, every 5 years or every 10 years. How sensitive are the models to the different processes?
Over the last 30 years, we've significantly improved our understanding of the different processes that govern the Earth and our climate system. As we follow the progress of scientific research over the past 30 years and the last 5 IPCC reports, we can see that there's greater understanding of how processes like clouds, the water cycle and aerosols interact with each other. Recently we've seen research that tracks the behavior of carbon sinks over land and water (for e.g. the Amazon rain forest) as the climate changes. Putting all this research together means that gaps in our understanding of the climate system have decreased, and we've been able to reconcile data and model results to a large extent. Or, in other words - the list of unknown unknowns has decreased!
Additionally, over the last 10 years, we've had access to better computing resources so that the climate models can be run at smaller spatial scales and over shorter time periods. Just as an example, this means that we can now use our climate models to predict how the climate in an area in a state will change over a 5 year time period - instead of only being able to predict state level changes over 10 years.
And that brings up the results that are related to the impact human actions and policies have on the climate and Earth systems.
The fourth set of results from the IPCC reports, especially the third report that was released earlier this month, are related to current and future government policies, the likelihood that these policies and behaviors will mitigate the worst effects of climate change and what needs to be done in the near future to ensure that our planet remains habitable for our species.
These results are based on scenario analyses. Different policies are tested in the climate models that incorporate the best available science to date, the results from these policies on the concentrations of greenhouse gases and other pollutants in the atmosphere are estimated, and the models are simulated at time steps of 5 years for the next 50-100 years to determine what the planet will look like at year 2050, year 2100 and so on.
The first and second reports have shown that with every fraction of a degree increase in global temperatures, the impacts of climate change will intensify. The long-term goal of keeping the planet habitable was initially set at 20C in the early 1990s. However, as the science progressed, we discovered that the impacts of climate change were increasing faster than expected and that we are likely to see significant disturbances to human civilization even earlier. The Paris agreement of 2015 that was signed by 196 countries set an ambitious goal of keeping global temperature rise at 1.50C and doing all possible to minimize and reduce greenhouse gas emissions.
So what's the current status of these policies?
The good news is that in the past decade the rate at which greenhouse gas (GHG) emissions have been rising has slowed from 2.1% in 2000-2009 to 1.3% in 2010-2019. The bad news is that GHGs have continued to rise, which means that meeting our goals of keeping the temperature rise at 1.50C have become increasingly difficult. In all the modeled scenarios, we need to reach our peak of GHG emissions by 2025 and halve the total emissions by 2030 in order to meet our goal of 1.50C. Here, it's important to note that the state of the art models can only be simulated in time-steps of 5 years. So, the best estimate is that we need to halve emissions by 2030 - when we need to peak is still uncertain. We may have already crossed the threshold of emissions in 2022, or we may still have a few more years - but either way, we need to be making a large shift in the way in which we live in order to have a habitable planet for our species.
It's also important to note that even if we overshoot the target of 1.50C, that doesn't automatically mean that we will immediately start living on an inhospitable planet. Similarly to a frog that's put in slowly warming water, we will continue to see unprecedented changes to the planet - more wildfires, more drought, more floods, more ecosystems and species lost, less food production and more disease and pandemics. The wild card here is that we are still discovering the knobs and feedback systems in our planetary processes. There may still be a point at which the climate changes from our current system into something that was there in the past (no Arctic sea ice and a much smaller Antarctica), but scientists are still trying to figure that out. Our best hope at this point is to minimize the changes to the planet so that we don't tip over into a new, inhospitable climate and do all that we can to reverse the damage and heal our planet.
Our best science to date indicates that the path we're on is unsustainable. Even if all the countries in the Paris agreement meet their pledges, we are still likely to overshoot the 1.50C target. Building any additional fossil fuel infrastructure would have the same result and we need to transform our economic and social systems within the next 5 years to avoid the worst impacts on the planet and our civilization.
The transformations include rapidly scaling up renewable energy systems, transforming cities and buildings to incorporate more green infrastructure and alternative transportation systems, including more decentralized energy production and transmission and changing our food and consumption patterns to more local, plant-based and circular systems.
The good news is that the transformations are not only possible, but they are likely to have several critical co-benefits for sustainable development. Conserving natural landscapes, for example, can support nearby households' livelihoods, bolster food and water security, and protect biodiversity. However, in order to be able to produce win-win situations, we need to ensure that our decisions around these transformations are transparent, inclusive and sensitive to the needs of local communities.
One of the new additions to the IPCC report was an examination of consumption and reducing GHG emissions through reducing consumption. Research showed that households with incomes in the top 10% worldwide are responsible for 36-45% of global GHG emissions, so transforming and reducing consumption in these households could result in reducing GHG emissions by 40%-70% by 2050 compared to current policies. For many in wealthier countries and high income households in other countries, this means that decisions such as walking over driving, shifting to plant-based diets, cutting food waste and using energy more efficiently could make a huge difference to our planet.
We have a small window in which to reverse our current trajectory and ensure that our beautiful blue dot stays habitable for us - so let's get to it!
Moonshots, Models, IoT and Machine Learning in Agriculture
What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They're all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems? Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively - scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
Machine Learning, AI and Climate
As the impacts of climate change on the planet become clearer, scientists and professionals in climate science are looking at the latest tools and technologies in AI and machine learning to help understand and mitigate the effects. At the same time, career opportunities in the field are growing and we're seeing increasing numbers of students and early career professionals interested in developing and using their skills in ways that can help the planet. So, when and where can machine learning and AI be used in climate science? And what are the pitfalls? If you're working in environmental and earth sciences, you probably already have a pretty big toolbox that has been developed over several decades! It consists of standard statistical techniques including spatial and temporal statistics, a range of physics-based or process based models, and several data collection and data integration technologies at different scales. What can machine learning add to this? Does it replace all the o
Suitcases and pipes: Making machine learning work for clean water Part II
Last time we looked at how machine learning can help water utilities manage their maintenance and operations efforts - especially when dealing with hard-to-reach parts of the water system like buried water pipes. Today, let's talk about how machine learning is being used in developing new technologies and building prototypes for decentralized, small-scale systems. Desalination has been studied and deployed at scale for several years now. As different parts of the planet face increasing water stress, desalination is being evaluated as one of several potential solutions - together with water conservation and recycled water. In the Middle East of course, large-scale desalination plants have been in operation for several decades, with Israel being one of the countries at the forefront of developing and implementing the technology. Large-scale desalination plants have the advantage of scale - you can build a single system and then connect it to your existing water network. Sufficient data a
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