Weather and Climate @ Reading

By: Dr. Harriet Turner

The solar wind is a constant stream of charged particles that flows from the Sun and fills the solar system. It is an important aspect of space weather, which is the term we use to describe the changing conditions in near-Earth space. Space weather can lead to impacts on Earth, such as problems with radio communications, damage to spacecraft, and it can harm the health of astronauts and people on high-altitude flights. Severe space weather is driven by coronal mass ejections (CMEs), which are transient eruptions of material and magnetic field from the Sun. They propagate through the solar wind, with its conditions affecting CME speed and arrival time at Earth. The solar wind itself can also be a source of recurring space weather, due to its structure of fast and slow streams that rotate as the Sun rotates.

The current method of solar wind forecasting uses observations of the solar magnetic field to initialise a model of the Sun’s outer atmosphere. The outer boundary of the coronal model can then be used as the inner boundary of a model of the solar system that propagates the solar wind out to Earth and beyond, known as a heliospheric model. After the initial magnetic field observations, the modelling system contains no further observational constraints, limiting forecast accuracy. Data assimilation (DA) is a technique that combines model output with observations of a system to find an optimum estimation of reality. It has been used extensively in terrestrial weather forecasting and has led to large forecast improvements, but it has been underused in space weather forecasting. Here, at the University of Reading, we have developed a novel solar wind DA scheme (named Burger Radius Variational Data Assimilation or BRaVDA) that uses in-situ observations of solar wind speed to update the inner boundary of the heliospheric model.

Figure 1. Schematic of the BRaVDA scheme. This is a view looking down onto the ecliptic plane, above the north pole of the Sun. Earth is the black circle and its orbital radius is shown in the black line. An observation is taken at the red cross (a), and this can be mapped back to the inner boundary at some time in the past (b). The inner boundary can be updated with this information, and it can be propagated back outwards (c), leading to an improved solar wind state. This can then be run forwards to produce a forecast (d).

The observations are taken from spacecraft located on similar orbits to Earth. They contain information about the conditions close to the Sun at some time in the past, and this can be used to improve the inner boundary of the heliospheric model, as shown in Figure 1. Previous work (e.g. Turner 2022, Turner 2023) has shown that BRaVDA is effective in improving the inner boundary of the heliospheric model when using the Magnetohydrodynamics Around a Sphere (MAS) coronal model, which is used primarily for research purposes due to its long and readily available archive. In an operational context, the Wang-Sheeley-Arge (WSA) model is more widely used, as it is updated daily. It is used by the UK Met Office Space Weather Operations Centre (MOSWOC) and by the National Oceanic and Atmospheric Administration Space Weather Prediction Centre (NOAA SWPC).

I have recently been working on using the WSA model with BRaVDA. Due to its use within government organisations, the data is not readily available, so we were limited to analysis of 2020 only. For the first half of the year, the WSA output was producing anomalously high solar wind speeds and therefore the solar wind forecast at Earth was overpredicting. The reason for this is not known; however, it provided an interesting opportunity to see whether DA could improve the forecast. As Figure 2 shows, the DA is providing a significant improvement in forecast error, reducing the mean absolute error (MAE) by approximately 50%. This shows that using DA in operational forecasting could not only provide important forecast improvements, but it could also help to correct systematic bias introduced by errors in the input conditions.

Figure 2. Variation of forecast mean absolute error (MAE) with forecast lead time. The black line shows the forecast driven using WSA, before any data assimilation has taken place. The red line shows the forecast after the DA has taken place.

This work is currently being written into a paper, which will hopefully be submitted soon. The study also shows the optimum way of processing the BRaVDA output for use in the heliospheric model used in the study. This would be required for use in any heliospheric model and sets a framework for how best to use the output from BRaVDA. The next study is to investigate the impact of using DA on CME speed and arrival time estimates at Earth. With an improved background solar wind, it is hoped that this could lead to more accurate CME forecasts.

References

Turner, H., Owens, M. J., Lang, M. S., Gonzi, S., & Riley, P. (2022). Quantifying the effect of ICME removal and observation age for in situ solar wind data assimilation. Space Weather, 20, e2022SW003109. https://doi.org/10.1029/2022SW003109.

Turner, H., Lang, M., Owens, M., Smith, A., Riley, P., Marsh, M., & Gonzi, S. (2023). Solar wind data assimilation in an operational context: Use of near-real-time data and the forecast value of an L5 monitor. Space Weather, 21, e2023SW003457. https://doi.org/10.1029/2023SW003457.

By: Prof. Richard Allan (Professor of Climate Science)

The exceptional global warmth of 2023 and 2024 generated much idle chit chat in Meteorological circles and following a summer Met department coffee room chinwag with Chris Merchant, an ill thought out plan was devised to solve all the world’s problems… after which we narrowed our focus slightly to investigate the role of Earth’s growing accumulation of heat on the record global temperatures and implications for ongoing climate change. The rebel professors decided to roll up their sleeves, recall basic python (and IDL, sorry) and make some top plots. Chris got all the press with part 1 so I’ll just have to settle for a blog on part 2.

There is more energy arriving than leaving the planet (an energy imbalance) which is driving a warming of climate. But this imbalance is growing – Earth’s rate of heating has doubled over the past 20 years as less sunlight is being reflected back to space by dimming clouds and melting ice over the oceans, adding to heat trapped by rising greenhouse gas levels (interestingly, although more outgoing infrared heat is trapped by rising greenhouse gas levels, this is offset by greater infrared heat loss to space as the surface and atmosphere warms). The source of the growing energy imbalance is illustrated in Figure 1 which shows the increase in net energy imbalance anomalies is mostly explained by increases in absorbed shortwave radiation over the ocean and the signal is mostly absent for clear-sky conditions.

Figure 1 – A graph showing increases in Earth’s net energy imbalance (red) since 2000 are mostly explained by increases in absorbed shortwave radiation (dark blue) over the ocean (light blue) but not for clear-sky conditions (dotted). See Allan & Merchant (2025) for details.

We analysed the satellite record going back to 1985 and compared with the ERA5 reanalysis, a model-observations hybrid. Heating of the planet reached record values in early 2023, equivalent to every person currently alive using SIXTY 2-kilowatt kettles to boil the ocean (including babies that may need supervision)! Although ERA5 can capture the month-to-month fluctuations in Earth’s energy imbalance, it does not pick out the increase, particularly since the mid-2010s (Fig. 2).

Figure 2 – Time series of seasonally corrected Earth energy imbalance where positive values show a heating of the planet and data is from CERES satellite measurements, the DEEP-C reconstruction and ERA5 reanalysis. See Allan and Merchant (2025) for details.

By analysing the growing difference between the satellite data and ERA5, could we find what was going on? Of course not, but there were some interesting signals across the globe, with hotspots over the vast expanses of low altitude stratocumulus clouds off the coast of California and Namibia where more sunlight was being absorbed. But the discrepancy seems to show up across much wider areas of the oceans in the satellite data (Fig. 3, left). And when only clear-sky scenes were analysed much of the differences were wiped clean suggesting that the root cause of Earth’s growing energy imbalance is explained by cloudy ocean regions soaking up more sunshine (see Fig. 1). In fact, the only remaining discrepancy for clear-sky conditions aside from some funny business in the Arctic is a suspicious blob over eastern China (Fig. 3, right). This is somewhat interesting as ERA5 used older estimates of aerosol particle pollution changes which assumed more dirty skies in this region than in reality as China acted to clean up its cities and industry. These land-based emissions of aerosols could be part of the story as they can blow over vast areas of the north Pacific ocean and affect the clouds and wind patterns across a wide area of the globe.

Figure 3 – Maps showing differences between CERES satellite data and the ERA5 reanalysis in estimates of changes in absorbed sunlight (left) and the same for clear-sky conditions (right). See Allan & Merchant (2025).

Another possible cause of the dimmer Earth is declining aerosol emissions from shipping, which has been surreptitiously cleaning up its act over many years, but particularly since 2020 when International Maritime Organisation limits on ship fuel emissions over the open ocean were added to earlier regulations on pollution within ports.  Aerosol haze can directly reflect sunlight back to space but its real superpower is acting as condensation seeds for cloud that cause water to be distributed across more numerous, smaller droplets, making clouds more reflective like a polished mirror. Taking away this artificial cleaning spray effect leaves the usual dusty mirror we tend to have in our house. So declining aerosol pollution, either from land based or ocean-based aerosol particle emissions, could be making our clouds dimmer again.

On the other hand, a warming ocean and the pattern of this warming can make the most reflective low altitude clouds break up, causing more sunlight to reach the sea surface, adding to the warming. Previous research has shown this to be a thing and clouds, along with increasing gaseous water vapour and diminishing ice coverage, are known to amplify the amount of climate change through feedback loops. Because there is only so much cleaner we can make our air, while clouds can continue amplifying climate change as long as greenhouse gas emissions continue to add heat, working out how much of the growing imbalance in Earth’s energy budget is due to cleaner air, cloud feedback loops or natural ocean fluctuations is critical to working out what sort of a hell hole future societies will be living in.

And that brings us to this somewhat surprising surge in global warmth up to the record global temperatures in 2023 and 2024. The general consensus is that the record global warmth was driven primarily by greenhouse gas emissions with an extra boost from the El Niño conditions that developed in 2023. A bright sun at the peak of its 11-year cycle only contributed marginally while the consensus is that the Hunga Tonga undersea volcanic eruption early in 2022 barely influenced global temperatures since heating from water vapour injected into the bone dry stratosphere was offset by cooling from sulfate aerosol particles also emitted. Effects from reduced Sahara dust, wildfires or other volcanoes may have influenced regional climate but were probably not important globally. Nevertheless, we sidestep these niggly issues by focusing on the heat budget alone.

It takes just over 4000 Joules of energy to heat up a kg of salty ocean water by a degree Celsius. So, can the extra energy flooding into the climate system explain the large 0.27^oC rise in ocean temperatures from 2022 to 2023, the amount of warming we’re more used to observing in a decade? We did a dull-as-dishwater accounting exercise, to estimate how much heat was used up warming the atmosphere and land or melting ice (Figure 4). We estimated this to account for a larger than normal proportion of the total heating (20%) but this still leaves a whopping 24,000,000,000,000,000,000,000 Joules of energy to simmer the oceans over a year. Further calculations suggest that even this is not enough to explain the annual warming so we propose two possibilities: either this heat is being focused on a narrower ocean layer (Fig. 4c) or the usual uptake of heat to deeper layers reverses (Fig. 4b) and instead bubbles up to return energy to the upper layers. They are probably both part of the story. It is known that the deeper ocean loses its breath and is less efficient at sequestering heat as the surface warms. Also, a return of heat from deeper ocean layers is consistent with the transition from an extended La Nina 2020-2022 (where heat builds below the ocean surface) to a moderate El Nino that developed in 2023 (when some of the stolen energy returns to the surface layers).

Figure 4 – Schematic of energy entering the ocean upper layers for (a) climatological conditions and plausible scenarios for the large warming period 2022-23 (b-c). See Allan & Merchant (2025) for details.

The planet is becoming dimmer in so many ways, and one of these seems to be caused by less shiny clouds over the ocean which is adding to the growing greenhouse effect to heat up our planet at ever increasing rates, accelerating climate change. The temporary help from ocean fluctuations will fade but inevitably pop up uninvited again sometime in the future to wreak havoc. But how much Earth’s heating rate increases will depends on whether less reflective clouds are a temporary response to declining particle pollution or a response to, and amplifier of, the ocean warming – this is a crucial outstanding question and one that affects the trajectory of climate change.  Current levels of global temperature suggest that surpassing the 1.5^oC threshold above pre-industrial conditions is now inevitable yet along with uncertain cloud effects underscores (in marker pen) how essential rapid and massive cuts in greenhouse gas emissions are for limiting further warming and associated impacts on societies and ecosystems.

Journal Article:

Allan RP & Merchant CJ (2025) Reconciling Earth’s growing energy imbalance with ocean warming, Environ. Res. Lett.20, 044002, https://doi.org/10.1088/1748-9326/adb448

By: Prof Sarah Dance (Professor of Data Assimilation)

Last week, on 25th February 2025, our colleagues at ECMWF (European Centre for Medium-range Weather Forecasts) took their deep-learning-based global weather forecasting system, known as the AIFS, into operational production, running alongside their physics-based numerical weather prediction system.  The AIFS outperforms state-of-the-art physics-based models for several measures of accuracy, and is computationally much faster to run. However, both systems currently share a need for initialization with a best estimate of the current state of the atmosphere, created using a process called data assimilation.  

Tens of millions of atmospheric observations are used in weather prediction every day, but observations alone cannot describe the weather at all points on the globe. To get a complete picture we use data assimilation to optimally combine the observations with information from a physics-based computer model, taking account of our confidence in each source of data.  The resulting analysis is used as a starting point for both physics-based and machine-learning-based weather forecasts. It is also important to note that reanalysis, using data assimilation with historic observations, is a key component of training data for machine learning forecasting systems.  

Figure: Schematic of the global observing system from the World Meteorological Organization https://community.wmo.int/en/observation-components-global-observing-system

In recent decades, steady improvements in global numerical weather prediction accuracy have been driven by enhancements to data assimilation methods and increasing volumes of observations assimilated (e.g., Bauer et al, 2015). However, new observations are expensive, with new satellites costing billions of pounds. Thus, investments in such systems are evidence-driven. To inform these financial decisions, data assimilation scientists have traditionally carried out quantitative experiments to estimate the impact of new observations on improving forecasts with physics-based numerical weather prediction systems (e.g., Hu et al, 2025).   

In the age of machine-learning forecasting, the connection between each observation-type and forecast accuracy is less transparent. It is not yet clear how to measure the impact of specific observations on machine learning training, nor initialization of machine learning models. This is a critical question to address to ensure continued improvement in forecast accuracy, and better resilience to hazardous weather events.  

References 

Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015). https://doi.org/10.1038/nature14956 

Hu, G., Dance, S.L., Fowler, A., Simonin, D., Waller, J., Auligne, T., et al. (2025) On methods for assessment of the value of observations in convection-permitting data assimilation and numerical weather forecasting. Quarterly Journal of the Royal Meteorological Society https://doi.org/10.1002/qj.4933 

By: Dr. Matthew Patterson

Figure 1 – A weather model representation of the North Pacific jet stream, with North America on the right hand side of the image. Credit: https://earth.nullschool.net

Wavy bands of fast flowing air, called jet streams, are some of the most recognisable features of the Earth’s atmospheric circulation (figure 1). They have a critical impact on weather in temperate regions by directing the flow of air and interacting with storm systems.  

Jet stream variability can also drive extreme weather. For example, a northward-shifted jet stream contributed to the extremely hot and dry European summer of 2022 (Patterson et al, 2024), while a weakened or shifted North Pacific jet stream reduces rainfall in California, increasing wildfire risk (Wahl et al 2019).   

Given the significant role of jet streams in our weather, it is important to understand how they might be changing with climate change. Even subtle shifts to the latitude of the mean jet streams, or changes to their speed, could have large impacts on extreme rainfall, with substantial implications for society. 

Under future scenarios of increasing greenhouse gases, climate models generally project a poleward shift of the jet streams, with some variation between regions and seasons (Ossó et al 2024). Broadly speaking, this is due to an increase in the temperature gradient between the equator and poles, which regulates the speed and latitude of the jets.  

However, understanding whether or not climate change has already affected jet streams is made challenging by their natural variability. Low frequency sea surface temperature variability resulting from changes to ocean circulation patterns can shift the jets on multi-decadal timescales. In fact, the way in which the Pacific jet changes will likely be affected by how climate change projects onto natural patterns of variability like El Niño Southern Oscillation. 

A recent paper by Keel et al (2024) showed that the wintertime North Pacific jet stream has shifted northwards since the beginning of the satellite-era in 1979.  This northward shift likely contributed to the drying trend which made the January 2025 wildfires in Los Angeles more likely. Keel et al (2024) suggested that this shift may bear the signature of anthropogenic climate change.  

I addressed this hypothesis and related questions in a new paper with my colleague, Chris O’Reilly (Patterson and O’Reilly, 2025). We wanted to know whether climate model simulations, forced with past variations of greenhouse gases, could capture this trend. These computer models are a climate scientist’s laboratory, and if the models could capture this jet trend we could use them to investigate the cause.  

Figure 2 – Trends in a winter (December-January-February) North Pacific jet stream index over the period 1979-2023, for an ensemble of climate models (histogram and frequency distribution) and two observation-based datasets (cross and triangle). Numbers show the percentile at which the observations lie relative to the models, i.e. they exceed all of the model trends.

Surprisingly, none of the 180 simulations we looked at could reproduce the magnitude of the North Pacific jet trend (figure 2). One reason for this is the differing behaviour of the tropical Pacific Ocean in the models compared with the real climate system. 

Figure 3 – Trends in sea surface temperatures (DJF, 1979-2023). The trend is shown for a) Observations (HadISST) b) the mean over trends in all CMIP6 model simulations. Hatching in a) indicates grid-points at which HadISST trends lie outside of the middle 95% of the CMIP6 ensemble.

Whilst most of the sea surface has warmed over the recent decades, the tropical Pacific has warmed little or even cooled in some seasons (figure 3a). In contrast climate models tend to show an even greater warming in this region, relative to the global average (figure 3b). It is unclear whether this discrepancy arises because the models aren’t responding properly to greenhouse gases in this region, or that this is just an expression of natural variability.  

In any case, we found that accounting for the differing trends could explain some but not all of the more northward jet shift in the real climate, compared to the models. It is possible that the models also don’t show enough jet stream variability on long timescales or do not respond correctly to greenhouse gas variations.  

So can we say that climate change is responsible? We extended the time series of jet stream variability back before 1979 using long datasets. The longer datasets indicate that while the jet has shifted northwards since 1979, it had shifted southward over the prior thirty years at a similar rate.  

This doesn’t mean that climate change wasn’t involved. However, it does suggest that the recent jet trend has not emerged from natural variability. It is clear that more work is required to understand the drivers of this recent northward trend in the North Pacific jet and the implications for future climate change in North America.  

References 

Keel, T., Brierley, C., Edwards, T. and Frame, T.H., 2024. Exploring uncertainty of trends in the North Pacific Jet position. Geophysical Research Letters, 51(16), p.e2024GL109500. Doi: https://doi.org/10.1029/2024GL109500

Patterson, M. and O’Reilly, C.H., 2025. Climate models struggle to simulate observed North Pacific jet trends, even accounting for tropical Pacific sea surface temperature trends. Geophysical Research Letters, 52(4), p.e2024GL113561. Doi: https://doi.org/10.1029/2024GL113561

Patterson, M., Befort, D.J., O’Reilly, C.H. and Weisheimer, A., 2024. Drivers of the ECMWF SEAS5 seasonal forecast for the hot and dry European summer of 2022. Quarterly Journal of the Royal Meteorological Society, 150(765), pp.4969-4986. Doi: https://doi.org/10.1002/qj.4851

Ossó, A., Bladé, I., Karpechko, A., Li, C., Maraun, D., Romppainen-Martius, O., Shaffrey, L., Voigt, A., Woollings, T. and Zappa, G., 2024. Advancing Our Understanding of Eddy-driven Jet Stream Responses to Climate Change–A Roadmap. Current Climate Change Reports, 11(1), p.2. Doi: https://doi.org/10.1007/s40641-024-00199-3

Wahl, E.R., Zorita, E., Trouet, V. and Taylor, A.H., 2019. Jet stream dynamics, hydroclimate, and fire in California from 1600 CE to present. Proceedings of the National Academy of Sciences, 116(12), pp.5393-5398. Doi: https://doi.org/10.1073/pnas.1815292116

By: Dr. Tanusri Chakraborty

As we move toward net-zero and low-carbon emissions, Hydrogen (H₂) is expected to play a crucial role as an alternative energy source. H₂ is considered a clean fuel, as it is produced through electrolysis, where water is split into H₂ and oxygen (O2). Its conversion to heat or power is both efficient and environmentally friendly. When combusted with O2, H₂ produces only water, without generating any pollutants. 

H₂ production is categorized based on the source and production method, often classified by colour codes: 

1. Green H₂ – Produced from 100% renewable sources (such as wind or solar energy) through electrolysis, resulting in a low carbon footprint. 

1. Blue H₂ – Derived from fossil fuels but with carbon capture and storage (CCS) technology to reduce emissions. 

1. Grey H₂ – Produced from fossil fuels without CCS, emitting significant CO₂ (one tonne of H₂ production can release up to 10 tonnes of carbon) ​(Dvoynikov et al., 2021)​.

These classifications help the energy industry differentiate H₂ types. The various methods of H₂ production, storage, and applications are illustrated in Figure 1. 

Figure 1. H₂ production routes, including renewables, fossil fuels, and nuclear, with H₂ being produced in power plants, pharmaceutical applications, synthetic fuels or their upgrades in transportation, ammonia synthesis, metal production, or chemical industry application​ (Osman et al., 2022)​.

H₂, being a small and highly diffusive molecule, can easily leak during production, storage, and distribution, which may reduce its climate benefits. When H₂ is released into the atmosphere, approximately 70%–80% is absorbed by the soil, while the remaining 20%–30% reacts with hydroxyl (OH) radicals. This oxidation process increases atmospheric concentrations of methane (CH₄), ozone (O₃), and water vapor (H₂O), leading to enhanced radiative forcing. 

A key consequence of H₂ oxidation is the depletion of OH radicals, which in turn extends the lifetime of CH₄, increasing its atmospheric abundance ​(Derwent et al., 2001)​. Figure 2 illustrates the oxidation process of H₂ in the atmosphere. 

Figure 2. The effects of H₂ oxidation in the atmosphere ​(Ocko & Hamburg, 2022)​.

Work done by​ N. J. Warwick et al. (2023) ​using the UKESM model by increasing H2 mixing ratios at the surface from 500 to 750, 1000, 1500, and 2000 ppb. The results showed a decrease in OH radical concentrations (Figure 3a), an increase in CH₄ lifetime (Figure 3b), and an increase in tropospheric O₃ concentrations (Figure 3c). These findings indicate that increased H₂ concentration could enhance radiative forcing (Figure 3d). 

Fig3. (a) mass-weighted tropospheric mean OH, (b) CH4 lifetime concerning OH, (c) tropospheric O3, and (d) increasing ERF in UKESM1. In panels (a)–(d), Black circle (BASE scenario), Blue circle (the CH4 LBC remains fixed at 2014 levels), Red circle (CH4 LBC is adjusted to account for the change in CH4 lifetime), Orange circle (changes in emissions of ozone precursors). Green circle (changes in emissions of CH4 and ozone precursors, as well as adjusted CH4 LBCs)​. See N. J. Warwick et al. (2023)​.

Global Warming Potential (GWP) is a key metric that compares the warming effect of different greenhouse gases relative to CO₂ over a specified period, typically 100 years (GWP100). Since H₂ is an indirect greenhouse gas, its GWP is influenced by its effects on CH₄, O₃, and stratospheric H₂O. However, the main uncertainty while calculating H₂ GWP came from the soil sink process in different models. A multimodal study with a homogeneous soil sink of 59 Tg yr−1 in all models estimated that GWP100 of H2 is 11.4 ± 2.8​ (Sand et al., 2023)​. A study by Weik et al. (2023) using the UKESM1 model with prescribed H₂ surface concentrations, estimated H₂ GWP as high as 12 ± 6. While H₂’s warming effects are potent, they are relatively short-lived compared to methane. Some of its effects occur within a decade after emission, but its influence on methane extends its climatic impact for roughly another decade​ (N. Warwick et al., 2022)​ . 

At Reading University, I am investigating the impact of H₂ emissions on atmospheric composition and climate using the UKESM model. We have conducted multiple 40-year model simulations under present and future H₂ and CH₄ concentration scenarios, isolating the contributions of effective radiative forcing (ERF) from O₃, aerosols, and stratospheric H₂O. Additionally, we decomposed each ERF component under clean-sky (no aerosol) and clear-sky (no cloud) conditions. 

This approach requires diagnostic calculations of top-of-atmosphere radiative fluxes under three conditions (​Ghan, 2013)​: whole-sky, clean-sky (neglecting aerosol scattering and absorption), and clear clean-sky (neglecting both cloud and aerosol interactions). Preliminary results suggest that clouds play a crucial role in modulating radiative forcing by altering aerosol properties. 

To further explore the complex interactions influencing CH₄, O₃, and H₂ evolution, we conducted pulse experiments in which a 1-month H₂ emission pulse was introduced globally and over specific regions. These experiments will help quantify the magnitude and timing of changes in H₂, CH₄, and O₃, improving our understanding of H₂’s indirect effects on atmospheric chemistry and climate. 

References  

Derwent, R. G., Collins, W. J., Johnson, C. E., & Stevenson, D. S. (2001). Transient behaviour of tropospheric ozone precursors in a global 3-D CTM and their indirect greenhouse effects. Climatic Change, 49(4), 463–487. https://doi.org/10.1023/A:1010648913655 

​Dvoynikov, M., Buslaev, G., Kunshin, A., Sidorov, D., Kraslawski, A., & Budovskaya, M. (2021). resources New Concepts of Hydrogen Production and Storage in Arctic Region. https://doi.org/10.3390/resources100 

​Ghan, S. J. (2013). Technical note: Estimating aerosol effects on cloud radiative forcing. Atmospheric Chemistry and Physics, 13(19), 9971–9974. https://doi.org/10.5194/acp-13-9971-2013 

​Ocko, I. B., & Hamburg, S. P. (2022). Climate consequences of hydrogen emissions. Atmospheric Chemistry and Physics, 22(14), 9349–9368. https://doi.org/10.5194/acp-22-9349-2022 

​Osman, A. I., Mehta, N., Elgarahy, A. M., Hefny, M., Al-Hinai, A., Al-Muhtaseb, A. H., & Rooney, D. W. (2022). Hydrogen production, storage, utilisation and environmental impacts: a review. In Environmental Chemistry Letters (Vol. 20, Issue 1, pp. 153–188). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s10311-021-01322-8 

​Sand, M., Skeie, R. B., Sandstad, M., Krishnan, S., Myhre, G., Bryant, H., Derwent, R., Hauglustaine, D., Paulot, F., Prather, M., & Stevenson, D. (2023). A multi-model assessment of the Global Warming Potential of hydrogen. Communications Earth and Environment, 4(1). https://doi.org/10.1038/s43247-023-00857-8 

​Warwick, N., Griffiths, P., Keeble, J., Archibald, A., Pyle, J., & Shine, K. (2022). Atmospheric implications of increased Hydrogen use. 

​Warwick, N. J., Archibald, A. T., Griffiths, P. T., Keeble, J., O’Connor, F. M., Pyle, J. A., & Shine, K. P. (2023). Atmospheric composition and climate impacts of a future hydrogen economy. Atmospheric Chemistry and Physics, 23(20), 13451–13467. https://doi.org/10.5194/acp-23-13451-2023 

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By: Dr. Salim Poovadiyil

Weather and climate model data play an increasingly vital role in assessing climate risks within energy system operations and planning. The reliability of these assessments heavily depends on the quality of the input meteorological data, particularly in accurately representing extreme events that challenge the resilience of energy systems. 

Climate models provide one of the most significant advantages in modern energy planning—the ability to generate large samples of data. These samples are critical for understanding long-term natural variability, such as interannual and interdecadal fluctuations, and for characterizing rare but impactful extreme weather events. As renewable energy becomes a cornerstone of Europe’s energy mix, understanding how climate change influences high-impact weather events is essential for ensuring the resilience of power systems. 

Rare events like low-likelihood, high-impact weather phenomena are notoriously difficult to capture with observational data or reanalysis-based datasets.  This is primarily due to the relatively short period of the historical record, spanning a few-to-several decades at most, depending on the dataset selected (Bloomfield et al., 2018; Kay et al., 2023; Wohland et al., 2019). In contrast, outputs from climate models can be used to create powerful simulations of recent ‘near-present’ and ‘decades-ahead future’ climate conditions containing many hundreds of relevant weather-years.  This potentially provides a much richer dataset to examine the characteristics of rare and extreme conditions, but require careful evaluation of the model’s performance to ensure the relevant meteorology is well-represented. 

An ongoing project at the University of Reading, in collaboration with the Electric Power Research Institute (EPRI), USA, seeks to examine the quality of ‘energy’ data produced using climate model output available on the Copernicus Climate Change Service (C3S). There, nationally-aggregated European wind power, solar power and demand are estimated from high-resolution EURO-CORDEX regional climate model outputs. The data include projections under multiple greenhouse gas emissions scenarios, offering insights that align with European energy initiatives like ENTSO’s Pan-European Climate Database (Bartok et al., 2019; Dubus et al., 2023). 

We analysed representation of Dunkelflaute events (periods of calm and cloudy weather typically associated with increased power supply stress) over Europe using wind and solar capacity factors from two tailored climate products: the recent “C3S-Energy” datasets and one of its predecessors, ECEM.  We examined the representation of these events in model-derived energy datasets. 

Our Preliminary findings reveal significant differences in how Dunkelflaute events are represented across the C3S-Energy and ECEM datasets. In particular, while the overall seasonal evolution of Dunkelflaute occurrence appears to be well represented (compared to their respective reanalyses), there are noticeable differences in winter-time Dunkelflaute frequency across many areas of Europe with the climate models typically simulating fewer Dunkelflautes in the northern part of the region and more frequent events in the south (potentially up to a few 10’s of percent depending on country and area).  These findings underscore the importance of cautious interpretation when utilizing climate model-derived energy datasets. While these datasets offer unprecedented opportunities for exploring climate risk in energy systems, careful validation and contextual understanding are necessary to ensure their effective application. 

Figure 1: Percentage difference in Dunkelflaute events (lasting at least 2 or more consecutive days) between C3S and ECEM datasets derived from EURO-CORDEX regional climate models (original GCMs: CNRM, EC-Earth, MPI, and IPSL). The percentage difference is calculated as (GCM - Reference Reanalysis Data), where ERA5 is the reference for C3S, and ERA-Interim is the reference for ECEM.

Conclusion: Opportunities and Cautions 

The integration of datasets like ECEM and C3S-Energy into energy system planning is a transformative step towards addressing climate risks. By leveraging high-resolution climate model outputs tailored for the energy sector, stakeholders can better prepare for renewable energy droughts and other climate-induced challenges. However, the observed discrepancies in the representation of Dunkelflaute events highlight the need for continuous improvement in climate modeling and rigorous validation against real-world data. 

As the energy sector continues to transition towards renewable sources, these tools will remain indispensable. Yet, their utility must be complemented by ongoing research, collaboration, and a commitment to refining the models that underpin our understanding of climate risks. 

References 

Bartok, B. et al. (2019). A climate projection dataset tailored for the European energy sector. Climate services. 16, p. 100138. https://doi.org/10.1016/j.cliser.2019.100138 

Bloomfield, H. et al. (2018). A critical assessment of the long-term changes in the wintertime surface Arctic Oscillation and Northern Hemisphere storminess in the ERA20C reanalysis. Environmental Research Letters. 13.9, p. 094004. https://doi.org/10.1088/1748-9326/aad5c5

Dubus, L. et al. (2023). C3S Energy: A climate service for the provision of power supply and demand indicators for Europe based on the ERA5 reanalysis and ENTSO-E data. Meteorological Applications. 30.5, e2145. https://doi.org/10.1002/met.2145

Kay, G. et al. (2023). Variability in North Sea wind energy and the potential for prolonged winter wind drought. Atmospheric Science Letters, e1158. https://doi.org/10.1002/asl.1158

Wohland, J. et al. (2019). Inconsistent wind speed trends in current twentieth century reanalyses. Journal of Geophysical Research: Atmospheres. 124.4, pp. 1931–1940. https://doi.org/10.1029/2018JD030083

By: Dr. Anna Denvil-Sommer

Mesoscale eddies, which are swirling, whirlpool-like motions, play a critical role in ocean circulation and the global energy budget. At scales of 10 to 300 km, these dynamic features transfer hydrographic properties (physical and chemical characteristics of seawater, such as temperature and salinity) and redistribute energy across spatial and temporal scales, influencing large-scale ocean dynamics and biogeochemical processes. Representing their effects in ocean models is vital for accurate long-term climate predictions, as mesoscale eddies impact sunlight reaching deeper ocean levels, ecosystem health, and climate feedbacks [Olbers et al., 2012]. 

However, achieving a balance between resolution and computational efficiency remains challenging. High-resolution ocean models, capable of resolving mesoscale eddies (so called eddy-permitting), are computationally expensive (CPU time and storage) and constrained by numerical stability requirements, such as high viscosity and dissipation, and the neglection of Reynolds constraints on eddy Reynolds stress [Zanna et al., 2017]. In their turn, coarse resolution models, used in climate and Earth System Models, fail to explicitly represent mesoscale processes. This underscores the need for innovative methods to improve their representation without excessive computational costs. 

Machine learning (ML), especially deep learning, offers a promising solution. By leveraging large datasets, ML reconstructs missing information across different spatial and temporal scales. For instance, processes invisible to satellites or unresolved by coarse models can now be modelled using ML techniques. A notable example is using deep neural networks to represent all subgrid atmospheric processes in a climate model and successively replace traditional subgrid parameterizations in a global general circulation model [Rasp et al., 2018]. 

One of the driving mechanisms for the emergence of mesoscales features in the ocean is the baroclinic instability (density-driven flow instability), especially in winter [Boccaletti, et al., 2007; Capet et al., 2008; Fox-Kemper and Ferrari, 2008; Mensa et al., 2013; Oiu et al., 2014; Sasaki et al., 2014]. This instability can be measured using the eddy buoyancy flux (u′b′), which quantifies correlations between 3D velocity and tracer anomalies. 

There several studies dedicate to this problem (Bolton & Zanna, 2019; Zanna & Bolton, 2020; Guillaumin & Zanna, 2021; Bodner et al, 2024). In our work we first concentrate on the vertical component of buoyancy flux w′b′. To address these challenges, we test a 3D Convolutional Neural Network (3DCNN) method to reconstruct w′b′ from large-scale ocean variables – temperature, salinity, and velocities. The model is trained on outputs from the eNATL60 simulation, a high-resolution regional configuration of NEMO (a general global model of ocean circulation), covering the North Atlantic with a horizontal resolution of 1/60°x1/60° and 300 vertical levels. This high-resolution dataset explicitly resolves mesoscale processes, providing an ideal foundation for training. The 3DCNN links coarse-resolution inputs to mesoscale fluxes. Testing involves averaging eNATL60 outputs to simulate various coarse resolutions, creating a robust training database.  

Figure 1. Vorticity filed of eNATL60 with the zoom on Gulf Stream region with high meso-scale activity. The grid on the zoomed plot is 1°x1° to show the meso-scale features that can be missed in course resilution model.

By improving mesoscale process representation (Figure 1), this work paves the way for enhanced biogeochemical and physical modelling in climate simulations. Once validated in other ocean regions, the method will contribute significantly to the accuracy of coarse resolution global ocean and climate models. Keep an eye on the scientific literature next year for the outcomes of this project as it progresses. 

References

1. Boccaletti,  G., Ferrari, R.,  and Fox-Kemper, B.  Mixed layer instabilities and restrati cation, Journal of Physical Oceanography,  37(9):  2228-50, doi: 10.1175/JPO3101.1, 2007. 
2. Bodner, A., Balwada, D., Zanna, L. A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer, arXiv preprint arXiv:2312.06972, https://arxiv.org/abs/2312.06972, 2023. 
3. Bolton, T. and Zanna, L. Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sy., 11, 376–399, doi:  2019. 
4. Capet, X., Campos, E.J., and Paiva, M. Submesoscale activity over the Argentinian shelf, Geophysical Research Letters, 35,  2-6, doi:10.1029/2008GL034736, 2008. 
5. Fox-Kemper, B., and Ferrari, R. Parameterization of mixed layer eddies.  Part II: prognosis and impact, Journal of Physical Oceanography, 38, 1166-79, doi: 10.1175/2007JPO3788.1, 2008. 
6. Guillaumin, A. P. and Zanna, L. Stochastic-deep learning parameterization of ocean momentum forcing. Journal of Advances in Modeling Earth Systems, 13(9), e2021MS002534, doi:https://doi.org/10.1029/2021MS002534, 2021. 
7. Mensa,  J.A.,  Z  Garraffo, Z.,  Griffa, A., Ozgokmen, T.M., Haza, A., and Veneziani, M. Seasonality of the submesoscale dynamics in the Gulf Stream region, Ocean Dynamics, 63, 923-41, doi: https://doi.org/10.1007/s10236-013-0633-1, 2013. 
8. Olbers, D., Willebrand, J., Eden, C. Ocean Dynamics, Springer, Heidelberg, 2012. 
9. Rasp, S., Pritchard, M.S., and Gentine, P. Deep learning to represent subgrid processes in climate models, PNAS  115 (39), 9684-9689, https://doi.org/10.1073/pnas.1810286115, 2018. 
10. Sasaki, H.,  Klein,P., Qiu B., and Sasa, Yi.. Impact of oceanic scale interactions on the seasonal modulation of ocean dynamics by the atmosphere, Nature  Communications, 5, 5636 https://doi.org/10.1038/ncomms6636, 2014. 
11. Zanna, L. and Bolton, T.. Data-driven equation discovery of ocean mesoscale closures, Geophys. Res. Lett., 47, e2020GL088376, https://doi.org/10.1029/2020GL088376, 2020. 
12. Zanna, L., P. P. Mana, J. Anstey, T. David, and T. Bolton. Scale-aware deterministic and stochastic parametrizations of eddy-mean flow interaction, Ocean Modelling, 111, 66–80, https://doi.org/10.1016/j.ocemod.2017.01.004, 2017.

By: Prof. Andrew Charlton-Perez

As some of you may be aware, it’s been an exciting time recently for work on climate education and it’s something that the University has been increasingly involved in. I’ve taken the opportunity of writing the departmental blog this week to take a bit of a look back on the work we’ve been involved in and to hopefully get some of you enthused about joining in with this effort. Perhaps even through our new Time to Make a Difference policy. 

Hopefully it’s clear to everyone in the department just why this work matters so much. For too long, despite the efforts of many brilliant teachers, school education on climate change has been fragmented and inconsistent. This is reflected both in what young people say about their climate education – see the incredible Teach the Future campaign – and in the climate literacy of school leavers.  

As part of our work leading into COP26, a small team of colleagues at the University put significant effort into this topic and in developing a national climate education summit. Very much a lockdown project that got out of hand. We realised quickly through the summit that there was a huge desire for creative and practical thinking about how to improve climate education – and we developed a National Climate Education Action Plan (NCEAP) with partners including the RMetS. This work has been influential – for example informing the Department for Education (DfE) Sustainability and Climate Change strategy. The group that formed through the summit has continued to meet regularly with more than 70 organisations contributing to our continued work on improving climate change education. You can see, for example, our recent work on where climate change could be included within the curriculum.  

One significant idea that came out of the action plan, was that the education system needs the help of the expertise that sits within universities, government and the private sector to move forward. In particular, to meet the government aim of all education settings to have a climate action plan in place by next year, we need to mobilise this expertise quickly and effectively.  

From this idea, was born the Climate Ambassador scheme with the generous support of STEM Learning who provided their platform and tools to the programme for free. I’m hugely proud that this simple idea has now grown into one of the biggest climate education schemes in the world. Following the award of £2m in funding from the DfE in January we have been able expand the scheme significantly. Our Climate Ambassador consortium, which I am co-leading with Charlotte Bonner at EAUC, has established nine regional hubs at the Met Office and eight universities including here at Reading. Our brilliant regional hub manager, Gemma Bailey, has been leading the charge in the South-East and the project is now managed by Jessica Gardner. 

It’s been so much fun to work with colleagues old and new as we have been growing and developing the scheme. So far, we have recruited 720 ambassadors – a huge achievement and a significant volunteer team already. We need to go much further though and we need your help, could you sign up to help us? Find details of how to do so on the STEM Learning website. 

Ultimately though the success of the scheme and of our key partners at the National Education Nature Park, Sustainability Support for Education and Let’s Go Zero will be measured by our ability to make transformative change in climate and nature education across the education system. In September, to kick off this exciting year of climate and nature action in education we held a series of nine linked events across all of our regional hubs. I’ve included some photos below of this work, the enthusiasm and energy across those who attended was really infectious. The desire for change is out there among teachers and school and college leaders – but we will need to keep up the hard work to make this change a reality. 

What is exciting is that this hard work does seem to be paying off. In the last ten months, Climate Ambassadors have supported more than 500 nurseries, schools and colleges. Across the four programmes I mentioned above nearly 4,000 education settings, more than 15% of the total in England, are already involved. There is still of course a huge way to go, but in just a couple of years we have collectively generated a huge amount of momentum – on the way to the DfE vision of the UK leading the world for climate action in education.  

So what’s next? As I’m sure many of you will be aware, one of the first acts of the new government was to launch a review of curriculum and assessment across the education system, led by Prof. Becky Francis. The University, along with many other organisations, will be advocating for the review to put high-quality climate education at the heart of the reforms that follow. On Monday (4th Nov) NCEAP will be hosting an open meeting at which we hope many different organisations will share their ideas about how this goal might be achieved.   

It’s been hugely personally satisfying to be involved in this work over the last three years. Thank you to the hundreds of Climate Ambassador volunteers who have given their time so generously and readily. While it’s probably the hardest I’ve ever worked in my career, the satisfaction I get from the work we are doing as a small team at the University, as an engaged and dedicated consortium and across the community of organisations seeking to make real change happen is enormous. As Head of School, I hope that you as my colleagues are able to get the same amount of joy from your work! 

By: Prof. David Ferreira

Recently, a colleague pointed out that, in the Northern Hemisphere subtropics, the winds blow eastward around 30-40N (the jet stream), and westward around 20-10N (the trade winds), and these winds make the ocean spin in the clockwise direction. This is akin to thinking of the ocean as a solid plate rotating around an axis: if one pushes the plate in one direction on one side, and the opposite direction on the other side, the plate will spin.

Fig. 1: The observed Mean Ocean topography estimated from a combination of satellite measurements and ocean drifters from Maximenko et al. (2009). Data, figure, and further details can be found here https://apdrc.soest.hawaii.edu/projects/DOT/. The pink arrows represent the dominant winds, while the blue arrows show the directions of the ocean current averaged over the full depths of the ocean.

Fig. 1 shows the Dynamical Ocean Topography (DOT). The currents tend to flow along the contours of DOT. Focusing on the North Pacific, the figure highlights the most striking example of a subtropical gyre, with a clockwise motion between about 10 and 40N (but these gyres are found in all basins). Applying the solid plate reasoning to the southern hemisphere suggests that the gyres spin counterclockwise (which they do! See Fig. 1).  

A “correlation” analysis supports the solid plate explanation. All subtropical gyres spin in the direction imparted by the winds, and indeed on some level, this is a decent explanation of subtropical gyres.  However, the remark of my colleague provides us with an opportunity to emphasize why we bother with fluid dynamics, equations and idealized modelling. 

The solid plate reasoning hides a complex dynamic. When the wind blows over the ocean, the first effect is not to make it spin. The winds first create horizontal currents confined to the upper 50-100 m of the oceans. Somewhat unintuitively, these currents, called Ekman currents, are at right angle of the wind and to their right (in the Northern Hemisphere). The meeting of the Ekman currents around 25N creates a bulge of the ocean surface between the Jet stream and the trade winds, but also a descending motion w_e (water cannot accumulate in the surface layer forever). This is where we use a version of the Sverdrup balance: 

βV = f w_e

where f is the Coriolis parameter (Earth is spinning on itself), V is the north–south flow in the deep ocean and β represents the variations of the Coriolis parameter with latitude.

Fundamentally, the parameter β represents a geometrical effect: Earth is a sphere. If Earth was flat, β would be zero. A direction that particularly matters for the dynamics is that along the axis of rotation. The sphericity of Earth implies that, even in a flat bottom ocean, columns of water aligning with the axis of rotation are smaller near the pole than near the Equator (Fig. 2 a).

Fig. 2: Schematic showing the variations of the height of water column in the direction of the rotation axis. Case a) corresponds to a flat-bottom ocean where the depth of the ocean is the same everywhere in the direction of gravity. Cases b) and c) illustrate what happens when the bottom of the ocean is tilted in some region of the planet.

We can now interpret the Sverdrup balance (1). The wind pushes water downward into the ocean interior (w_e <0) making water columns taller. As a result, water columns must move southward where they can accommodate their new height. Indeed, we see in the real ocean (Fig. 1) that the southward motions of the ocean spread over the full width of the basin and is only broken in a very narrow region on the western side of the basin (in the Kuroshio current, the Gulf Stream, the Agulhas current etc.).

Obviously, we cannot experiment with the real ocean to test our understanding. However, we can do it in numerical models that solve more complex equations than Eq. (1) (where nonetheless we can afford some simplifications to make things easier).

This is illustrated in Fig. 3 using a square basin with a flat bottom. The ocean is assumed to be of uniform density. It is driven by eastward wind to the north of the basin and westward wind to the south, mimicking the real-world wind pattern. Fig. 3a shows that the circulation is southward throughout the basin, except in a very narrow current on the western boundary.

Fig. 3: Simulations with a numerical ocean model (the MITgcm, mitgcm.org). The pink arrows in the top left panel show the direction of the winds. The blue arrows indicate the direction of the currents. The upper row has a flat bottom ocean with (a) and without (b) beta effect. In the bottom row, the sea floor is tilted, shallower in the northern part of the basin (corresponds to Fig. 2c)) and shallower in the southern part of the basin (corresponds to Fig. 2b)

What happens if we set β to zero, i.e. make a flat Earth? This is shown Fig. 3b (same winds as in Fig. 3a): the circulation becomes circular around the center of the basin. The flow is southward over half of the basin, and northward over the other half, nothing like the real ocean.

The geometrical interpretation of β suggests that the shape of the ocean bottom could play a role here. Imagine that, in some region of the ocean, the sea floor is getting deeper as one moves northward. Thus, seen from the perspective on the sphere, water columns in that region have similar heights (Fig. 2b). The tilted bottom floor makes Earth “flatter” in this region. This effect is called the topographic β-effect. In this case, it partially cancels effect. For a given wind, columns need to travel southward faster than before to accommodate for the pilling up of water by the wind. So, the gyre must be stronger.

Conversely, if the ocean floor is getting shallower moving northward in some region (Fig. 2c), the height of water columns varies much more, the Earth is more “spherical”. The topographic β-effect reinforces the normal β-effect, and we predict that the gyre should be weaker than for a flat bottom ocean.

Again, we can test our predictions through numerical experiments. Fig. 3c) and d) show the circulation in our simplified geometry, but now with tilted bottoms. As predicted, the gyre in Fig. 3c (corresponding to the tilt in Fig. 2b) is stronger than with a flat bottom while in Fig. 3d (corresponding to the tilt in Fig. 2c) it is weaker than with a flat bottom.

The β-effect is therefore critical to explain the characteristic pattern of the subtropical gyres. Something we could not get from the “correlation” analysis. One could do much more with the theory. It is possible to make more analytical advances, which reveals that β also controls the width of the narrow western boundary current (the smaller β, the wider the boundary current). Incidentally, the analytical solution can be used to test numerical models (and be happy that discretization, used in climate predictions, can be trusted). Retrospectively, it appears that the correlation analysis of wind patterns and gyres did not tell us much that was interesting.

The gyre dynamics discussed above was done decades ago and is pretty much textbook material nowadays, but the need to develop theory for new research remains. Recently, we explored the connection between Arctic sea ice decline and Ocean heat transport under climate change (Aylmer et al. 2024). Climates models that predict larger sea ice decreases in the future also have larger increases in ocean heat transport into the Arctic. This sounds intuitively OK, but is it what happens? Is it even quantitatively possible? That is, is the additional heat to the Arctic ocean large enough to explain the additional melting in these models? Less intuitive, models that predict more heat to the Arctic by the atmosphere tend to show relatively less sea ice retreat. These correlations have been noted for decades, but their interpretation has remained speculative. Aylmer et al. (2024) provides a theoretical framework that predicts, quantitatively, the behavior of the climate models, providing firm ground for interpretation.

Interestingly, this work started in 2019 with the development of an analytical model of climate by Dr Jake Aylmer during the first year of his PhD (published in Aylmer et al., 2020).

Yes, it takes time. As our field turns more and more toward statistical analysis, big data, and is pushed toward more applied, impactful research by funding agencies (with cascading effect on doctoral training programs), there is less and less room for this type of work.

As I am polishing this blog, I am listening to a seminar by David Karoly discussing Hoskins and Karoly (1981), a paper which had a massive impact in the field of meteorology and climate dynamics. This paper has been cited more than 3000 times and is still cited 100 times per year 40 years after publication! David Karoly pointed out that this work had major limitations and should be critically evaluated before being cited. Hoskins and Karoly (1981) is grounded in mathematics, geophysical fluid dynamics, and idealized experiments. One needs to be trained in such things to critically assess and carry on fundamental research in climate science. Unfortunately, the number of PhD students trained with these skills is gradually decreasing.

Correlation analysis is cool, but a bit of theory on top is cooler.

References:

Aylmer, J., D. Ferreira, and D. Feltham, 2020: Impacts of oceanic and atmospheric heat transports on sea-ice extent. J. Climate, 33, 7197–7215. https://doi.org/10.1175/JCLI-D-19-0761.1 

Aylmer, J., D. Ferreira, and D. Feltham, 2024: Ocean heat transport regulates Arctic sea ice loss. In press in Nature Communications.  https://doi.org/10.21203/rs.3.rs-3249087/v1 

Hoskins, B. J., and D. J. Karoly, 1981: The Steady Linear Response of a Spherical Atmosphere to Thermal and Orographic Forcing. J. Atmos. Sci., 38, 1179–1196, https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2. 

Maximenko, N., P. Niiler, M.-H. Rio, O. Melnichenko, L. Centurioni, D. Chambers, V. Zlotnicki, and B. Galperin, 2009: Mean dynamic topography of the ocean derived from satellite and drifting buoy data using three different techniques. J. Atmos. Oceanic Tech., 26 (9), 1910-1919. DOI: https://doi.org/10.1175/2009JTECHO672.1 

By: Prof. Keith Haines and Samantha Petch

Atmospheric CO2 levels are rising every year, primarily due to human activities, Fig 1. However, the rate of this increase does vary significantly from year to year, not because of human emissions, but mostly due to changes in how much CO2 is absorbed by land ecosystems. 

Fig 1. Recent changes in Atmospheric CO2 concentrations. Interannual changes can just about be seen.

The land’s ability to absorb CO2 is determined by the balance between photosynthesis and respiration and is the least understood part of the global carbon cycle. This land carbon sink plays a crucial role in offsetting anthropogenic CO2 emissions, accounting for approximately 30 ±10% of these emissions each year (Friedlingstein et al., 2020). The controls on this land surface carbon sink are still poorly understood and would have a strong impact on future CO2 growth rates (CGR) if they were to change significantly.  There are notable increases in CGR during El Niño events and decreases during La Niña events (Keeling & Revelle, 1985) and there is also a widespread consensus that variations in tropical ecosystems exert the most significant influence on global CGR.  

Tropical temperatures have a strong positive correlation with CGR (Wang et al., 2013) and given that tropical temperatures are usually considered optimal for photosynthesis, any elevation in these temperatures under global warming could amplify the CGR. However it has recently been realised that water availability is also playing a really important role, see Fig 2.   

Figure 2 shows detrended interannual variations in the atmospheric CGR in black, and the total terrestrial water storage (TWS on a reversed axis) in blue, as measured from the GRACE satellite which detects water through mass redistributions over the Earth’s surface, (from Petch et al. 2024 following Humphreys et al. 2018).

Less water over land e.g. during the El Niño in 2015/6, goes with more rapid increases in atmospheric CO2 as the land surface absorption is reduced.  This range of uptake (up to 4GtC/yr) is really large and to understand this relationship better we really need to regionalise the surface response, see Fig 3.   

Figure 3 shows the regional interannual correlations in GRACE surface water storage and the global CGR (Petch et al. 2024).

Petch et al. (2024) found that the tropics alone can explain the entire global TWS-CGR relationship seen in Fig 2, with only minor offsetting contributions from the northern and southern hemisphere extratropics. Additionally, tropical forests, e.g. over the Amazon, were found to be the dominant land cover type governing this relationship, despite occupying only a relatively small portion of the land surface. While the negative correlations in Fig 3 (and other evidence) point to the importance of the tropics in the global relation seen in Fig 2, it would be important to have direct regional surface CO2 flux data to back this up.   

Having a verifiable way of comparing surface CO2 fluxes is of course of the highest scientific and political importance. Currently national anthropogenic CO2 emissions all rely on national inventories which are hard to verify or compare. CO2 in the atmosphere gets mixed relatively quickly, at least at the hemispheric level, and so only frequent reliable satellite based atmospheric measurements stand a chance of achieving this, e.g. ESA’s CO2M-(A,B,C) multi-satellite mission, launching 2025/6. In the meantime, less frequent satellite measurements e.g. GOSAT (2009 onwards) can be assimilated into atmospheric transport models which may then be used to infer surface sources and sinks which best match observed atmospheric CO2 concentrations. Given the importance of these estimates many teams produce such surface CO2 flux products, although many still rely on in situ measurements alone and show a lot of disagreement between regional results (Fig 4).

Figure 4 shows % contributions to global land carbon flux interannual variability over 2001-2023 from four atmospheric CO2 inverse model products. Note, the NISMON-CO2 map is depicted over a larger range due to higher contributions from tropical forests. While these products agree well with the global CGR from Fig 2, the regional agreements can be seen to be fairly poor, Petch et al. (2024).

We believe there is potential to bring in additional satellite-based information from the land surface such as the GRACE water data shown earlier, and perhaps other datasets such as ESA’s Climate Change Initiative Land surface temperature measurements (https://climate.esa.int/en/projects/land-surface-temperature/), to provide additional constraints to these atmospheric CO2 transport models and bring greater confidence to space-based estimates of regional CO2 fluxes.  This would help both improve understanding of ecosystem responses to climatic changes as well as provide better verification tools for monitoring anthropogenic emissions in a more verifiable way.  

References 

Friedlingstein, P., O’Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Olsen,  A., . . . Zaehle, S. (2020). Global carbon budget 2020. Earth Syst. Sci. Data,  12 , 3269–3340. https://doi.org/10.5194/essd-12-3269-2020 

Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., & Seneviratne, S. I. (2018). Sensitivity of atmospheric co2 growth rate to observed changes in terrestrial water storage. Nature, 560 , 628–631. https://doi.org/10.1038/s41586-018-0424-4 

Keeling, C. D., & Revelle, R. (1985). Effects of el nino/southern oscillation on the atmospheric content of carbon dioxide. Meteoritics, 20 , 437-450. https://doi.org/10.1093/nsr/nwab150 

Petch, S., Feng, L., Palmer, P. King, R. P., Quaife, T. & Haines, K.  (2024) Strong relation between atmospheric CO2 growth rate and terrestrial water storage in tropical forests on interannual timescales. Submitted to Glob. Biogeochem. Cyc. Also ESS Open Archive . https://doi.org/10.22541/essoar.172132358.83519012/v1  

Wang, W., Ciais, P., Nemani, R. R., Canadell, J. G., Piao, S., Sitch, S., . . ., Myneni, R. B. (2013). Variations in atmospheric CO2 growth rates coupled  with tropical temperature. Proc. Natl Acad. Sci., 110 , 13061–13066. https://doi.org/10.1073/pnas.1219683110