18 research outputs found
Local and remote impacts of regional aerosol emissions on climate
Aerosols are short-lived in the atmosphere, and so their distribution and climate forcing is very inhomogeneous. To understand the behaviour of the climate system in response to inhomogeneous forcing, and to inform emission policy choices, we must investigate how emissions from individual geographic regions affect the climate regionally and globally. I present here the results of two interwoven modelling studies. First, I analyse the simulated temperature response to perturbing sulfur dioxide emissions over a specific region – China – in three current generation climate models. Second, I systematically investigate with a single model the temperature and precipitation responses to black carbon and sulfur dioxide emissions from the United States, Europe, East Asia, and India.
These simulations reveal in the first instance that there is very large uncertainty around aerosol-climate interactions in present climate models. Removing SO2 emissions from China results in a six-fold difference in the optical depth and short-wave flux changes over China between different models, and the resulting surface temperature response is poorly constrained. However, the subsequent systematic perturbations indicate that in the event the regional forcing is large, then there are striking features of the climate response that are consistent across different perturbation locations. Emission changes always result not only in a strong local response around the emission region, but also a strong remote response, the pattern of which is insensitive to the original location of emission changes. There is, however, variation in the efficacy with which emissions from different regions force the climate, with US and European sulphur dioxide emission changes having a larger effect than East Asian emission changes.
The results presented here are relevant for understanding the effect of potential future emission controls, and also for understanding how the climate responds to different localised forcings, which has not been tested in complex coupled climate models previously.Open Acces
Reduced order digital twin and latent data assimilation for global wildfire prediction
The occurrence of forest fires can impact vegetation in the ecosystem, property, and human health, but also indirectly affect the climate. JULES-INFERNO is a global land surface model, which simulates vegetation, soils, and fire occurrence driven by environmental factors. However, this model incurs substantial computational costs due to the high data dimensionality and the complexity of differential equations. Deep learning-based digital twins have an advantage in handling large amounts of data. They can reduce the computational cost of subsequent predictive models by extracting data features through Reduced Order Modelling (ROM) and then compressing the data to a low-dimensional latent space. This study proposes a JULES-INFERNO-based digital twin fire model using ROM techniques and deep learning prediction networks to improve the efficiency of global wildfire predictions. The iterative prediction implemented in the proposed model can use current-year data to predict fires in subsequent years. To avoid the accumulation of errors from the iterative prediction, Latent data Assimilation (LA) is applied to the prediction process. LA manages to efficiently adjust the prediction results to ensure the stability and sustainability of the prediction. Numerical results show that the proposed model can effectively encode the original data and achieve accurate surrogate predictions. Furthermore, the application of LA can also effectively adjust the bias of the prediction results. The proposed digital twin also runs 500 times faster for online predictions than the original JULES-INFERNO model without requiring High-Performance Computing (HPC) clusters. The implementation code of this study and the developed models are available at https://github.com/DL-WG/Digital-twin-LA-global-wildfire.</p
A global behavioural model of human fire use and management: WHAM! v1.0
Fire is an integral ecosystem process and a major natural source of vegetation disturbance globally. Yet at the same time, humans use and manage fire in diverse ways and for a huge range of purposes. Therefore, it is perhaps unsurprising that a central finding of the first Fire Model Intercomparison Project was simplistic representation of humans is a substantial shortcoming in the fire modules of dynamic global vegetation models (DGVMs). In response to this challenge, we present a novel, global geospatial model that seeks to capture the diversity of human–fire interactions. Empirically grounded with a global database of anthropogenic fire impacts, WHAM! (the Wildfire Human Agency Model) represents the underlying behavioural and land system drivers of human approaches to fire management and their impact on fire regimes. WHAM! is designed to be coupled with DGVMs (JULES-INFERNO in the current instance), such that human and biophysical drivers of fire on Earth, and their interactions, can be captured in process-based models for the first time. Initial outputs from WHAM! presented here are in line with previous evidence suggesting managed anthropogenic fire use is decreasing globally and point to land use intensification as the underlying reason for this phenomenon.</p
Similar patterns of tropical precipitation and circulation changes under solar and greenhouse gas forcing
Funder: "Cosmic and electric effects on aerosols and clouds”; Grant(s): (MIS: 5049552)Funder: Villum Fonden; doi: http://dx.doi.org/10.13039/100008398Abstract: Theory and model evidence indicate a higher global hydrological sensitivity for the same amount of surface warming to solar as to greenhouse gas (GHG) forcing, but regional patterns are highly uncertain due to their dependence on circulation and dynamics. We analyse a multi-model ensemble of idealized experiments and a set of simulations of the last millennium and we demonstrate similar global signatures and patterns of forced response in the tropical Pacific, of higher sensitivity for the solar forcing. In the idealized simulations, both solar and GHG forcing warm the equatorial Pacific, enhance precipitation in the central Pacific, and weaken and shift the Walker circulation eastward. Centennial variations in the solar forcing over the last millennium cause similar patterns of enhanced equatorial precipitation and slowdown of the Walker circulation in response to periods with stronger solar forcing. Similar forced patterns albeit of considerably weaker magnitude are identified for variations in GHG concentrations over the 20th century, with the lower sensitivity explained by fast atmospheric adjustments. These findings differ from previous studies that have typically suggested divergent responses in tropical precipitation and circulation between the solar and GHG forcings. We conclude that tropical Walker circulation and precipitation might be more susceptible to solar variability rather than GHG variations during the last-millennium, assuming comparable global mean surface temperature changes
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The influence of remote aerosol forcing from industrialised economies on the future evolution of East and West African rainfall
Past changes in global industrial aerosol emissions have played a significant role in historical shifts in African rainfall and yet assessment of the impact on African rainfall of near term (10-40 year) potential aerosol emission pathways remains largely unexplored.
Whilst existing literature links future aerosol declines to a northward shift of Sahel rainfall, existing climate projections rely on RCP scenarios that do not explore the range of air quality drivers. Here we present projections from two emission scenarios that better envelope the range of potential aerosol emissions. More aggressive emission cuts results in northward shifts of the tropical rain-bands whose signal can emerge from expected internal variability on short, 10-20 year, time horizons. We also show for the first time that this northward shift also impacts East Africa, with evidence of delays to both onset and withdrawal of the Short Rains. However, comparisons of rainfall impacts across models suggest that only certain aspects of both the West and East African model responses may be robust, given model uncertainties.
This work motivates the need for wider exploration of air quality scenarios in the climate science community to assess the robustness of these projected changes and to provide evidence to underpin climate adaptation in Africa. In particular, revised estimates of emission impacts of legislated measures every 5-10 years would have a value in providing near term climate adaptation information for African stakeholders
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Predicting global patterns of long-term climate change from short-term simulations using machine learning
Understanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art climate models remains a central bottleneck in this endeavour. Here we introduce a machine learning approach, which utilises a unique dataset of existing climate model simulations to learn relationships between short-te¬rm and long-term temperature responses to different climate forcing scenarios. This approach not only has the potential to accelerate climate change projections by reducing the costs of scenario computations, but also helps uncover early indicators of modelled long-term climate responses, which is of relevance to climate change detection, predictability and attribution. Our results highlight challenges and opportunities for data-driven climate modelling, especially concerning the incorporation of even larger model datasets in the future. We therefore encourage extensive data sharing among research institutes to build ever more powerful climate response emulators, and thus to enable faster climate change projections
Improving together: better science writing through peer learning
Science, in our case the climate and geosciences, is increasingly interdisciplinary. Scientists must therefore communicate across disciplinary boundaries. For this communication to be successful, scientists must write clearly and concisely, yet the historically poor standard of scientific writing does not seem to be improving. Scientific writing must improve, and the key to long-term improvement lies with the early-career scientist (ECS). Many interventions exist for an ECS to improve their writing, like style guides and courses. However, momentum is often difficult to maintain after these interventions are completed. Continuity is key to improving writing. This paper introduces the ClimateSnack project, which aims to motivate ECSs to develop and continue to improve their writing and communication skills. The project adopts a peer-learning framework where ECSs voluntarily form writing groups at different institutes around the world. The group members learn, discuss, and improve their writing skills together. Several ClimateSnack writing groups have formed. This paper examines why some of the groups have flourished and others have dissolved. We identify the challenges involved in making a writing group successful and effective, notably the leadership of self-organized groups, and both individual and institutional time management. Within some of the groups, peer learning clearly offers a powerful tool to improve writing as well as bringing other benefits, including improved general communication skills and increased confidence
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The effect of rapid adjustments to halocarbons and N2O on radiative forcing
Rapid adjustments occur after initial perturbation of an external climate driver (e.g., CO2) and involve changes in, e.g. atmospheric temperature, water vapour and clouds, independent of sea surface temperature changes. Knowledge of such adjustments is necessary to estimate effective radiative forcing (ERF), a useful indicator of surface temperature change, and to understand global precipitation changes due to different drivers. Yet, rapid adjustments have not previously been analysed in any detail for certain compounds, including halocarbons and N2O. Here we use several global climate models combined with radiative kernel calculations to show that individual rapid adjustment terms due to CFC-11, CFC-12 and N2O are substantial, but that the resulting flux changes approximately cancel at the top-of-atmosphere due to compensating effects. Our results further indicate that radiative forcing (which includes stratospheric temperature adjustment) is a reasonable approximation for ERF. These CFCs lead to a larger increase in precipitation per kelvin surface temperature change (2.2 ± 0.3% K−1) compared to other well-mixed greenhouse gases (1.4 ± 0.3% K−1 for CO2). This is largely due to rapid upper tropospheric warming and cloud adjustments, which lead to enhanced atmospheric radiative cooling (and hence a precipitation increase) and partly compensate increased atmospheric radiative heating (i.e. which is associated with a precipitation decrease) from the instantaneous perturbation