Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections

Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China

Climate policies will affect future surface ozone pollution in China. Here, we simulate changes in summertime ozone across China by 2030 under four emission scenarios reflecting different levels of climate action. We also contrast results obtained with two different chemical mechanisms employed in the chemical transport model (WRF-Chem). With emission reductions in ozone precursors introduced by climate policies, both mechanisms show promising ozone mitigation for most parts of China. However, they disagree starkly in China\u27s three main city clusters, where one mechanism projects worsening ozone pollution by 2030 despite the emission reductions. We analyze possible drivers of this important discrepancy, in particular the role of varying ozone chemical regimes affecting its sensitivity to emission changes. We recommend an intercomparison project to examine this critical modeling uncertainty among other models/mechanisms, which would be invaluable for informing local and regional emission control strategies that are based on single-model results

Tropical Pacific climate variability under solar geoengineering: impacts on ENSO extremes

Many modelling studies suggest that the El Niño-Southern Oscillation (ENSO), in interaction with the tropical Pacific background climate, will change with rising atmospheric greenhouse gas concentrations. Solar geoengineering (reducing the solar flux from outer space) has been proposed as a means to counteract anthropogenic climate change. However, the effectiveness of solar geoengineering concerning a variety of aspects of Earth's climate is uncertain. Robust results are particularly challenging to obtain for ENSO because existing geoengineering simulations are too short (typically ∼ 50 years) to detect statistically significant changes in the highly variable tropical Pacific background climate. We here present results from a 1000-year-long solar-geoengineering simulation, G1, carried out with the coupled atmosphere-ocean general circulation model HadCM3L. In agreement with previous studies, reducing the solar irradiance (4 %) to offset global mean surface warming in the model more than compensates the warming in the tropical Pacific that develops in the 4 × CO2 scenario. We see an overcooling of 0.3 °C and a 0.23 mm d-1 (5 %) reduction in mean rainfall over the tropical Pacific relative to preindustrial conditions in the G1 simulation, owing to the different latitudinal distributions of the shortwave (solar) and longwave (CO2) forcings. The location of the Intertropical Convergence Zone (ITCZ) in the tropical Pacific, which moved 7.5° southwards under 4 × CO2, is restored to its preindustrial position. However, other aspects of the tropical Pacific mean climate are not reset as effectively. Relative to preindustrial conditions, in G1 the time-averaged zonal wind stress, zonal sea surface temperature (SST) gradient, and meridional SST gradient are each statistically significantly reduced by around 10 %, and the Pacific Walker Circulation (PWC) is consistently weakened, resulting in conditions conducive to increased frequency of El Niño events. The overall amplitude of ENSO strengthens by 9 %-10 % in G1, but there is a 65 % reduction in the asymmetry between cold and warm events: cold events intensify more than warm events. Notably, the frequency of extreme El Niño and La Niña events increases by ca. 60 % and 30 %, respectively, while the total number of El Niño events increases by around 10 %. All of these changes are statistically significant at either 95 or 99 % confidence level. Somewhat paradoxically, while the number of total and extreme events increases, the extreme El Niño events become weaker relative to the preindustrial state, while the extreme La Niña events become even stronger. That is, such extreme El Niño events in G1 become less intense than under preindustrial conditions but also more frequent. In contrast, extreme La Niña events become stronger in G1, which is in agreement with the general overcooling of the tropical Pacific in G1 relative to preindustrial conditions

Large modeling uncertainty in projecting decadal surface ozone changes over city clusters of China

Climate policies will affect future surface ozone pollution in China. Here, we simulate changes in summertime ozone across China by 2030 under four emission scenarios reflecting different levels of climate action. We also contrast results obtained with two different chemical mechanisms employed in the chemical transport model (WRF-Chem). With emission reductions in ozone precursors introduced by climate policies, both mechanisms show promising ozone mitigation for most parts of China. However, they disagree starkly in China's three main city clusters, where one mechanism projects worsening ozone pollution by 2030 despite the emission reductions. We analyze possible drivers of this important discrepancy, in particular the role of varying ozone chemical regimes affecting its sensitivity to emission changes. We recommend an intercomparison project to examine this critical modeling uncertainty among other models/mechanisms, which would be invaluable for informing local and regional emission control strategies that are based on single-model results

A 1D RCE study of factors affecting the tropical tropopause layer and surface climate

There are discrepancies between global climate models regarding the evolution of the tropical tropopause layer (TTL) and also whether changes in ozone impact the surface under climate change. We use a 1D clear-sky radiative–convective equilibrium model to determine how a variety of factors can affect the TTL and how they influence surface climate. We develop a new method of convective adjustment, which relaxes the temperature profile toward the moist adiabat and allows for cooling above the level of neutral buoyancy. The TTL temperatures in our model are sensitive to CO2 concentration, ozone profile, the method of convective adjustment, and the upwelling velocity, which is used to calculate a dynamical cooling rate in the stratosphere. Moreover, the temperature response of the TTL to changes in each of the above factors sometimes depends on the others. The surface temperature response to changes in ozone and upwelling at and above the TTL is also strongly amplified by both stratospheric and tropospheric water vapor changes. With all these influencing factors, it is not surprising that global models disagree with regard to TTL structure and evolution and the influence of ozone changes on surface temperatures. On the other hand, the effect of doubling CO2 on the surface, including just radiative, water vapor, and lapse-rate feedbacks, is relatively robust to changes in convection, upwelling, or the applied ozone profile

High-mobility, trap-free charge transport in conjugated polymer diodes

Charge transport in conjugated polymer semiconductors has traditionally been thought to be limited to a low mobility regime by pronounced energetic disorder. Much progress has recently been made in advancing carrier mobilities in field-effect transistors through developing low-disorder conjugated polymers. However, in diodes these polymers have to date not shown much improved mobilities, presumably reflecting the fact that in diodes lower carrier concentrations are available to fill up residual tail states in the density of states. Here, we show that the bulk charge transport in low-disorder polymers is limited by water-induced trap states and that their concentration can be dramatically reduced through incorporating small molecular additives into the polymer film. Upon incorporation of the additives we achieve space-charge limited current characteristics that resemble molecular single crystals such as rubrene with high, trap-free SCLC mobilities up to 0.2 cm2/Vs and a width of the residual tail state distribution comparable to kBT.We gratefully acknowledge financial support the Engineering and Physical Sciences Research Council (EPSRC) through a Programme Grant (EP/M005141/1). M.N. acknowledges financial support from the European Commission through a Marie-Curie Individual Fellowship (EC Grant Agreement Number: 747461)

Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations

A number of studies have demonstrated the importance of ozone in climate change simulations, for example concerning global warming projections and atmospheric dynamics. However, fully interactive atmospheric chemistry schemes needed for calculating changes in ozone are computationally expensive. Climate modelers therefore often use climatological ozone fields, which are typically neither consistent with the actual climate state simulated by each model nor with the specific climate change scenario. This limitation applies in particular to standard modeling experiments such as preindustrial control or abrupt 4xCO2 climate sensitivity simulations. Here we suggest a novel method using a simple linear machine learning regression algorithm to predict ozone distributions for preindustrial and abrupt 4xCO2 simulations. Using the atmospheric temperature field as the only input, the regression reliably predicts three-dimensional ozone distributions at monthly to daily time intervals. In particular, the representation of stratospheric ozone variability is much improved compared with a fixed climatology, which is important for interactions with dynamical phenomena such as the polar vortices and the Quasi-Biennial Oscillation. Our method requires training data covering only a fraction of the usual length of simulations and thus promises to be an important stepping stone towards a range of new computationally efficient methods to consider ozone changes in long climate simulations. We highlight key development steps to further improve and extend the scope of machine learning-based ozone parameterizations

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