System Model Research Articles

West African Monsoon System’s Responses to Global Ocean–Regional Atmosphere Coupling

Abstract This study explores the added value (AV) of a regional Earth system model (ESM) compared to an atmosphere-only regional climate model (RCM) in simulating West African monsoon (WAM) rainfall. The primary goals are to foster discussions on the suitability of coupled RCMs for WAM projections and deepen our understanding of ocean–atmosphere coupling’s influence on the WAM system. The study employs results from dynamical downscaling of the ERA-Interim reanalysis and Max Plank Institute ESM, low resolution (MPI-ESM-LR), by two RCMs, atmosphere only (REMO) and REMO coupled with Max Planck Institute Ocean Model (MPIOM) (ROM), at ∼25-km horizontal resolution. Results show that in regions distant from coupling domain boundaries such as West Africa (WA), constraint conditions from ERA-Interim are more beneficial than coupling effects. REMO, reliant on oceanic sea surface temperatures (SSTs) from observations and influenced by ERA-Interim, is biased under coupling conditions, although coupling offers potential advantages in representing heat and mass fluxes. Contrastingly, as intended, coupling improves SSTs and monsoon fluxes’ relationships under ESM-forced conditions. In this latter case, the coupling features a dipole-like spatial structure of AV, improving precipitation over the Guinea Coast but degrading precipitation over half of the Sahel. Our extensive examination of physical processes and mechanisms underpinning the WAM system supports the plausibility of AV. Additionally, we found that the monsoonal dynamics over the ocean respond to convective activity, with the Sahara–Sahel surface temperature gradient serving as the maintenance mechanism. While further efforts are needed to enhance the coupled RCM, we advocate for its use in the context of WAM rainfall forecasts and projections.

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

An Assessment of Convergence of Climate Reanalyses from Two Coupled Data Assimilation Systems with Identical High-Efficiency Filtering

Abstract Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitime-scale high-efficiency filtering algorithm which includes a deep-ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously established CDA system within the Coupled Model, version 2.1 (CM2-MSHea-CDA), that is developed by the Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past 4 decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep-ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, El Niño–Southern Oscillation (ENSO), and Pacific decadal oscillation (PDO). Particularly, both CDA systems generate similar time mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations. Significance Statement A coupled climate model simulates the interactions of the atmosphere, ocean, sea ice, and land processes and derives change and variations of the climate system by combining these components. Due to imperfect numerical schemes, a model usually has systematic biases to the real world. Coupled data assimilation (CDA) combines coupled model dynamics and physics with atmospheric and oceanic observations to reconstruct the historical states of Earth’s climate system, forming the analysis of climate change and variability. However, because of the existence of model errors and individual data assimilation algorithm behaviors, currently, the ocean variability of reanalysis produced by different numerical systems is basically divergent. Here, we first apply an identical coupled data assimilation algorithm and effective model error relaxing scheme to two widely used IPCC coupled models to establish two CDA systems. Then, we use both CDA systems to assimilate historical atmospheric and oceanic observations to form two sets of climate reanalyses for the past 4 decades. We found, by some degree, that the results of these multimodel coupled climate reanalyses converged to what historical states ought to be in observational estimation. These convergent climate reanalyses are expected to significantly increase our understanding of CDA and climate assessment.

Pair Potential between Poloxamer 407 Micelles in 14 wt % Aqueous Solution Clarifying the Sol-Gel-Sol Transition by Temperature Rise.

Poloxamer 407 (P407) is used as a safety-guaranteed, invaluable pharmaceutical nanocarrier. The aqueous solution of P407 exhibits sol-to-gel and gel-to-sol transitions, specifically during a temperature rise. Here, we develop a method to determine the pair potential between colloidal particles based primarily on experimental small-angle scattering data. Using this approach, the pair potential between the P407 micelles in the sol-gel-sol transition state is revealed without prelimiting any type of interaction forces. The results indicate that the increase in the attractive interaction contributes to enhancing the volume fraction, which is the decisive parameter for the gelation in terms of the Alder transition theory, i.e., the fluid-to-crystal phase transition of the hard-sphere model in the micelle system. This study demonstrates that one of the key mechanisms of the gel-to-sol transition upon heating is the enhancement of the structural fluctuation due to widening of the potential well in the intermicellar pair potential.

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models

This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior. To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability.

Oxic corrosion model for KAERI Reference disposal system via O2 consumption reactions and mixed-potential theory

The durability of copper (Cu) canisters against corrosion is critical for the licensing of deep geological repositories. Assessing oxic corrosion, a primary degradation mechanism, is essential for ensuring the reliability of such repositories. Due to the complex interactions influencing oxic corrosion, a comprehensive model is necessary for evaluating Cu canister corrosion. This study develops a model for the KAERI Reference Disposal System (KRS), incorporating mixed-potential theory with key O2 consumption reactions, including Cu corrosion, Cu(I) oxidation, FeS2 oxidation, aerobic microbial activity, and O2 dissolution and consumption. Simulation of 11 scenarios revealed that the representative KRS case would experience a maximum corrosion depth of 9.3 μm on the Cu canister after 2.3 years due to oxic corrosion, under conditions that are unfavorable for the initiation of pitting corrosion. These results suggest that oxic corrosion is not a threat to the isolation of spent nuclear fuels in KRS.

Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable ‘grey-box’ AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale ‘world model’ by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.

Thermodynamic modeling and analysis of a Carnot battery system integrating calcium looping thermochemical energy storage with coal-fired power plant

Long-term energy storage and carbon capture technologies are pivotal in managing renewable energy surpluses and achieving carbon neutrality. This paper proposes a Carnot battery system integrating calcium-looping thermochemical energy storage with a coal-fired power plant. The system utilizes excess electricity from the grid for energy input, facilitating long-term energy storage, achieving carbon capture, and reducing coal consumption in the power plant. An optimization framework is developed, incorporating component thermodynamic models and optimization algorithms, to maximize the coal savings in plants and energy efficiencies of the Carnot battery system. During the energy release process, the carbonator partially replaces the reheat load of the boiler, necessitating retrofitting of the boiler’s heating surface to ensure normal operation. The base system demonstrates a CO2 capture capacity of 3.23 MJ/kg and a reduction in coal consumption by 7.07 %. The round-trip efficiency and comprehensive efficiency of the base system are 36.93 % and 37.91 %, respectively. The relatively low energy efficiency is primarily due to the deactivation of circulating adsorbents and the limited efficiency of the subcritical coal-fired power plant. By incorporating an additional recarbonation step to mitigate adsorbent deactivation, the system’s comprehensive efficiency is improved to 42.20 %. Further improvements in system efficiency can be achieved by using modified adsorbents with high cycling stability instead of natural limestone and by coupling the system with more efficient ultra-supercritical units instead of the investigated subcritical units. This study offers valuable insights into the development of long-term energy storage solutions and multifunctional Carnot battery technology.

A dynamic modelling method for the naval gun under the influence of random wave excitation and servo system

ABSTRACT This paper proposed a mechanical–electrical-hydraulic coupling dynamic modelling method for naval guns coupled with multiple nonlinear factors. The ship's rocking motion equation is derived based on the wave spectrum formula to introduce the influence of random wave excitation into the vibration analysis of naval guns. An efficient gear transmission model of the servo system is developed using a multibody contact-based model to control the computational cost and based on the tooth stiffness theory, the time-varying mesh stiffnesses are considered. The dynamic results show that the ship's rolling motion will have more influence on gun dynamic characteristics than the pitching motion. Though under the control of servo systems, the adverse influence of the ship's rocking motion on cradle motion is basically eliminated. The instantaneous residual disturbances of the cradle increase for the driving torque saturation and the residual disturbances of the cradle will increase gradually with the ship's rocking motion increasing.

A Hierarchical Model for Complex Adaptive System: From Adaptive Agent to AI Society

As complex adaptive system involves human and social factors (e.g., changing demands, competition and collaboration among agents), accurately modeling the complex features of adaptive agents and AI society is crucial for the effective analysis and governance of complex adaptive systems. However, existing modeling methods struggle to accurately represent these complex features, there is a gap between existing technologies and complex features modeling. In this context, this paper proposes a hierarchical model based on the computational experiments method, which consists of four layers (i.e., L1, L2, L3 and L4) modeling the autonomous, evolutionary, interactive, and emergent features respectively from adaptive agent to AI society. Additionally, taking intelligent transportation system as an example, a computational experiments system is constructed to demonstrate the effectiveness of the proposed model. This model builds a bridge between complex feature modeling and various technologies, thereby offering theoretical support for further research in complex adaptive systems.

Aeolian Environment Regionalization in Xinjiang and Suggestions for Sand Prevention in Typical Areas

The Xinjiang region is prone to frequent and complex wind and sand disasters, which present a significant challenge to the sustainable development of local areas. This research uses multi-source data to analyze the spatial distribution of the aeolian environment in Xinjiang, establishes a four-level zoning scheme, and proposes recommendations for ecological management and engineering and control. Results indicate that (1) Xinjiang’s aeolian environment and its types exhibit spatial heterogeneity. The aeolian environment types display a high concentration in the eastern region and a low concentration in the western region. Furthermore, the aeolian environment types are concentrated in the basin region. Moreover, the aeolian environment types exhibit a meridional distribution pattern. (2) A four-level zoning system for aeolian environments in Xinjiang was developed, comprising two first-level zones, seven s-level subzones, 22 third-level wind zones, and 31 fourth-level subdivisions. (3) A structural model for a highway sand control system is proposed for aeolian environment types of subdivisions, including fixing-based, combined blocking and fixing, wind-blocking and sand-transferring, and combined blocking and fixing–transferring. The aeolian environment regionalization program proposed in this study can be a scientific reference for relevant departments in formulating and implementing sand prevention and control planning.

Early warning signals for bifurcations embedded in high dimensions

Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded in a much higher dimensional state space. In many cases of practical relevance, the form of this embedding is poorly understood or entirely unknown. This paper explores how measurement of the critical phenomena that generically precede such bifurcations can be used to make inferences about some properties of their embeddings, and, conversely, how prior knowledge about the mechanism of bifurcation can robustify predictions of an oncoming tipping event. These modes of analysis are first demonstrated on a simple fluid flow system undergoing a Hopf bifurcation. The same approach is then applied to data associated with the West African monsoon shift, with results corroborated by existing models of the same system. This example highlights the effectiveness of the methodology even when applied to complex climate data, and demonstrates how a well-resolved spatial structure associated with the onset of atmospheric instability can be inferred purely from time series measurements.

SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning

As a crucial nonlinear phenomenon, stimulated Raman scattering (SRS) plays multifaceted roles involved in forward and inverse problems. In fibre-optic systems, these roles range from detrimental interference that impairs optical performance to beneficial effects that enables various devices such as Raman amplifier. To obtain solutions of SRS, various numerical methods customized for different scenarios have been proposed. However, these methods are time-consuming, low-efficiency, and experience-orientated, particularly in combined scenarios consisting of both forward and inverse problems. Inspired by physics-informed neural networks, we propose SRS-Net, which combines the efficient automatic differentiation and powerful representation ability of neural networks with the regularization of SRS physical laws, to obtain universal solutions for SRS of forward, inverse, and combined problems. We showcase the intuitive solving procedure and high-speed performance of SRS-Net through extensive simulations covering different scenarios. Additionally, we validate its capabilities in experiments involving the high-fidelity modelling of a wavelength division multiplexing system spanning the C + L-band with approximately 10 THz. The versatility of the SRS-Net framework extends beyond SRS, indicating its potential as a promising universal solution in other engineering problems with nonlinear dynamics governed by partial differential equations.

In-silico Evaluation of the effects of blood flow rate change in ductus arteriosus on cardiac function and cerebral blood flow rate in term and preterm infants

The transition of cardiovascular system is vital for newborn infants. It is expected that ductus arteriosus which is vessel between aorta and pulmonary arteries closes in the first day of life. However, ductus arteriosus may not close within 24 h in preterm infants. In this study, a lumped parameter model was used to evaluate the effects of the change in blood flow rate through ductus arteriosus on cardiac function and cerebral blood flow rate in term and preterm neonatal infants. Mean arterial pressure increased from 41 mmHg to 53 mmHg at 24th hour after birth in term neonatal infant cardiovascular system model whereas it increased from 33 mmHg to 43 mmHg in preterm neonatal infant cardiovascular system model. Left cardiac output decreased from 911 mL/min to 721 mL/min and right cardiac output increased 551 mL/min to 706 mL/min in term neonatal infant cardiovascular system model. Left cardiac output decreased from 705 mL/min to 612 mL/min and right cardiac output increased 369 mL/min to 484 mL/min in preterm neonatal infant cardiovascular system model. Cerebral blood flow rate increased from 115 mL/min to 147.40 mL/min in term neonatal infant cardiovascular system model at 24th hour after birth and it increased 29.65 mL/min to 38.92 mL/min in preterm neonatal cardiovascular system model. The results showed that blood flow rate through ductus arteriosus alters cardiac function and cerebral blood flow rate in both term and preterm cardiovascular system models.

Unveiling the Dominant Factors Controlling the Long‐Term Changes in Northwest Pacific Tropical Cyclone Intensification Rates

AbstractTropical cyclones (TCs), especially intense TCs, pose serious threats to life and property particularly in the affected coastal regions. Understanding the factors determining the TC intensification rate (IR) remains a great challenge. This study identifies the dominant factors responsible for the observed significant increase in TC IR over the western North Pacific in recent decades using the energetically based dynamical system model of TC intensification. It is found that the environmental dynamical efficiency mainly contributed by vertical wind shear and upper‐level divergence is responsible for the long‐term changes in TC IR during the strong TC stage, but it played a secondary role in the long‐term changes in IR during the weak TC stage. The latter were primarily contributed by the maximum potential intensity, which is primarily determined by sea surface temperature. Results also strongly suggest that global warming is the primary driver of the long‐term changes in TC IR.

In 50 Shades of Orange: Germany’s Photovoltaic Power Generation Landscape

Spatiotemporally resolved data on photovoltaic (PV) power generation are very helpful to analyze the multiple impacts of this variable renewable energy on regional and local scales. In the absence of such disaggregated data for Germany, numerical simulations are needed to obtain the electricity production from PV systems for a time period and region under study. This manuscript presents how a physical simulation model, which uses open access weather and plant data as input vectors, can be created. The developed PV model is then applied to an ensemble of approximately 1.95 million PV systems, consisting of ground-mounted and rooftop installations, in order to compute their electricity production in Germany for the year 2020. The resulting spatially aggregated time series closely matches the measured PV feed-in pattern of Germany throughout the simulated year. Such disaggregated data can be applied to investigate the German PV power generation landscape at various spatiotemporal levels, as each PV system is taken into account with its technical data and the weather conditions at its geo-location. Furthermore, the German PV power generation landscape is presented as detailed maps based on these simulation results, which can also be useful for many other scientific fields such as energy system modeling.

Exploration of diverse solutions for the calibration of imperfect climate models

Abstract. The calibration of Earth system model parameters is subject to data, time, and computational constraints. The high dimensionality of this calibration problem, combined with errors arising from model structural assumptions, makes it impossible to find model versions fully consistent with historical observations. Therefore, the potential for multiple plausible configurations presenting different trade-offs between skills in various variables and spatial regions remains usually untested. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a perturbed physics ensemble relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. A meta-model is used to predict the outputs of a climate model reduced through principal component analysis. Then, a subset of input parameter values yielding results similar to a reference simulation is identified. We argue that the effective degrees of freedom in the model performance response to parameter input (the “parametric component”) are, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. The results explore the potential for comparably performing parameter configurations that have different trade-offs in model errors. These model candidates can inform model development and could potentially lead to significantly different future climate evolution.

Contrasting responses of vegetation productivity to intraseasonal rainfall in Earth system models

Abstract. Correctly representing the response of vegetation productivity to water availability in Earth system models (ESMs) is essential for accurately modelling the terrestrial carbon cycle and the evolution of the climate system. Previous studies evaluating gross primary productivity (GPP) in ESMs have focused on annual mean GPP and interannual variability, but physical processes at shorter timescales are important for determining vegetation–climate coupling. We evaluate GPP responses at the intraseasonal timescale in five CMIP6 ESMs by analysing changes in GPP after intraseasonal rainfall events with a timescale of approximately 25 d. We compare these responses to those found in a range of observation-based products. When composited around all intraseasonal rainfall events globally, both the amplitude and the timing of the GPP response show large inter-model differences, demonstrating discrepancies between models in their representation of water–carbon coupling processes. However, the responses calculated from the observational datasets also vary considerably, making it challenging to assess the realism of the modelled GPP responses. The models correctly capture the fact that larger increases in GPP at the regional scale are associated with larger increases in surface soil moisture and larger decreases in atmospheric vapour pressure deficit. However, the sensitivity of the GPP response to these drivers varies between models. The GPP in NorESM is insufficiently sensitive to vapour pressure deficit perturbations when compared all to other models and six out of seven observational GPP products tested. Most models produce a faster GPP response where the surface soil moisture perturbation is larger, but the observational evidence for this relationship is weak. This work demonstrates the need for a better understanding of the uncertainties in the representation of water–vegetation relationships in ESMs and highlights a requirement for future daily-resolution observations of GPP to provide a tighter constraint on global water–carbon coupling processes.

Performance evaluation and comparative study on a novel solar-heat-driven ejection-compression hybrid cooling system with subcooling storage

Refrigeration technology contributes to 10∼15% of global energy consumption, resulting in significant carbon emission. Solar-driven ejection-compression refrigeration system is promising for reducing electricity consumption and carbon emissions. However, existing solar ejection-compression refrigeration systems suffer from drawbacks of low heat utilization efficiency, oversized solar collectors, and thermal leakage due to large temperature differences of storage devices. Addressing these challenges, this study proposes and investigates a new solar-assisted ejector-compressor hybrid refrigeration system with subcooling storage coupled at intermediate temperatures. The system model is established, and the ejector model is experimentally validated. Through system modelling, an energetic and exergetic performance comparative analyses are conducted. The results indicate that the COP of the proposed system is 9.7% higher than that of traditional system combinations, producing the same cooling capacity with the same generating heat. Moreover, considering the stored cooling energy can be fully converted to cooling energy at evaporating temperatures, the COPh of the new system reaches 8.29, 24.5% higher than traditional systems. The cooling storage at 15°C with 0.325 ejector entrainment ratio suggests a reduction of approximately two-thirds in energy storage, lower temperature differences, and reduced thermal leakage, leading to decreased space and economic costs and improved energy performance. Additionally, as the COPh, COPgh, and ηex of MCS mode consistently outperform those of ZCS and HCS modes, the MCS mode is prioritized in system operation. This study underscores that the new system offers superior overall energy efficiency and requires smaller storage devices compared to traditional systems, revealing its promising practical applications.

Models and methods for transport demand and decarbonisation: A review

Abstract Rising global greenhouse gas emissions from the transport sector pose a major challenge to meeting the targets of the Paris Agreement. This raises questions of how technology, infrastructure and societal trends and policies can influence transport demand and thus also emissions, energy demand and service levels. Here the literature on factors relevant to shifting total transport activity and mode shares, categorised into exogenous drivers, socio-behavioural, infrastructural and technological aspects, is reviewed. For each factor, current approaches to modelling and measuring the impact of each factor on transport systems are summarised, resulting in a proposed taxonomy to classify transport demand modelling approaches. We then comment on the suitability and sufficiency of existing modelling approaches for representing scenarios consistent with the Paris Agreement targets in models of the entire global energy system. Factors that affect transport demand are currently insufficiently represented in integrated assessment modelling approaches and thus emission reduction pathways. Improving the comprehension and representation of diverse factors that affect transport demand in global energy systems models, by incorporating features of complementary models with high resolution representations of transport, holds promise for generating well informed policy recommendations. Accordingly, policies could influence the development of the factors themselves and their potential role in mitigating climate change.