Large Model Ensembles Research Articles

Precipitation trends determine future occurrences of compound hot\u2013dry events

Compound hot–dry events—co-occurring hot and dry extremes—frequently cause damages to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Strong increases in the occurrence of these events are projected with warming, but associated uncertainties remain large and poorly understood. Here, using climate model large ensembles, we show that mean precipitation trends exclusively modulate the future occurrence of compound hot–dry events over land. This occurs because local warming will be large enough that future droughts will always coincide with at least moderately hot extremes, even in a 2 °C warmer world. By contrast, precipitation trends are often weak and equivocal in sign, depending on the model, region and internal climate variability. Therefore, constraining regional precipitation trends will also constrain future compound hot–dry events. These results help to assess future frequencies of other compound extremes characterized by strongly different trends in the drivers.

Upscaling and downscaling Monte Carlo ensembles with generative models

SUMMARYMonte Carlo methods are widespread in geophysics and have proved to be powerful in non-linear inverse problems. However, they are associated with significant practical challenges, including long calculation times, large output ensembles of Earth models, and difficulties in the appraisal of the results. This paper addresses some of these challenges using generative models, a family of tools that have recently attracted much attention in the machine learning literature. Generative models can, in principle, learn a probability distribution from a set of given samples and also provide a means for rapid generation of new samples which follow that approximated distribution. These two features make them well suited for application to the outputs of Monte Carlo algorithms. In particular, training a generative model on the posterior distribution of a Bayesian inference problem provides two main possibilities. First, the number of parameters in the generative model is much smaller than the number of values stored in the ensemble, leading to large compression rates. Secondly, once trained, the generative model can be used to draw any number of samples, thereby eliminating the dependence on an often large and unwieldy ensemble. These advantages pave new pathways for the use of Monte Carlo ensembles, including improved storage and communication of the results, enhanced calculation of numerical integrals, and the potential for convergence assessment of the Monte Carlo procedure. Here, these concepts are initially demonstrated using a simple synthetic example that scales into higher dimensions. They are then applied to a large ensemble of shear wave velocity models of the core–mantle boundary, recently produced in a Monte Carlo study. These examples demonstrate the effectiveness of using generative models to approximate posterior ensembles, and indicate directions to address various challenges in Monte Carlo inversion.

Increasing spatiotemporal proximity of heat and precipitation extremes in a warming world quantified by a large model ensemble

Increases in climate hazards and their impacts mark one of the major challenges of climate change. Situations in which hazards occur close enough to one another to result in amplified impacts, because systems are insufficiently resilient or because hazards themselves are made more severe, are of special concern. We consider projected changes in such compounding hazards using the Max Planck Institute Grand Ensemble under a moderate (RCP4.5) emissions scenario, which produces warming of about 2.25 °C between pre-industrial (1851–1880) and 2100. We find that extreme heat events occurring on three or more consecutive days increase in frequency by 100%–300%, and consecutive extreme precipitation events increase in most regions, nearly doubling for some. The chance of concurrent heat and drought leading to simultaneous maize failures in three or more breadbasket regions approximately doubles, while interannual wet-dry oscillations become at least 20% more likely across much of the subtropics. Our results highlight the importance of taking compounding climate extremes into account when looking at possible tipping points of socio-environmental systems.

Attributing Compound Events to Anthropogenic Climate Change

Abstract Extreme event attribution answers the question of whether and by how much anthropogenic climate change has contributed to the occurrence or magnitude of an extreme weather event. It is also used to link extreme event impacts to climate change. Impacts, however, are often related to multiple compounding climate drivers. Because extreme event attribution typically focuses on univariate assessments, these assessments might only provide a partial answer to the question of anthropogenic influence to a high-impact event. We present a theoretical extension to classical extreme event attribution for certain types of compound events. Based on synthetic data, we illustrate how the bivariate fraction of attributable risk (FAR) differs from the univariate FAR depending on the extremeness of the event as well as the trends in and dependence between the contributing variables. Overall, the bivariate FAR is similar in magnitude or smaller than the univariate FAR if the trend in the second variable is comparably weak and the dependence between both variables is moderate or high, a typical situation for temporally co-occurring heat waves and droughts. If both variables have similarly large trends or the dependence between both variables is weak, bivariate FARs are larger and are likely to provide a more adequate quantification of the anthropogenic influence. Using multiple climate model large ensembles, we apply the framework to two case studies, a recent sequence of hot and dry years in the Western Cape region of South Africa and two spatially co-occurring droughts in crop-producing regions in South Africa and Lesotho.

Fast Screening Tool for Assessing the Impact of Poro-Mechanics on Fractured Reservoirs Using Dual-Porosity Flow Diagnostics

Summary Accounting for poro-mechanical effects in full-field reservoir simulation studies and uncertainty quantification workflows is still limited, mainly because of their high computational cost. We introduce a new approach that couples hydrodynamics and poro-mechanics with dual-porosity flow diagnostics to analyze how poro-mechanics could affect reservoir dynamics in naturally fractured reservoirs without significantly increasing computational overhead. Our new poro-mechanically informed dual-porosity flow diagnostics account for steady-state and single-phase flow conditions in the fractured medium while the fracture-matrix fluid exchange is approximated using a physics-based transfer rate coefficient, which models two-phase flow using an analytical solution for spontaneous imbibition or gravity drainage. The deformation of the system is described by the dual-porosity poro-elastic theory, which is based on mixture theory and micromechanics to compute the effective stresses and strains of the rock matrix and fractures. The solutions to the fluid flow and rock deformation equations are coupled sequentially. The governing equations for fluid flow are discretized using a finite-volume method with two-point flux-approximation while the governing equations for poro-mechanics are discretized using the virtual element method. The solution of the coupled system considers stress-dependent permeabilities for fractures and matrix. Our framework is implemented in the open-source MATLAB Reservoir Simulation Toolbox (MRST). We present a case study using a fractured carbonate reservoir analog to illustrate the integration of poro-mechanics within the dual-porosity flow diagnostics framework. The extended flow diagnostics calculations enable us to quickly screen how the dynamics in fractured reservoirs (e.g., reservoir connectivity, sweep efficiency, and fracture-matrix transfer rates) are affected by the complex interactions between poro-mechanics and fluid flow where changes in pore pressure and effective stress modify petrophysical properties and hence affect reservoir dynamics. Because of the steady-state nature of the calculations and the effective coupling strategy, these calculations do not incur significant computational overheads. They provide an efficient complement to traditional reservoir simulation and uncertainty quantification workflows because they enable us to assess a broader range of reservoir uncertainties (e.g., geological, petrophysical, and hydromechanical uncertainties). The capability of studying a much broader range of uncertainties allows the comparison and ranking from a large ensemble of reservoir models and select individual candidates for more detailed full-physics reservoir simulation studies without compromising on assessing the range of uncertainties inherent to fractured reservoirs.

Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations

We present a dataset of daily, bias-adjusted temperature and precipitation projections for continental Africa based on a large ensemble of regional climate model simulations, which can be useful for climate change impact studies in several sectors. We provide guidance on the benefits and caveats of using the dataset by investigating the effect of bias-adjustment on impact-relevant indices (both their future absolute value and change). Extreme threshold-based temperature indices show large differences between original and bias-adjusted values at the end of the century due to the general underestimation of temperature in the present climate. These results indicate that when biases are accounted for, projected risks of extreme temperature-related hazards are higher than previously found, with possible consequences for the planning of adaptation measures. Bias-adjusted results for precipitation indices are usually consistent with the original results, with the median change preserved for most regions and indices. The interquartile and full range of the original model ensemble is usually well preserved by bias-adjustment, with the exception of maximum daily precipitation, whose range is usually greatly reduced by the bias-adjustment. This is due to the poor simulation and extremely large model range for this index over the reference period; when the bias is reduced, most models converge in projecting a similar change. Finally, we provide a methodology to select a small subset of simulations that preserves the overall uncertainty in the future projections of the large model ensemble. This result can be useful in practical applications when process-based impact models are too expensive to be run with the full ensemble of model simulations.

Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1): Multivariate bias‐corrected climate model outputs for terrestrial modelling and attribution studies in North America

AbstractThe Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) datasets are bias‐corrected using a multivariate quantile‐mapping algorithm for statistical consistency – in terms of marginal distributions and multivariate dependence structure – with two observationally constrained historical meteorological forcing datasets. For each observational dataset, bias‐adjusted variables are provided for two sets of 50‐member initial‐condition CanESM2 ensembles (historical plus RCP8.5 scenarios, 1950–2005 and 2006–2100, respectively; and historicalNAT scenario, 1950–2020, which excludes anthropogenic forcings), and one 50‐member CanRCM4 ensemble (historical plus RCP8.5). The archive includes daily minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation and incoming longwave radiation. Intended uses include hydrological and land surface impact modelling, as well as related event attribution studies.

Minimax and Multi-Criteria Selection of Representative Model Portfolios for Complex Systems Analysis

The analysis of complex systems typically requires a large and diverse ensemble of models to confront the richness and complexity of the problem. Nevertheless, a common development and validation process of each individual model can be rigorous and time consuming. Because of limited time and resources, it is sometimes unrealistic to model every individual object in the population to capture the complex phenomena of the system. One such challenge arises in aerospace engineering when only a very limited number of aircraft performance models can be built to cover the aircraft fleet of the world for the environmental impact modeling of air transportation system. This paper considers the problem of selecting a small proportion of representative models to sufficiently cover and represent the population for more efficient and accurate systems analysis. At the intersection of unsupervised data mining and high-dimensional data analysis, the proposed methodology uses minimax and multi-criteria considerations to select a representative model portfolio at each budget level. Applying on the case study of representative aircraft models for environmental impact modeling, the benefits of the proposed method are demonstrated through both data visualization and quantitative metrics in multiple experiments. Potential limitations, extensions, and broader impacts of the method are also thoroughly discussed.

Defining the Internal Component of Atlantic Multidecadal Variability in a Changing Climate

AbstractThe canonical index of “Atlantic Multidecadal Variability” (AMV) is the low‐pass filtered timeseries of sea surface temperature anomalies (SSTA) averaged over the North Atlantic. This index and its associated SSTA spatial pattern confound externally forced climate change and internally generated climate variability. The internal component of AMV is commonly isolated by either subtracting the global‐mean SSTA or removing the pattern of SSTA associated with the global‐mean. This study evaluates the skill of each method with regard to the spatial pattern of internal AMV, using nine coupled model Large Ensembles over the period 1940–2100 as a testbed in which the true internal AMV is known a priori. The first method aliases the structure of forced climate change onto internal AMV, while the second method is generally robust to climate change. The models simulate realistic patterns of internal AMV, although such an assessment is hampered by the brevity of the observational record.

Projected changes in global terrestrial near-surface wind speed in 1.5 °C–4.0 °C global warming levels

Understanding future changes in global terrestrial near-surface wind speed (NSWS) in specific global warming level (GWL) is crucial for climate change adaption. Previous studies have projected the NSWS changes; however, the changes of NSWS with different GWLs have yet to be studied. In this paper, we employ the Max Planck Institute Earth System Model large ensembles to evaluate the contributions of different GWLs to the NSWS changes. The results show that the NSWS decreases over the Northern Hemisphere (NH) mid-to-high latitudes and increases over the Southern Hemisphere (SH) as the GWL increases by 1.5 °C–4.0 °C relative to the preindustrial period, and that these characteristics are more significant with the stronger GWL. The probability density of the NSWS shifts toward weak winds over NH and strong winds over SH between the current climate and the 4.0 °C GWL. Compared to 1.5 °C GWL, the NSWS decreases −0.066 m s−1 over NH and increases +0.065 m s−1 over SH with 4.0 °C GWL, especially for East Asia and South America, the decrease and increase are most significant, which reach −0.21 and +0.093 m s−1, respectively. Changes in the temperature gradient induced by global warming could be the primary factor causing the interhemispheric asymmetry of future NSWS changes. Intensified global warming induces the reduction in Hadley, Ferrell, and Polar cells over NH and the strengthening of the Hadley cell over SH could be another determinant of asymmetry changes in NSWS between two hemispheres.

Validity of estimating flood and drought characteristics under equilibrium climates from transient simulations

Future flood and drought risks have been predicted to transition from moderate to high levels at global warmings of 1.5 °C and 2.0 °C above pre-industrial levels, respectively. However, these results were obtained by approximating the equilibrium climate using transient simulations with steadily warming. This approach was recently criticised due to the warmer global land temperature and higher mean precipitation intensities of the transient climate in comparison with the equilibrium climate. Therefore, it is unclear whether floods and droughts projected under a transient climate can be systematically substituted for those occurring in an equilibrated climate. Here, by employing a large ensemble of global hydrological models (HMs) forced by global climate models, we assess the validity of estimating flood and drought characteristics under equilibrium climates from transient simulations. Differences in flood characteristics under transient and equilibrium climates could be largely ascribed to natural variability, indicating that the floods derived from a transient climate reasonably approximate the floods expected in an equally warm, equilibrated climate. By contrast, significant differences in drought intensity between transient and equilibrium climates were detected over a larger global land area than expected from natural variability. Despite the large differences among HMs in representing the low streamflow regime, we found that the drought intensities occurring under a transient climate may not validly represent the intensities in an equally warm equilibrated climate for approximately 6.7% of the global land area.

Estuarine tidal range dynamics under rising sea levels.

How an estuary responds to sea level rise (SLR) is complex and depends on energy drivers (e.g., tides and river inflows), estuarine geometry (e.g., length and depth), intrinsic fluid properties (e.g., density), and bed/bank roughness. While changes to the tidal range under SLR can impact estuarine sediment transport, water quality, and vegetation communities, studies on the altered tidal range under SLR are often based on case studies with outcomes applicable to a specific site. As such, this study produced a large ensemble of estuarine hydrodynamic models (>1800) to provide a systematic understanding of how tidal range dynamics within different estuary types may change under various SLR and river inflow scenarios. The results indicated that SLR often amplifies the tidal range of different estuary types, except for short estuaries with a low tidal range at the mouth where SLR attenuates the tides. SLR alters the location of the points with minimum tidal range and overall tidal range patterns in an estuary. Variations in tidal range were more evident in converging estuaries, shallower systems, or in estuaries with strong river inflows. These findings provide an indication of how different estuary types may respond to estuaries and may assist estuarine managers and decision makers.

The Emergence and Transient Nature of Arctic Amplification in Coupled Climate Models

Under rising atmospheric greenhouse gas concentrations, the Arctic exhibits amplified warming relative to the globe. This Arctic amplification is a defining feature of global warming. However, the Arctic is also home to large internal variability, which can make the detection of a forced climate response difficult. Here we use results from seven model large ensembles, which have different rates of Arctic warming and sea ice loss, to assess the time of emergence of anthropogenically-forced Arctic amplification. We find that this time of emergence occurs at the turn of the century in all models, ranging across the models by a decade from 1994–2005. We also assess transient changes in this amplified signal across the 21st century and beyond. Over the 21st century, the projections indicate that the maximum Arctic warming will transition from fall to winter due to sea ice reductions that extend further into the fall. Additionally, the magnitude of the annual amplification signal declines over the 21st century associated in part with a weakening albedo feedback strength. In a simulation that extends to the 23rd century, we find that as sea ice cover is completely lost, there is little further reduction in the surface albedo and Arctic amplification saturates at a level that is reduced from its 21st century value.

Alternate History: A Synthetic Ensemble of Ocean Chlorophyll Concentrations

AbstractInternal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long‐term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different sampling of internal climate variability. We further demonstrate the validity of our statistical approach by recreating an ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long‐term trends in the presence of internal variability and find a wider range of possible trends in chlorophyll due to the sampling of internal variability in subpolar regions than in subtropical regions.

The Alaskan Summer 2019 Extreme Heat Event: The Role of Anthropogenic Forcing, and Projections of the Increasing Risk of Occurrence

AbstractExtreme heat occurred over Alaska in June–July 2019, posing risks to infrastructure, ecosystem, and human health. It is vital to improve our understanding of the causes of such events and the extent to which anthropogenic forcing may alter their likelihood and magnitude. Here, we use multiple large ensembles of climate models, comprising thousands of simulated years, to investigate these issues. Our results suggest that the presence of anthropogenic radiative forcing increased the likelihood of the 2019 extreme heat event by as much as 6%. Further we show the rate of occurrence of such an extreme heat event is likely to substantially increase in the future with increasing levels of atmospheric greenhouse gases. While uncertainty in projected climate risk from model choice leads to a broad range of future extreme heat event probabilities, some models project that with rapidly increasing levels of greenhouse gases the likelihood of such events would exceed 75% by 2090.

Characterizing unforced decadal climate variability in global climate model large ensembles

Characterizing unforced decadal climate variability in global climate model large ensembles

Changes in precipitation variability across time scales in multiple global climate model large ensembles

Anthropogenic changes in the variability of precipitation stand to impact both natural and human systems in profound ways. Precipitation variability encompasses not only extremes like droughts and floods, but also the spectrum of precipitation which populates the times between these extremes. Understanding the changes in precipitation variability alongside changes in mean and extreme precipitation is essential in unraveling the hydrological cycle’s response to warming. We use a suite of state-of-the-art climate models, with each model consisting of a single-model initial-condition large ensemble (SMILE), yielding at least 15 individual realizations of equally likely evolutions of future climate state for each climate model. The SMILE framework allows for increased precision in estimating the evolving distribution of precipitation, allowing for forced changes in precipitation variability to be compared across climate models. We show that the scaling rates of precipitation variability, the relation between the rise in global temperature and changes in precipitation variability, are markedly robust across timescales from interannual to decadal. Over mid- and high latitudes, it is very likely that precipitation is increasing across the entire spectrum from means to extremes, as is precipitation variability across all timescales, and seasonally these changes can be amplified. Model or structural uncertainty is a prevailing uncertainty especially over the Tropics and Subtropics. We uncover that model-based estimates of historical interannual precipitation variability are sensitive to the number of ensemble members used, with ‘small’ initial-condition ensembles (of less than 30 members) systematically underestimating precipitation variability, highlighting the utility of the SMILE framework for the representation of the full precipitation distribution.

Influential factors on the development of a low-enthalpy geothermal reservoir: A sensitivity study of a realistic field

A realistic deep low-enthalpy geothermal reservoir based on real data with high detail and complicated sedimentary structure is utilized to perform sensitivity analyses of the geological features influencing reservoir properties. We perform simulations using the Delft Advanced Research Terra Simulator (DARTS). Compelling numerical performance of DARTS makes it suitable for handling a large ensemble of models including efficient sensitivity and uncertainty analyses. The major finding is that shale facies, generally ignored in hydrocarbon reservoir simulations, can significantly extend the predictive lifetime of geothermal reservoirs exploited by deep well doublets. It is important to accurately account for the shale facies in the simulation, though with an additional computational overhead. The overburden layers can improve doublet performance, but the impact depends on reservoir heterogeneity. In addition, heterogeneity will also divert the flow path with even a minor shift in the well placement. The discharge rate, an essential parameter of geothermal operation strategy, inversely corresponds to the doublet lifetime but positively correlates with the energy production for studied parameter ranges. Low sensitivity of doublet lifetime to vertical-horizontal permeability ratio and permeability-porosity correlation is observed. All these systematic findings for a realistic geothermal field with characterization at unprecedented level of detail can help to provide a general guideline for forward simulation and farther improve the profitability of geothermal energy production in realistic deep geothermal reservoirs through computer-assisted modeling and optimization.

Rise in Northeast US extreme precipitation caused by Atlantic variability and climate change

Extreme precipitation (EP) in the Northeastern United States increased abruptly after 1996, coinciding with warming Atlantic sea surface temperatures (SSTs). We examine the importance of internal variability and external forcings (including anthropogenic and natural forcings) to these EP and SST increases by using the Community Earth System Model large ensembles and an optimal fingerprint method to isolate the effects of different forcings on 1929–2018 Northeast EP and North Atlantic SSTs. We find that external forcings have significantly influenced both Northeast EP and North Atlantic SSTs, with a time of detection in 2008 and 1968, respectively. Beyond SST changes attributable to internal variability of the Atlantic, anthropogenic aerosols and greenhouse gases are important drivers of SST changes, first detected in 1968 and 1983, respectively. Greenhouse gases are the only anthropogenic forcing exerting substantial influence on EP, first detected in 2008. We therefore attribute the 1996 EP shift to both unforced Atlantic variability and anthropogenic forcings.

On the Origin of Discrepancies Between Observed and Simulated Memory of Arctic Sea Ice

AbstractTo investigate the inherent predictability of sea ice and its representation in climate models, we compare the seasonal‐to‐interannual memory of Arctic sea ice as given by lagged correlations of sea‐ice area anomalies in large model ensembles (Max Planck Institute Grand Ensemble and Coupled Model Intercomparison Project phase 6) and multiple observational products. We find that state‐of‐the‐art climate models significantly overestimate the memory of pan‐Arctic sea‐ice area from the summer months into the following year. This cannot be explained by internal variability. We further show that the observed summer memory can be disentangled regionally into a reemergence of positive correlations in the perennial ice zone and negative correlations in the seasonal ice zone; the latter giving rise to the discrepancy between observations and model simulations. These findings could explain some of the predictability gap between potential and operational forecast skill of Arctic sea‐ice area identified in previous studies.