Near-term tropical cyclone risk and coupled Earth system model biases.

Most current climate models predict that the equatorial Pacific will evolve under greenhouse gas-induced warming to a more El Niño-like state over the next several decades, with a reduced zonal sea surface temperature gradient and weakened atmospheric Walker circulation. Yet, observations over the last 50 y show the opposite trend, toward a more La Niña-like state. Recent research provides evidence that the discrepancy cannot be dismissed as due to internal variability but rather that the models are incorrectly simulating the equatorial Pacific response to greenhouse gas warming. This implies that projections of regional tropical cyclone activity may be incorrect as well, perhaps even in the direction of change, in ways that can be understood by analogy to historical El Niño and La Niña events: North Pacific tropical cyclone projections will be too active, North Atlantic ones not active enough, for example. Other perils, including severe convective storms and droughts, will also be projected erroneously. While it can be argued that these errors are transient, such that the models' responses to greenhouse gases may be correct in equilibrium, the transient response is relevant for climate adaptation in the next several decades. Given the urgency of understanding regional patterns of climate risk in the near term, it would be desirable to develop projections that represent a broader range of possible future tropical Pacific warming scenarios-including some in which recent historical trends continue-even if such projections cannot currently be produced using existing coupled earth system models.

CO2-forced Late Miocene cooling and ecosystem reorganizations in East Asia.

In parallel with pronounced cooling in the oceans, vast areas of the continents experienced enhanced aridification and restructuring of vegetation and animal communities during the Late Miocene. Debate continues over whether pCO2-induced global cooling was the primary driver of this climate and ecosystem upheaval on land. Here we present an 8 to 5 Ma land surface temperatures (LST) record from East Asia derived from paleosol carbonate clumped isotopes and integrated with climate model simulations. The LST cooled by ~7 °C between 7.5 and 5.7 Ma, followed by rapid warming across the Miocene-Pliocene transition (5.5 to 5 Ma). These changes occurred synchronously with variations in alkenone and Mg/Ca-based sea surface temperatures and with hydroclimate and ecosystem shifts in East Asia, highlighting a global climate forcing mechanism. Our modeling experiments additionally demonstrate that pCO2-forced cooling would have altered moisture transfer and pathways and driven extensive aridification in East Asia. We, thus, conclude that the East Asian hydroclimate and ecosystem shift was primarily controlled by pCO2-forced global cooling between 8 and 5 Ma.

Exceptional stratospheric contribution to human fingerprints on atmospheric temperature.

In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO2 should warm Earth's troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth's surface (S25 - 50). To date, however, S25 - 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a "fingerprint" study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S25 - 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S25 - 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S25 - 50 signal and noise patterns are accompanied by large cooling of S25 - 50 (1 to 2[Formula: see text]C over 1986 to 2022) and low S25 - 50 noise levels. Our results explain why extending "vertical fingerprinting" to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth's atmosphere.

Estimated cloud-top entrainment index explains positive low-cloud-cover feedback.

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming: The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using state-of-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes.

Are general circulation models obsolete?

Traditional general circulation models, or GCMs-that is, three-dimensional dynamical models with unresolved terms represented in equations with tunable parameters-have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail and the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.

The latitudinal temperature gradient and its climate dependence as inferred from foraminiferal δ18O over the past 95 million years.

SignificanceThe temperature difference between low and high latitudes is one measure of the efficiency of the global climate system in redistributing heat and is used to test the ability of models to represent the climate system through time. Here, we show that the latitudinal temperature gradient has exhibited a consistent inverse relationship with global mean sea-surface temperature for at least the past 95 million years. Our results help reduce conflicts between climate models and empirical estimates of temperature and argue for a fundamental consistency in the dynamics of heat transport and radiative transfer across vastly different background states.

Observational evidence that cloud feedback amplifies global warming.

Global warming drives changes in Earth's cloud cover, which, in turn, may amplify or dampen climate change. This "cloud feedback" is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)-the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m-2⋅K-1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

Addressing partial identification in climate modeling and policy analysis.

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research.

Modeling the species occurrence probability and response of climate change on Himalayan Somalata plant under different Shared Socioeconomic Pathways.

In this study, the current distribution probability of Ephedra gerardiana (Somalata), a medicinally potent species of the Himalayas, was assessed, and its spatial distribution change was forecasted until the year 2100 under three Shared Socioeconomic Pathways. Here, we used the maximum entropy model (MaxEnt) on 274 spatially filtered occurrence data points accessed from GBIF and other publications, and 19 bioclimatic variables were used as predictors against the probability assessment. The area under the curve, Continuous Boyce Index, True Skill Statistics, and kappa values were used to evaluate and validate the model. It was observed that the SSP5-8.5, a fossil fuel-fed scenario, saw a maximum habitat decline for E. gerardiana driving its niche towards higher altitudes. Nepal Himalayas witnessed a maximum decline in suitable habitat for the species, whereas it gained area in Bhutan. In India, regions of Himachal Pradesh, Uttarakhand, Jammu and Kashmir, and Sikkim saw a maximum negative response to climate change by the year 2100. Mean annual temperature, isothermality, diurnal temperature range, and precipitation seasonality are the most influential variables isolated by the model that contribute in defining the species' habitat. The results provide evidence of the effects of climate change on the distribution of endemic species in the study area under different scenarios of emissions and anthropogenic coupling. Certainly, the area of consideration encompasses several protected areas, which will become more vulnerable to increased variability of climate, and regulating their boundaries might become a necessary step to conserve the regions' biodiversity in the future.

Microbially mediated climate feedbacks from wetland ecosystems.

Wetlands are crucial nodes in the carbon cycle, emitting approximately 20% of global CH4 while also sequestering 20%-30% of all soil carbon. Both greenhouse gas fluxes and carbon storage are driven by microbial communities in wetland soils. However, these key players are often overlooked or overly simplified in current global climate models. Here, we first integrate microbial metabolisms with biological, chemical, and physical processes occurring at scales from individual microbial cells to ecosystems. This conceptual scale-bridging framework guides the development of feedback loops describing how wetland-specific climate impacts (i.e., sea level rise in estuarine wetlands, droughts and floods in inland wetlands) will affect future climate trajectories. These feedback loops highlight knowledge gaps that need to be addressed to develop predictive models of future climates capturing microbial contributions. We propose a roadmap connecting environmental scientific disciplines to address these knowledge gaps and improve the representation of microbial processes in climate models. Together, this paves the way to understand how microbially mediated climate feedbacks from wetlands will impact future climate change.

African climate response to orbital and glacial forcing in 140,000-y simulation with implications for early modern human environments.

A climate/vegetation model simulates episodic wetter and drier periods at the 21,000-y precession period in eastern North Africa, the Arabian Peninsula, and the Levant over the past 140,000 y. Large orbitally forced wet/dry extremes occur during interglacial time, ∼130 to 80 ka, and conditions between these two extremes prevail during glacial time, ∼70 to 15 ka. Orbital precession causes high seasonality in Northern Hemisphere (NH) insolation at ∼125, 105, and 83 ka, with stronger and northward extended summer monsoon rains in North Africa and the Arabian Peninsula and increased winter rains in the Mediterranean Basin. The combined effects of these two seasonally distinct rainfall regimes increase vegetation and narrow the width of the Saharan-Arabian desert and semidesert zones. During the opposite phase of the precession cycle (∼115, 95, and 73 ka), NH seasonality is low, and decreased summer insolation and increased winter insolation cause monsoon and storm track rains to decrease and the width of the desert zone to increase. During glacial time (∼70 to 15 ka), forcing from large ice sheets and lowered greenhouse gas concentrations combine to increase winter Mediterranean storm track precipitation; the southward retreat of the northern limit of summer monsoon rains is relatively small, thereby limiting the expansion of deserts. The lowered greenhouse gas concentrations cause the near-equatorial zone to cool and reduce convection, causing drier climate with reduced forest cover. At most locations and times, the simulations agree with environmental observations. These changing regional patterns of climate/vegetation could have influenced the dispersal of early humans through expansions and contractions of well-watered corridors.

A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network.

In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.

Cold Temperature Limits to Biodiesel Use under Present and Future Climates in North America.

Cold weather operability is sometimes a limiting factor in the use of biodiesel blends for transportation. Regional temperature variability can therefore influence biodiesel adoption, with potential economic and environmental implications. This study assesses present and future biodiesel cold weather operability limits in North America according to temperature data from weather stations, atmospheric reanalysis, and global climate models with highest resolution over Ontario, Canada. Future temperature projections using the RCP8.5 climate change scenario show increases in the potential duration for certain seasonal fuel blends. For example, biodiesel blends whose cloud point temperature is -9 °C may expand their duration by 3-7% in North America for nonwinter seasons according to projections for 2040. Cloud point specifications among supply orbits in Ontario increase up to +6 °C during nonwinter seasons, with most increases observed in Fall and Spring. In winter, however, the modeling suggests no change in Ontario cloud point specifications because the coldest temperatures by mid-century are not significantly warmer than the past climate normal according to our climate simulations. This study provides a quantitative analysis on biodiesel usage scenarios under a changing climate, including Ontario region geographic temperature clusters that could prove useful for biodiesel blend-related decision-making.

A round Earth for climate models.

Sunlight drives the Earth's weather, climate, chemistry, and biosphere. Recent efforts to improve solar heating codes in climate models focused on more accurate treatment of the absorption spectrum or fractional clouds. A mostly forgotten assumption in climate models is that of a flat Earth atmosphere. Spherical atmospheres intercept 2.5 W⋅m-2 more sunlight and heat the climate by an additional 1.5 W⋅m-2 globally. Such a systematic shift, being comparable to the radiative forcing change from preindustrial to present, is likely to produce a discernible climate shift that would alter a model's skill in simulating current climate. Regional heating errors, particularly at high latitudes, are several times larger. Unlike flat atmospheres, constituents in a spherical atmosphere, such as clouds and aerosols, alter the total amount of energy received by the Earth. To calculate the net cooling of aerosols in a spherical framework, one must count the increases in both incident and reflected sunlight, thus reducing the aerosol effect by 10 to 14% relative to using just the increase in reflected. Simple fixes to the current flat Earth climate models can correct much of this oversight, although some inconsistencies will remain.

An Integrated Biosecurity Risk Assessment Model (IBRAM) For Evaluating the Risk of Import Pathways for the Establishment of Invasive Species.

An important aspect of analyzing the risk of unwanted organisms establishing in an area is understanding the pathways by which they arrive. Evaluating the risks of these pathways requires use of data from multiple sources, which frequently are uncertain. To address the needs of agencies responsible for biosecurity operations, we present an Integrated Biosecurity Risk Assessment Model (IBRAM) for evaluating the risk of establishment and dispersal of invasive species along trade pathways. The IBRAM framework consists of multiple linked models which describe pest entry into the country, escape along trade pathways, initial dispersal into the environment, habitat suitability, probabilities of establishment and spread, and the consequences of these invasions. Bayesian networks (BN) are used extensively to model these processes. The model includes dynamic BN components and geographic data, resulting in distributions of output parameters over spatial and temporal axes. IBRAM is supported by a web-based tool that allows users to run the model on real-world pest examples and investigate the impact of alternative risk management scenarios, to explore the effect of various interventions and resource allocations. Two case studies are provided as examples of how IBRAM may be used: Queensland fruit fly (Bactrocera tryoni) (Diptera: Tephritidae) and brown marmorated stink bug (Halyomorpha halys) (Hemiptera: Pentatomidae) are unwanted organisms with the potential to invade Aotearoa New Zealand, and IBRAM has been influential in evaluating the efficacy of pathway management to mitigate the risk of their establishment in the country.

Damage Functions and the Social Cost of Carbon: Addressing Uncertainty in Estimating the Economic Consequences of Mitigating Climate Change.

Mitigating the effects of human-induced climate change requires the reduction of greenhouse gases. Policymakers must balance the need for mitigation with the need to sustain and develop the economy. To make informed decisions regarding mitigation strategies, policymakers rely on estimates of the social cost of carbon (SCC), which represents the marginal damage from increased emissions; the SCC must be greater than the marginal abatement cost for mitigation to be economically desirable. To determine the SCC, damage functions translate projections of carbon and temperature into economic losses. We examine the impact that four damage functions commonly employed in the literature have on the SCC. Rather than using an economic growth model, we convert the CO2 pathways from the Representative Concentration Pathways (RCPs) into temperature projections using a three-layer, energy balance model and subsequently estimate damages under each RCP using the damage functions. We estimate marginal damages for 2020-2100, finding significant variability in SCC estimates between damage functions. Despite the uncertainty in choosing a specific damage function, comparing the SCC estimates to estimates of marginal abatement costs from the Shared Socioeconomic Pathways (SSPs) indicates that reducing emissions beyond RCP6.0 is economically beneficial under all scenarios. Reducing emissions beyond RCP4.5 is also likely to be economically desirable under certain damage functions and SSP scenarios. However, future work must resolve the uncertainty surrounding the form of damage function and the SSP estimates of marginal abatement costs to better estimate the economic impacts of climate change and the benefits of mitigating it.

Bioclimatic zoning for dairy cows in Brazil by statistical modeling.

BACKGROUND: Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle-producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C) and relative humidity (%, RH) data from a 30-year historical series (1989-2019) collected by the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform were used. The Temperature and Humidity Index (THI) was determined for the hottest and coldest months. Milk production losses due to climate factors in the Midwest of Brazil for two daily production levels, 10 kg Milk (PL10) and 25 kg Milk (PL25), were estimated. RESULTS: The Midwest presented three THI classifications throughout the year: 'normal', 'alert', and 'critical alert'. The entire Midwest region was classified as 'normal' (THI < 70) between autumn and winter. The decrease in milk production (DMP) during the autumn and winter presented no loss for both production levels (PL10 and PL25). CONCLUSION: On the other hand, a 1 to 2 kg reduction in milk production was observed for cows with a PL25 production level between spring and summer in the southern Midwest region, while cows with a PL10 production level showed no reduction in milk production. Only the cities of Sinop and Cuiabá did not present a 'critical alert' during spring/summer for the risk of heat stress. © 2021 Society of Chemical Industry.

Physics-informed machine learning: case studies for weather and climate modelling.

Machine learning (ML) provides novel and powerful ways of accurately and efficiently recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio-temporal evolution of weather and climate processes. Off-the-shelf ML models, however, do not necessarily obey the fundamental governing laws of physical systems, nor do they generalize well to scenarios on which they have not been trained. We survey systematic approaches to incorporating physics and domain knowledge into ML models and distill these approaches into broad categories. Through 10 case studies, we show how these approaches have been used successfully for emulating, downscaling, and forecasting weather and climate processes. The accomplishments of these studies include greater physical consistency, reduced training time, improved data efficiency, and better generalization. Finally, we synthesize the lessons learned and identify scientific, diagnostic, computational, and resource challenges for developing truly robust and reliable physics-informed ML models for weather and climate processes. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Striking stationarity of large-scale climate model bias patterns under strong climate change.

Because all climate models exhibit biases, their use for assessing future climate change requires implicitly assuming or explicitly postulating that the biases are stationary or vary predictably. This hypothesis, however, has not been, and cannot be, tested directly. This work shows that under very large climate change the bias patterns of key climate variables exhibit a striking degree of stationarity. Using only correlation with a model's preindustrial bias pattern, a model's 4xCO2 bias pattern is objectively and correctly identified among a large model ensemble in almost all cases. This outcome would be exceedingly improbable if bias patterns were independent of climate state. A similar result is also found for bias patterns in two historical periods. This provides compelling and heretofore missing justification for using such models to quantify climate perturbation patterns and for selecting well-performing models for regional downscaling. Furthermore, it opens the way to extending bias corrections to perturbed states, substantially broadening the range of justified applications of climate models.

Deep learning to represent subgrid processes in climate models.

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.