Global Mean Surface Temperature Research Articles

Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework.

Earth's temperature variability can be partitioned into internal and externally forced components. Yet, underlying mechanisms and their relative contributions remain insufficiently understood, especially on decadal to centennial timescales. Important reasons for this are difficulties in isolating internal and externally forced variability. Here, we provide a physically motivated emulation of global mean surface temperature (GMST) variability, which allows for the separation of internal and external variations. To this end, we introduce the "ClimBayes" software package, which infers climate parameters from a stochastic energy balance model (EBM) with a Bayesian approach. We apply our method to GMST data from temperature observations and 20 last millennium simulations from climate models of intermediate to high complexity. This yields the best estimates of the EBM's forced and forced + internal response, which we refer to as emulated variability. The timescale-dependent variance is obtained from spectral analysis. In particular, we contrast the emulated forced and forced + internal variance on interannual to centennial timescales with that of the GMST target. Our findings show that a stochastic EBM closely approximates the power spectrum and timescale-dependent variance of GMST as simulated by modern climate models. Small deviations at interannual timescales can be attributed to the simplified representation of internal variability and, in particular, the absence of (pseudo-)oscillatory modes in the stochastic EBM. Altogether, we demonstrate the potential of combining Bayesian inference with conceptual climate models to emulate statistics of climate variables across timescales.

A new bootstrap technique to quantify uncertainty in estimates of ground surface temperature and ground heat flux histories from geothermal data

Abstract. Estimates of the past thermal state of the land surface are crucial to assess the magnitude of current anthropogenic climate change as well as to assess the ability of Earth System Models (ESMs) to forecast the evolution of the climate near the ground, which is not included in standard meteorological records. Subsurface temperature reacts to long-term changes in surface energy balance – from decadal to millennial time scales – thus constituting an important record of the dynamics of the climate system that contributes, with low-frequency information, to proxy-based paleoclimatic reconstructions. Broadly used techniques to retrieve past temperature and heat flux histories from subsurface temperature profiles based on a singular value decomposition (SVD) algorithm were able to provide robust global estimates for the last millennium, but the approaches used to derive the corresponding 95 % confidence interval were wrong from a statistical point of view in addition to being difficult to interpret. To alleviate the lack of a meaningful framework for estimating uncertainties in past temperature and heat flux histories at regional and global scales, we combine a new bootstrapping sampling strategy with the broadly used SVD algorithm and assess its performance against the original SVD technique and another technique based on generating perturbed parameter ensembles of inversions. The new bootstrap approach is able to reproduce the prescribed surface temperature series used to derive an artificial profile. Bootstrap results are also in agreement with the global mean surface temperature history and the global mean heat flux history retrieved in previous studies. Furthermore, the new bootstrap technique provides a meaningful uncertainty range for the inversion of large sets of subsurface temperature profiles. We suggest the use of this new approach particularly for aggregating results from a number of individual profiles, and to this end, we release the programs used to derive all inversions in this study as a suite of codes labeled CIBOR v1: Codes for Inverting BORholes, version 1.

Climate change impacts on flood peaks in Britain for a range of global mean surface temperature changes

AbstractAn increase in extreme weather events is leading to increased flood occurrence and risk in many areas. Although climate mitigation strategies are being implemented, it is widely accepted that societies must adapt to climate variability and climate change. Traditional climate change impact studies have used projections for future time‐slices, often for a range of possible emissions scenarios. Recently however, there has been a move to instead consider climate change impacts relative to global mean surface temperature (GMST) change, to try to encourage action to avoid the more severe impacts from higher GMST changes. To support adaptation planning, more localised information on impacts is required. Here, data on the potential range of changes in flood peaks is generated by combining flood response surfaces and the new UK Climate Projections 2018, for every river cell on a 1 km grid across Britain, for GMST changes of 1–4.5°C. The results show significant spatial variation, with impacts typically higher in the west than the east, and generally increasing with GMST change. Some southern regions show flood peak changes accelerating with GMST change. The changes in flood peaks can be translated into changes in flood inundation and associated flood risk under alternative adaptation assumptions.

Reducing uncertainty in local temperature projections.

Planning for adaptation to climate change requires accurate climate projections. Recent studies have shown that the uncertainty in global mean surface temperature projections can be considerably reduced using historical observations. However, the transposition of these new results to the local scale is not yet available. Here, we adapt an innovative statistical method that combines the latest generation of climate model simulations, global observations, and local observations to reduce uncertainty in local temperature projections. By taking advantage of the tight links between local and global temperature, we can derive the local implications of global constraints. The model uncertainty is reduced by 30% up to 70% at any location worldwide, allowing to substantially improve the quantification of risks associated with future climate change. A rigorous evaluation of these results within a perfect model framework indicates a robust skill, leading to a high confidence in our constrained climate projections.

Global warming at near-constant tropospheric relative humidity is supported by observations

Although global warming is expected to occur at approximately constant relative humidity, the latest IPCC report remains elusive about the magnitude of observed changes in tropospheric humidity and their attribution. Here we use a quality-controlled dataset of in situ observations, global reanalyses, and a long record of global mean surface temperature to constrain both recent and future changes in global mean total precipitable water. Most state-of-the-art global climate models tend to exaggerate the projected atmospheric moistening, in line with their overestimation of global warming and of the sensitivity of atmospheric humidity to both anthropogenic greenhouse gases and aerosols across the 20th century. A 39% narrowing in the range of the projections is obtained after applying the observational constraints, with a best-guess estimate of + 7% per °C of global warming. This finding provides further evidence of a substantial intensification of the global water cycle as long as global warming continues.

Climate change threatens the microbiological stability of non-refrigerated foods

Most studies on the impact of climate change on foods focus on the consequences to security and safety. In the present study we provide scientific evidence on an overlooked aspect of climate change related to the microbiological stability of foods. Most microbiologically stable processed foods are contaminated with spores of thermophilic spoilage bacteria which are highly heat-resistant and can survive thermal processing. Current temperatures during distribution and storage in temperate climates do not allow growth of thermophilic bacteria to levels that can cause spoilage, ensuring their microbiological stability. Our findings suggest that the latter limiting condition can be eliminated by global warming. By assessing different global warming scenarios for 38 European cities in a case study with canned milk, we show that failing to limit the increase of global mean surface temperature below 2 °C can lead to a very high risk of spoilage and subsequently cause a collapse of the shelf-stable food chain.

The impact of long-term memory on the climate response to greenhouse gas emissions

Global warming exerts a strong impact on the Earth system. Despite recent progress, Earth System Models still project a large range of possible warming levels. Here we employ a generalized stochastic climate model to derive a response operator which computes the global mean surface temperature given specific forcing scenarios to quantify the impact of past emissions on current warming. This approach enables us to systematically separate between the “forcing-induced direct” and the “memory-induced indirect” trends. Based on historical records, we find that the direct-forcing-response is weak, while we attribute the major portion of the observed global warming trend to the indirect-memory responses that are accumulated from past emissions. Compared to CMIP6 simulations, our data-driven approach projects lower global warming levels over the next few decades. Our results suggest that CMIP6 models may have a higher transient climate sensitivity than warranted from the observational record, due to them having larger long-term memory than observed.

Major Contribution of Halogenated Greenhouse Gases to Global Surface Temperature Change

This paper aims to better understand why there was a global warming pause in 2000–2015 and why the global mean surface temperature (GMST) has risen again in recent years. We present and statistically analyze substantial time-series observed datasets of global lower-stratospheric temperature (GLST), troposphere–stratosphere temperature climatology, global land surface air temperature, GMST, sea ice extent (SIE) and snow cover extent (SCE), combined with modeled calculations of GLSTs and GMSTs. The observed and analyzed results show that GLST/SCE has stabilized since the mid-1990s with no significant change over the past two and a half decades. Upper-stratospheric warming at high latitudes has been observed and GMST or global land surface air temperature has reached a plateau since the mid-2000s with the removal of natural effects. In marked contrast, continued drastic warmings at the coasts of polar regions (particularly Russia and Alaska) are observed and well explained by the sea-ice-loss warming amplification mechanism. The calculated GMSTs by the parameter-free quantum-physics warming model of halogenated greenhouse gases (GHGs) show excellent agreement with the observed GMSTs after the natural El Niño southern oscillation and volcanic effects are removed. These results have provided strong evidence for the dominant warming mechanism of anthropogenic halogenated GHGs. The results also call for closer scrutiny of the assumptions made in current climate models.

Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020

Following the end of the decadal-warming-hiatus in 2016, the global mean surface temperature (GMST) abruptly showed a 3 yr warming slowdown and peaked again in 2020, overturning the conventional concept that highest GMST occurs with strong El Niño. The high GMST in 2016 was controlled by secular trend and annual variability (ANV). However, the dominator of the sharp GMST rise in 2020 was SCT alone because the ANVs in different seasons canceled each other out in 2020, contributing little to the annual mean GMST. By analyzing the two hottest years, 2016 and 2020, we identified that seasonally varying ANVs are mainly located in Eurasia, North America, the Arctic Ocean, and the tropical eastern Pacific Ocean. Dominance by surface temperatures over the four crucial regions on the subseasonal-to-seasonal (S2S) GMST variations was also observed in 73% of the years during 1982–2021, indicating a potential opportunity to improve the S2S GMST forecast.

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). II. Moist Cases—The Two Waterworlds

To identify promising exoplanets for atmospheric characterization and to make the best use of observational data, a thorough understanding of their atmospheres is needed. Three-dimensional general circulation models (GCMs) are one of the most comprehensive tools available for this task and will be used to interpret observations of temperate rocky exoplanets. Due to parameterization choices made in GCMs, they can produce different results, even for the same planet. Employing four widely used exoplanetary GCMs—ExoCAM, LMD-G, ROCKE-3D, and the UM—we continue the TRAPPIST-1 Habitable Atmosphere Intercomparison by modeling aquaplanet climates of TRAPPIST-1e with a moist atmosphere dominated by either nitrogen or carbon dioxide. Although the GCMs disagree on the details of the simulated regimes, they all predict a temperate climate with neither of the two cases pushed out of the habitable state. Nevertheless, the intermodel spread in the global mean surface temperature is nonnegligible: 14 K and 24 K in the nitrogen- and carbon dioxide-dominated case, respectively. We find substantial intermodel differences in moist variables, with the smallest amount of clouds in LMD-Generic and the largest in ROCKE-3D. ExoCAM predicts the warmest climate for both cases and thus has the highest water vapor content and the largest amount and variability of cloud condensate. The UM tends to produce colder conditions, especially in the nitrogen-dominated case due to a strong negative cloud radiative effect on the day side of TRAPPIST-1e. Our study highlights various biases of GCMs and emphasizes the importance of not relying solely on one model to understand exoplanet climates.

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). I. Dry Cases—The Fellowship of the GCMs

With the commissioning of powerful, new-generation telescopes such as the James Webb Space Telescope (JWST) and the ground-based Extremely Large Telescopes, the first characterization of a high molecular weight atmosphere around a temperate rocky exoplanet is imminent. Atmospheric simulations and synthetic observables of target exoplanets are essential to prepare and interpret these observations. Here we report the results of the first part of the TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) project, which compares 3D numerical simulations performed with four state-of-the-art global climate models (ExoCAM, LMD-Generic, ROCKE-3D, Unified Model) for the potentially habitable target TRAPPIST-1e. In this first part, we present the results of dry atmospheric simulations. These simulations serve as a benchmark to test how radiative transfer, subgrid-scale mixing (dry turbulence and convection), and large-scale dynamics impact the climate of TRAPPIST-1e and consequently the transit spectroscopy signature as seen by JWST. To first order, the four models give results in good agreement. The intermodel spread in the global mean surface temperature amounts to 7 K (6 K) for the N2-dominated (CO2-dominated) atmosphere. The radiative fluxes are also remarkably similar (intermodel variations less than 5%), from the surface (1 bar) up to atmospheric pressures ∼5 mbar. Moderate differences between the models appear in the atmospheric circulation pattern (winds) and the (stratospheric) thermal structure. These differences arise between the models from (1) large-scale dynamics, because TRAPPIST-1e lies at the tipping point between two different circulation regimes (fast and Rhines rotators) in which the models can be alternatively trapped, and (2) parameterizations used in the upper atmosphere such as numerical damping.

On the Effects of Mixed and Deep Ocean Layers on Climate Change and Variability

The ocean, one of the five major components of the Earth’s climate system, plays a key role in climate-forming processes, affecting its change and variability. The ocean influences climate over a wide range of time–space scales. To explore the climate, its components, interactions between them and, in particular, the effect of the ocean on weather and climate, researchers commonly use extremely complex mathematical models of the climate system that describe the atmospheric and ocean general circulations. However, this class of climate models requires enormous human and computing resources to simulate the climate system itself and to analyze the output results. For simple climate models, such as energy balance and similar models, the computational cost is insignificant, which is why these models represent a test tool to mimic a complex climate system and obtaining preliminary estimates of the influence of various internal and external factors on climate, its change and variability. The global mean surface temperature (GMST) and its fluctuations in time serve as critical indicators of changes in the climate system state. We apply a simple two-box ocean model to explore the effect of mixed and deep ocean layers on climate-forming processes and especially on climate change and variability. The effect of mixed and deep ocean layers on GMST is parameterized via the layers’ effective heat capacities and heat exchange between layers. For the listed parameters, the sensitivity functions were derived numerically and analytically, allowing one to obtain an idea of how the mixed and deep ocean layers affect climate change and variability. To study climate change, a deterministic version of the model was used with radiative forcing parameterized by both stepwise and linear functions. In climate variability experiments, a stochastic version of the model was applied in which the radiative forcing is considered as a delta-correlated random process.

Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models.

Emulators of Earth System Models (ESMs) are complementary to ESMs by providing climate information at lower computational costs. Thus far, the emulation of spatially resolved climate extremes has only received limited attention, even though extreme events are one of the most impactful aspects of climate change. Here, we propose a method for the emulation of local annual maximum temperatures, with a focus on reproducing essential statistical properties such as correlations in space and time. We test different emulator configurations and find that driving the emulations with global mean surface temperature offers an optimal compromise between model complexity and performance. We show that the emulations can mimic the temporal evolution and spatial patterns of the underlying climate model simulations and are able to reproduce their natural variability. The general design and the good performance for annual maximum temperatures suggest that the proposed methodology can be applied to other climate extremes.

Anticipated impacts of climate change on women's health: Abackground primer.

Due to human activities, atmospheric greenhouse gas levels have increased dramatically, leading to an increase in the global mean surface temperature by 1.1° Celsius. Unless we can achieve a significant reduction in emissions, the global mean surface temperature will continue to rise to a dangerous level. Adverse outcomes of this warming will include extreme weather events, a deterioration of food, water and air quality, decreased food security, and an increase in vector-borne infectious disease. Political and economic instability as well as mass population migration will result in reduced access to healthcare resources. Mitigation of and adaptation to climate change will be key determinants of humanity's survival in the face of this existential crisis. Women will be more adversely affected by climate change than men, and pregnant persons will be particularly vulnerable. Particular differential impacts on women include higher heat and particulate-related morbidity and mortality; pregnancy risks including preterm birth, fetal growth lag, hypertensive disorders; and mental health impacts. To prepare for the climate crisis, it is imperative for women's healthcare providers to assist their patients through political advocacy, provide family planning services, and focus on nutrition and lifestyle counseling.

Neutral Mode Dominates the Forced Global and Regional Surface Temperature Response in the Past and Future

AbstractUsing a large suite of Green's function perturbation experiments, we construct the emergent dynamical operator for surface temperature and extract the most excitable mode. The leading mode turns out to be the most dominant mode excited by the climate forcing of doubling CO2, alone capturing 56% of the total spatial variances of the surface temperature response. The pattern of the leading mode is partly organized by the atmospheric annular modes in both hemispheres, and further modulated by the feedbacks from ocean circulations. Though derived from a single model, the leading mode can capture the spatio‐temporal variabilities of the global mean surface temperature over the past century in both the CMIP6 model simulations and observations. Moreover, the leading mode is most efficiently excited by radiative forcings from the midlatitude bands centered around 45°N and 25°S, where most human activities take place, suggesting the potency of human influence on the climate change.

Climate nonlinearities: selection, uncertainty, projections, and damages

Climate projections are uncertain; this uncertainty is costly and impedes progress on climate policy. This uncertainty is primarily parametric (what numbers do we plug into our equations?), structural (what equations do we use in the first place?), and due to internal variability (natural variability intrinsic to the climate system). The former and latter are straightforward to characterise in principle, though may be computationally intensive for complex climate models. The second is more challenging to characterise and is therefore often ignored. We developed a Bayesian approach to quantify structural uncertainty in climate projections, using the idealised energy-balance model representations of climate physics that underpin many economists’ integrated assessment models (IAMs) (and therefore their policy recommendations). We define a model selection parameter, which switches on one of a suite of proposed climate nonlinearities and multidecadal climate feedbacks. We find that a model with a temperature-dependent climate feedback is most consistent with global mean surface temperature observations, but that the sign of the temperature-dependence is opposite of what Earth system models suggest. This difference of sign is likely due to the assumption tha the recent pattern effect can be represented as a temperature dependence. Moreover, models other than the most likely one contain a majority of the posterior probability, indicating that structural uncertainty is important for climate projections. Indeed, in projections using shared socioeconomic pathways similar to current emissions reductions targets, structural uncertainty dwarfs parametric uncertainty in temperature. Consequently, structural uncertainty dominates overall non-socioeconomic uncertainty in economic projections of climate change damages, as estimated from a simple temperature-to-damages calculation. These results indicate that considering structural uncertainty is crucial for IAMs in particular, and for climate projections in general.

An Assessment of the Impact of Temperature Rise Due to Climate Change on Asphalt Pavement in China

In the global warming context, understanding the impact of temperature rise on asphalt pavement is the basis for making adaptation strategies. An approach based on historical climate data and pavement performance models was employed to assess the potential impact of temperature rise on asphalt pavement in China. It is shown that permanent deformation is one sensitive aspect of asphalt pavement performance, which increased on average by 20.70% from 1992 to 2019. Another one is low-temperature cracking, which decreased by 20.99% from 1970 to 1997, but has remained almost unchanged since 1997. Global mean surface temperature anomalies of 1.5 °C and 2.0 °C will increase the permanent deformation of asphalt pavement by 18.63% and 36.71%, respectively, compared to 2019. Global warming is bringing serious challenges to the structure and material design of asphalt pavement due to the increasing service temperature range.

Increased Indian Ocean-North Atlantic Ocean warming chain under greenhouse warming

Over the past half a century, both the Indian Ocean (IO) and the North Atlantic Ocean (NA) exhibit strong warming trends like a global mean surface temperature (SST). Here, we show that not only simply as a result of increased greenhouse gases, but the IO-NA interaction through atmospheric teleconnection boosts up their warming trends. Climate model simulations demonstrate that the IO warming increases the NA SST by enhancing the longwave radiation through atmospheric teleconnection, subsequently, the warmer NA SST-induced atmospheric teleconnection leads to IO warming by reducing evaporative cooling with weakened surface winds. This two-way interaction (i.e., IO-NA warming chain) acts as positive feedback that reinforces warming over both ocean basins. The Pacific Ocean is partly involved in this warming chain as a modulator in an interdecadal timescale. These results highlight the importance of understanding ocean-basin interactions that may provide a more accurate future projection of warming.

A Computational Approach to a Mathematical Model of Climate Change Using Heat Sources and Diffusion

The present work aims to extend the climate change energy balance models using a heat source. An ordinary differential equations (ODEs) model is extended to a partial differential equations (PDEs) model using the effects of diffusion over the spatial variable. In addition, numerical schemes are presented using the Taylor series expansions. For the climate change model in the form of ODEs, a comparison of the presented scheme is made with the existing Trapezoidal method. It is found that the presented scheme converges faster than the existing scheme. Also, the proposed scheme provides fewer errors than the existing scheme. The PDEs model is also solved with the presented scheme, and the results are displayed in the form of different graphs. The impact of the climate feedback parameter, the heat uptake parameter of the deep ocean, and the heat source parameter on global mean surface temperature and deep ocean temperature is also portrayed. In addition, these recently developed techniques exhibit a high level of predictability. Doi: 10.28991/CEJ-2022-08-07-04 Full Text: PDF

Climate Outcomes of Earth-similar Worlds as a Function of Obliquity and Rotation Rate

A set of simulations with a 3D global climate model are performed to investigate the roles of obliquity and rotation period in the habitability of Earthlike exoplanets. The simulations cover the obliquity–rotation parameter space, from 0° to 90° in obliquity and 1–128 days in rotation period. The simulated global mean temperatures are warmest at 45° obliquity with fast rotations, due to the modification of the greenhouse effect from the spatial redistribution of clouds and water vapor. The slow-moving insolation–cloud mechanism, previously found in simulations with slow rotations and zero obliquity, also produces a cooling trend from intermediate obliquity to high obliquity, with the coldest climate occurring at 90° obliquity for all rotation periods. At low obliquities and fast rotation, persistent snow and sea ice can form, producing cooler temperatures. A Climate Habitability metric is defined, based on temperature and precipitation, which compares well with observations when applied to a simulation using Earth’s obliquity and rotation. Over a wider range of obliquity and rotation period, the Climate Habitability ranges from 10% to 70% of the terrestrial area. Overall, the simulated global mean surface temperature shows a much larger spread across the range of simulated rotation periods at 45° obliquity compared to 0° obliquity. Therefore, we conclude that 3D exoplanet simulations using intermediate obliquities (e.g., 45°) instead of 0° will reveal a wider range of possible climate conditions for specific orbital configurations. In addition, Earth’s climate habitability can increase by 25% if the obliquity increases from 23.°5 to 45°.