Global Mean Temperature Research Articles

Time-heterogeneous impacts of global temperature cycle on world business cycle

ABSTRACT We study the statistical relationship between the world business cycle and the global temperature cycle. To amplify the signal-to-noise ratio, we estimate a two-state latent dynamic process from the original data using the endogenous regime-switching methodology. Subsequently, we apply a time-varying structural VAR analysis to identify the time-heterogeneous relationship between the extracted latent factors. Our findings provide empirical evidence that the global mean temperature cycle has a negative impact on the world business cycle during super El Niño periods, which are characterized by relatively high temperature variance records given the past information.

Enhanced sulfide burial in low-oxygen aquatic environments could offset the carbon footprint of aquaculture production.

Carbon removal from the atmosphere is needed to keep global mean temperature increases below 2 °C. Here, we develop a model to explore how alkalinity production through enhanced iron sulfide formation in low-oxygen aquatic environments, such as aquaculture systems, could offer a cost-effective means of CO2 removal. We show that enhanced sulfide burial through the supply of reactive iron to surface sediments may be able to capture up to a hundred million tonnes of CO2 per year, particularly in countries with the highest number of fish farms, such as China and Indonesia. These efforts could largely offset the carbon footprint associated with their aquaculture industry. Enhanced sulfide burial could directly benefit both fish farms and surrounding ecosystems by removing toxic sulfide from aquatic systems, providing an addition to durable global CO2 removal markets and a path towards large-scale, carbon-neutral aquatic food production.

Root physiological and morphology processes co-regulate the growth of Chinese-fir saplings in response to warming and precipitation reduction in the sub-tropical regions

Subtropical China is projected to experience elevated temperature greater than the mean global temperature increase and is accompanied by reduced precipitation. The plasticity of roots to changing environment strongly influences ecosystem feedbacks to climate change. However, knowledge gaps on the individual and combined effects of warming and precipitation reduction on root systems hinder our ability to accurately predict the growth and adaptability of forests under future climate change. To examine the effects of warming (W) and precipitation reduction (P) on roots physiology and morphology of Chinese-fir saplings, we used a randomized complete block design with factorial soil warming (ambient, ambient + 5℃) and precipitation reduction (ambient, ambient-50%) treatments. A full excavation method was adopted to obtain roots, then we measured the root physiology (osmoregulatory substances, oxidant substances, protective enzymes, endogenous hormones), morphology (specific root length, SRL; surface root area, SRA; root tissue density, RTD). The content of carbon and nitrogen, isotopes (δ13C and δ15N); soil temperature, soil moisture and sapling growth were also measured. We found that compared with the control, W decreased the abscisic acid (IAA) content; P increased the contents of hydrogen peroxide (H2O2) and proline (Pro), and decreased the contents of IAA and cytokinin (CTK); warming plus precipitation reduction (WP) increased the Pro content, and decreased the contents of IAA and CTK. In addition, the effects of W and P on root morphology varied with soil depth and root diameter class. W, P, and WP all increased fine root SRL and SRA in deep soil. Warming and precipitation reduction could affect physiological traits (e.g. non-enzymatic substances and antioxidant enzymes) and subsequently morphological traits via influencing soil environment and root tissue chemistry. Collectively, the results indicated that Chinese-fir saplings responded to warming and precipitation reduction by comprehensive regulation of the non-enzymatic substances (e.g., osmotic substances and endogenous hormones) of fine roots and changing root morphological characteristics in deep soil.

New multivariate models for the estimation of earth’s albedo and its variation with meteorological parameters over Kano, Nigeria

The study of what happens to sunlight as it goes through the atmosphere is vital to many fields of science because over time, reflection, scattering, and absorption in the atmosphere have changed the amount of solar radiation that reaches the Earth's surface. This study examined the variation in albedo over Kano, situated in the Sahelian climatic zone of Nigeria. The study's data, which covered the years 1984–2022, totaled thirty-nine, was obtained from the National Aeronautics and Space Administration (NASA). Utilizing monthly average daily measured meteorological data on global solar radiation, wind speed, mean temperature, surface pressure, and relative humidity, eleven models of three categories based on two, three, and four variable regression models were developed. Five validation indicators were used to verify the regression models: the t-test, coefficient of determination (R2), mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE). The developed models that were considered most appropriate for estimating surface albedo were ranked; the three-variable regression model ALB7, which included the wind speed, relative humidity, and mean temperature, outperformed the previously employed generalized method from literature in estimating surface albedo over Kano. The variation of measured albedo with meteorological parameters depicts a direct relationship for clearness index and wind speed; an inverse relationship was observed for surface pressure. Mean temperature and surface pressure exhibit an inverse relationship during the wet season and vice versa for the dry season.

Observed carbon decoupling of subnational production insufficient for net-zero goal by 2050

Historically, economic growth has been closely coupled to carbon emissions responsible for climate change, but to stabilize global mean temperature, net-zero carbon emissions are necessary. Some economies have begun to reduce emissions while continuing to grow, but this decoupling is not fast enough to achieve global climate targets. Subnational climate actions seem to be crucial for the achievement of these targets. Here, we uncover the effectiveness of subnational efforts by estimating decoupling rates and CO2 emission intensities over the last three decades for over 1,500 subnational regions, encompassing 85% of global emissions, using global data on reported economic output and gridded production-based emissions. Thirty percent of regions with available data have fully decoupled, with higher-income and historically carbon-intensive regions exhibiting higher rates of decoupling and declining emission intensity. Countries of the Organization for Economic Co-operation and Development with greater spending on subnational climate actions show higher decoupling rates, as do subnational regions in EU countries where climate policies have been implemented, highlighting the effectiveness of subnational policies. Moreover, subnational analysis reveals greater variance of decoupling rates within national boundaries than between them and that countries with weaker governance typically show higher variance of decoupling within their borders. If recent rates of production-based carbon decoupling continue, less than half of subnational regions would reach net-zero before 2050, even when accounting for observed acceleration via socioeconomic development and assuming no interregional carbon leakage.

Persistently active El Niño–Southern Oscillation since the Mesozoic

The El Niño-Southern Oscillation (ENSO), originating in the central and eastern equatorial Pacific, is a defining mode of interannual climate variability with profound impact on global climate and ecosystems. However, an understanding of how the ENSO might have evolved over geological timescales is still lacking, despite a well-accepted recognition that such an understanding has direct implications for constraining human-induced future ENSO changes. Here, using climate simulations, we show that ENSO has been a leading mode of tropical sea surface temperature (SST) variability in the past 250 My but with substantial variations in amplitude across geological periods. We show this result by performing and analyzing a series of coupled time-slice climate simulations forced by paleogeography, atmospheric CO2 concentrations, and solar radiation for the past 250 My, in 10-My intervals. The variations in ENSO amplitude across geological periods are little related to mean equatorial zonal SST gradient or global mean surface temperature of the respective periods but are primarily determined by interperiod difference in the background thermocline depth, according to a linear stability analysis. In addition, variations in atmospheric noise serve as an independent contributing factor to ENSO variations across intergeological periods. The two factors together explain about 76% of the interperiod variations in ENSO amplitude over the past 250 My. Our findings support the importance of changing ocean vertical thermal structure and atmospheric noise in influencing projected future ENSO change and its uncertainty.

Global and regional climate in 2023

Abstract2023 was the warmest year on record with a global mean temperature of 1.45 ± 0.12 degC above pre‐industrial levels, surpassing the previous record (from 2016) by 0.17 degC. The onset of El Niño in early July was accompanied by record‐breaking land and sea‐surface temperatures, with June to September all exceeding previous monthly temperature records, and July and August being the hottest months on record. Sea‐surface temperatures have attained record highs for all months since March 2023 and Antarctic sea ice reached record lows throughout the year.

Combining Temperature and Precipitation to Constrain the Aerosol Contribution to Observed Climate Change

Abstract Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP. Significance Statement Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.

Climate geographies and geographies of knowledge

AbstractFocusing on ‘Geographies of Knowledge’, this Commentary explores interactions between knowledge about climate change and the evolving multi‐level governance of climate. Three vignettes illustrate the complexity of this relationship. The first shows how expert advice, combined with other factors, persuaded the UK government to adopt an ambitious, long‐term emissions reduction target in the early 2000s. The second considers the ‘2°C target’ (latterly 1.5°C) to limit rises in global mean temperature, variously seen as essential, unachievable or symbolic. The third turns to geoengineering and its governance. All three demonstrate the significance of epistemic, political and discursive factors, as these intertwine and play out across different sites, scales and levels of governance.

The 2023 global warming spike was driven by the El Niño–Southern Oscillation

Abstract. Global-mean surface temperature rapidly increased 0.29 ± 0.04 K from 2022 to 2023. Such a large interannual global warming spike is not unprecedented in the observational record, with a previous instance occurring in 1976–1977. However, why such large global warming spikes occur is unknown, and the rapid global warming of 2023 has led to concerns that it could have been externally driven. Here we show that climate models that are subject only to internal variability can generate such spikes, but they are an uncommon occurrence (p = 1.6 % ± 0.1 %). However, when a prolonged La Niña immediately precedes an El Niño in the simulations, as occurred in nature in 1976–1977 and 2022–2023, such spikes become much more common (p = 10.3 % ± 0.4 %). Furthermore, we find that nearly all simulated spikes (p = 88.5 % ± 0.3 %) are associated with El Niño occurring that year. Thus, our results underscore the importance of the El Niño–Southern Oscillation in driving the occurrence of global warming spikes such as the one in 2023, without needing to invoke anthropogenic forcing, such as changes in atmospheric concentrations of greenhouse gases or aerosols, as an explanation.

Late Holocene extraordinary palaeoflood events and their climatological context in the Shahe River, Huaihe River Basin, China

Palaeoflood events represent immediate hydrological responses to extreme climate change. A loess-paleosol sedimentary profile containing three overbank flood deposits (OFD1, OFD2, and OFD3) layers that recorded palaeoflood events was discovered on the platform scarp of the Shahe River, a tributary of the Huaihe River through a field investigation. Sediment samples were collected, and their physicochemical properties, as well as optically stimulated luminescence (OSL) dating, were analyzed. The results indicated that OFD1, OFD2, and OFD3 were indeed overbank flood deposits influenced by hydrodynamic forces. However, OFD3 and OFD1 consisted primarily of sand (>60 %) in terms of particle size composition, whereas paleosol (S0), transitional loess (Lt), and Malan loess (L1) mainly comprised silt (>70 %). Moreover, the magnetic susceptibility values of OFD3 and OFD1 exceeded those of S0, Lt, and L1. The contents of Na2O, K2O, and SiO2 were higher, while their contents of Al2O3 and Fe2O3 were lower in OFD3 and OFD1. These suggested that OFD3 and OFD1 may originate from weathered bedrock materials transported from the upper reaches of the Shahe River under significant hydrodynamic forces. The particle size composition, magnetic susceptibility value, and geochemical element composition of OFD2 were similar to those of S0, Lt, and L1 but diverged significantly from those of OFD1 and OFD3, indicating that OFD2 originated from loess and soil sediments widely distributed on both sides of the valley. Through OSL dating and stratigraphic chronological framework of the sedimentary profile, these three extraordinary palaeoflood events in the Huaihe River Basin occurred during the late Holocene, ∼1470 a. The analysis of high-resolution climate proxy indicators, atmospheric circulation factors, and global mean temperature demonstrated a direct correlation between these extreme flood events and abrupt climate changes during ∼ 1470 a. This period corresponded to severe climate deterioration during the Northern and Southern Dynasties (589–420 CE) in China. These findings are crucial for enhancing our understanding of regional hydrological climate responses to global changes.

Global sustainability scenarios lead to regionally different outcomes for terrestrial biodiversity

Abstract Mitigating climate change (CC) and reversing biodiversity decline are urgent and interconnected global priorities. Strategies to address both crises must consider the relationships, synergies and trade-offs between key response measures, including sustainable production and consumption patterns, protected areas (PAs) and climate mitigation policy (CP). In this paper, we review a large set of scenarios (n = 96) from the Integrated Model to Assess the Global Environment (IMAGE) describing future development of land use, greenhouse gas emissions and their impact on CC and biodiversity. We calculate the global mean temperature increase (GMTI) and the Mean Species Abundance (MSA) of plants, a metric indicative of local terrestrial biodiversity intactness. The set includes scenarios with and without specific CP to address CC, PA for biodiversity and demand and supply sustainability measures such as increased energy efficiency and reduced meat consumption. Our findings indicate that scenarios with integrated measures can prevent biodiversity loss at the global scale, yet with clear regional differences. By 2050, 15 out of 30 (50%) scenarios with at least 30% of global land as PAs show positive MSA changes in grasslands and tropical non-forests (Grass & TnF), but only 1 (3%) does so in tropical forests (TF). We demonstrate that pasture and food/feed crops are the main drivers of MSA loss in Grass & TnF and that scenarios with high levels of PAs prevent land conversion and increase biodiversity. By 2100, 28 out of 46 (60%) scenarios with mitigation measures to restrict CC to 2 °C or less in 2100 result in positive MSA changes in TF, but only 13 (28%) do so in Grass & TnF, reflecting the larger impacts of land use change in the latter region. These results underscore the importance of time and regionally-tailored approaches to address the biodiversity and CC crises.

Effects of climate warming on energetics and habitat of the world's largest marine ectotherm

Responses of organisms to climate warming are variable and complex. Effects on species distributions are already evident and mean global surface ocean temperatures are likely to warm by up to 4.1 °C by 2100, substantially impacting the physiology and distributions of ectotherms. The largest marine ectotherm, the whale shark Rhincodon typus, broadly prefers sea surface temperatures (SST) ranging from 23 to 30 °C. Whole-species distribution models have projected a poleward range shift under future scenarios of climate change, but these models do not consider intraspecific variation or phenotypic plasticity in thermal limits when modelling species responses, and the impact of climate warming on the energetic requirements of whale sharks is unknown. Using a dataset of 111 whale shark movement tracks from aggregation sites in five countries across the Indian Ocean and the latest Earth-system modelling produced from Coupled Model Intercomparison Project Phase 6 for the Intergovernmental Panel on Climate Change, we examined how SST and total zooplankton biomass, their main food source, may change in the future, and what this means for the energetic balance and extent of suitable habitat for whale sharks. Earth System Models, under three Shared Socioeconomic Pathways (SSPs; SSP1–2.6, SSP3–7.0 and SSP5–8.5), project that by 2100 mean SST in four regions where whale shark aggregations are found will increase by up to 4.9 °C relative to the present, while zooplankton biomass will decrease. This reduction in zooplankton is projected to be accompanied by an increase in the energetic requirements of whale sharks because warmer water temperatures will increase their metabolic rate. We found marked differences in projected changes in the extent of suitable habitat when comparing a whole-species distribution model to one including regional variation. This suggests that the conventional approach of combining data from different regions within a species' distribution could underestimate the amount of local adaptation in populations, although parameterising local models could also suffer from having insufficient data and lead to model mis-specification or highly uncertain estimates. Our study highlights the need for further research into whale shark thermal tolerances and energetics, the complexities involved in projecting species responses to climate change, and the potential importance of considering intraspecific variation when building species distribution models.

Trends, Skill, and Sources of Skill in Initialized Climate Forecasts of Global Mean Temperature

AbstractWe evaluate the skill and sources of skill in initialized seasonal climate forecasts of monthly global mean temperature from the North American Multi‐Model Ensemble (NMME) during the period 1991–2024. The forecasts demonstrate skill in addition to that from the long‐term trend, and that skill is primarily attributable to ENSO. However, the skill varies seasonally, with skill being lowest for target periods during Northern Hemisphere summer. Single model ensembles show underdispersion at short leads, while the multi‐model ensemble is overdispersed, suggesting initial condition errors and highlighting the importance of model initialization for quantification of forecast uncertainty. Lead‐time dependent errors in global mean temperature trends appear related to Pacific trend errors. The multi‐model mean captured the overall trend but underestimated the record‐breaking temperatures of 2023. Forecasts for the remainder of 2024 indicate cooling by the end of the year.

Paleoclimate data provide constraints on climate models' large-scale response to past CO2 changes

The paleoclimate record provides a test-bed in which climate models can be evaluated under conditions of substantial CO2 change; however, these data are typically under-used in the process of model development and evaluation. Here, we use a set of metrics based on paleoclimate proxy observations to evaluate climate models under three past time periods. We find that the latest CMIP6/PMIP4 ensemble mean does a remarkably good job of simulating the global mean surface air temperatures of these past periods, and is improved on CMIP5/PMIP3, implying that the modern climate sensitivity of the CMIP6/PMIP4 model ensemble mean is consistent with the paleoclimate record. However, some models, in particular those with very high or very low climate sensitivity, simulate paleo temperatures that are outside the uncertainty range of the paleo proxy temperature data; in this regard, the paleo data can provide a more stringent constraint than data from the historical record. There is also consistency between models and data in terms of polar amplification, with amplification increasing with increasing global mean temperature across all three time periods. The work highlights the benefits of using the paleoclimate record in the model development and evaluation cycle, in particular for screening models with too-high or too-low climate sensitivity across a range of CO2 concentrations.

Linking local climate scenarios to global warming levels: applicability, prospects and uncertainties

Abstract Global warming levels (GWLs) are increasingly becoming a central concept in climate change studies. In recent years, their integrative quality for climate change impact analysis has been demonstrated, and methodological advancements have helped to compensate for some inherent shortfalls of the concept. However, their applicability at the regional level is debatable, and no study to date has examined the possibility of linking local climate scenarios to GWLs. For the case of Austria, we have evaluated the relation between global and local warming patterns, and whether version changes of global climate models could be incorporated into local climate scenarios by means of the GWL concept, without updating the actual data. We applied the time sampling approach, where GWLs are determined as periods when global mean temperature anomalies cross a certain threshold. GWL periods were sampled both from the global models in the background of the local climate scenarios (CMIP5), and from an equivalent ensemble of newer-generation climate models (CMIP6). Uncertainties resulting from sampling GWLs from different global climate model ensembles were examined, and prospects for local climate change impact assessments were discussed. Accounting for updated global climate model versions might be useful when the changes at certain GWLs are related to fixed reference periods, but temperature increments between GWLs remained relatively constant across model versions, even on the local level. The study bridges a significant gap to link regional and local climate projections to GWLs. Climate change impacts assessments that build on those datasets can benefit from the integrative character of GWLs, making studies comparable across multiple disciplines and model versions, and thus fostering a way to communicate local climate change impacts more comprehensible.

Emulating inconsistencies in stratospheric aerosol injection

Stratospheric aerosol injection (SAI) would involve the addition of sulfate aerosols in the stratosphere to reflect part of the incoming solar radiation, thereby cooling the climate. Studies trying to explore the impacts of SAI have often focused on idealized scenarios without explicitly introducing what we call ‘inconsistencies’ in a deployment. A concern often discussed is what would happen to the climate system after an abrupt termination of its deployment, whether inadvertent or deliberate. However, there is a much wider range of plausible inconsistencies in deployment than termination that should be evaluated to better understand associated risks. In this work, we simulate a few representative inconsistencies in a pre-existing SAI scenario: an abrupt termination, a decade-long gradual phase-out, and 1 year and 2 year temporary interruptions of deployment. After examining their climate impacts, we use these simulations to train an emulator, and use this to project global mean temperature response for a broader set of inconsistencies in deployment. Our work highlights the capacity of a finite set of explicitly simulated scenarios that include inconsistencies to inform an emulator that is capable of expanding the space of scenarios that one might want to explore far more quickly and efficiently.

The influence of proxy selection on global annual mean temperature reconstructions during the Common Era

The influence of proxy selection on global annual mean temperature reconstructions during the Common Era

Can climate change signals be detected from the terrestrial water storage at daily timescale?

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future.

Biochar as a carbon dioxide removal strategy in integrated long-run mitigation scenarios

Limiting global warming to under 2 °C would require stringent mitigation and likely additional carbon dioxide removal (CDR) to compensate for otherwise unabated emissions. Because of its technology readiness, relatively low cost, and potential co-benefits, the application of biochar to soils could be an effective CDR strategy. We use the Global Change Analysis Model, a global multisector model, to analyze biochar deployment in the context of energy system uses of biomass with CDR under different carbon price trajectories. We find that biochar can create an annual sink of up to 2.8 GtCO2 per year, reducing global mean temperature increases by an additional 0.5%–1.8% across scenarios by 2100 for a given carbon price path. In our scenarios, biochar’s deployment is dependent on potential crop yield gains and application rates, and the competition for resources with other CDR measures. We find that biochar can serve as a competitive CDR strategy, especially at lower carbon prices when bioenergy with carbon capture and storage is not yet economical.