Global Mean Research Articles

Sampling the diurnal and annual cycles of the Earth's energy imbalance with constellations of satellite-borne radiometers

Abstract. The Earth's energy imbalance, i.e. the difference between incoming solar radiation and outgoing reflected and emitted radiation, is the one quantity that ultimately controls the evolution of our climate system. However, despite its importance, there is limited knowledge of the exact magnitude of the energy imbalance, and the small net difference of about 1 W m−2 between two large fluxes (approximately 340 W m−2) makes it challenging to measure directly. There has recently been renewed interest in using wide-field-of-view radiometers on board satellites to measure the outgoing radiation, as a possible method for deducing the global annual mean energy imbalance. Here we investigate how to sample in order to correctly determine the global annual mean imbalance and interannual trends, using a limited number of satellites. We simulate satellites in polar (90° inclination), sun-synchronous (98°) and precessing orbits (73, 82°), as well as constellations of these types of satellite orbits. We find that no single satellite provides sufficient sampling, both globally and of the diurnal and annual cycles, to reliably determine the global annual mean. If sun-synchronous satellites are used, at least six satellites are required for an uncertainty below 1 W m−2. One precessing satellite combined with one polar satellite results in root-mean-square errors of 0.08 to 0.10 W m−2, and a combination of two or three polar satellites results in root-mean-square errors of 0.10 or 0.04 W m−2, respectively. In conclusion, at least two satellites that complement each other are necessary to ensure global coverage and achieve a sampling uncertainty well below the current estimate of the energy imbalance.

Glacial isostatic adjustment driven by asymmetric ice sheet melt during the Last Interglacial causes multiple local sea-level peaks

Global mean sea-level (GMSL) change during the Last Interglacial (LIG, 129−116 ka) gives perspective on how ice sheets respond to warming. Observations of multiple peaks in LIG relative sea level (RSL) records, combined with an assumption that the Laurentide Ice Sheet (LIS) collapsed prior to the LIG, have been used to infer Greenland and Antarctic ice sheet melt histories as well as oscillations in LIG GMSL. However, evidence for an LIS outburst flood at ca. 125 ka and extensive early-LIG Antarctic melt suggests that Laurentide remnants may have persisted longer into the LIG than typically thought even as Antarctic melt accelerated. Here, we explore the effect of concurrent early-Holocene Laurentide persistence and Antarctic collapse on glacial isostatic adjustment and sea level. In our models, we hold GMSL constant at present levels (i.e., GMSL = 0) from 128 ka to 117 ka by balancing excess Laurentide ice with early-LIG Antarctic melt. We find that due to glacial isostatic adjustment, this synchronous but asymmetric ice change causes multiple RSL peaks, separated by ∼4.2 ± 2.5 m of RSL fall near North America and ∼1.3 ± 0.7 m around the Indian Ocean. This spatial pattern resembles observations. These results show that multiple peaks in LIG RSL could have occurred with asymmetric ice changes between the Northern and Southern Hemisphere that sum to little, if any, change in GMSL. Our work highlights the need for LIG modeling studies to consider that dynamic cryospheric changes can occur even with near-constant GMSL.

Evidence and Explanation for the 2023 Global Warming Anomaly

In 2023, the rapid increase in global temperature of around 0.25 °C caught the scientific community by surprise. Its cause has been investigated largely by exploring variations on a long-term trend, with little success. Building on previous work, this paper proposes an alternative explanation—on decadal timescales, observed temperature shows a complex, nonlinear response to forcing, stepping through a series of steady-state regimes. The 2023 event is nominated as the latest in the sequence. Step changes in historical and modeled global mean surface temperatures (GMSTs) were detected using the bivariate test. Each time series was then separated into gradual (trends) and rapid components (shifts) and tested using probative criteria. For sea surface, global and land surface temperatures from the NOAA Global Surface Temperature Dataset V6.0 1880–2022, the rapid component of total warming was 94% of 0.72 °C, 78% of 1.16 °C and 74% of 1.93 °C, respectively. These changes are too large to support the gradual warming hypothesis. The recent warming was initiated in March 2023 by sea surface temperatures (SSTs) in the southern hemisphere, followed by an El Niño signal further north. Global temperatures followed, then land. A preceding regime shift in 2014 and subsequent steady-state 2015–2022 was also initiated and sustained by SSTs. Analysis of the top 100 m annual average ocean temperature from 1955 shows that it forms distinct regimes, providing a substantial ‘heat bank’ that sustains the changes overhead. Regime shifts are also produced by climate models. Archived data show these shifts emerged with coupling of the ocean and atmosphere. Comparing shifts and trends with equilibrium climate sensitivity (ECS) in an ensemble of 94 CMIP5 RCP4.5 models 2006–2095 showed that shifts had 2.9 times the influence on ECS than trends. Factors affecting this relationship include ocean structure, initialization times, physical parameters and model skill. Single model runs with skill ≥75 showed that shifts were 6.0 times more influential than trends. These findings show that the dominant warming mechanism is the sudden release of heat from the ocean rather than gradual warming in the atmosphere. The model ensemble predicted all regime changes since the 1970s within ±1 year, including 2023. The next shift is projected for 2036, but current emissions are tracking higher than projected by RCP4.5. Understanding what these changes mean for the estimation of current and future climate risks is an urgent task.

Quantifying the impact of global nitrate aerosol on tropospheric composition fields and its production from lightning NOx

Abstract. Several global modelling studies have explored the effects of lightning-generated nitrogen oxides (LNOx) on gas-phase chemistry and atmospheric radiative transfer, but few have quantified LNOx's impact on aerosol, particularly when nitrate aerosol is included. This study addresses two key questions: (1) how does including nitrate aerosol affect properties such as tropospheric composition, and (2) how do these effects depend on lightning parameterisation and LNOx levels? Using the Met Office's Unified Model–United Kingdom Chemistry and Aerosol (UM–UKCA) global chemistry–climate model, which now includes a modal nitrate aerosol scheme, we investigate these effects with two lightning-flash-rate parameterisations. Our findings show that both nitrate aerosol and LNOx significantly impact tropospheric composition and aerosol responses. Including nitrate aerosol reduces global mean tropospheric OH by 5 %, decreases the tropospheric ozone burden by 4 %–5 %, increases methane lifetime by a similar amount, and alters the top-of-atmosphere (TOA) net downward radiative flux by −0.4 W m−2. The inclusion of nitrate also shifts the aerosol size distribution, particularly in the Aitken and accumulation modes. A 5.2 Tg N yr−1 increase in LNOx from a zero baseline results in global aerosol increases of 2.8 % in NH4, 4.7 % in fine NO3, 12 % in coarse NO3, and 5.8 % in SO4 mass burdens. This much LNOx increase causes relatively small positive changes in aerosol optical depth, TOA radiative flux, and cloud droplet number concentration compared to when nitrate is included. The results, based on a fast uptake rate for HNO3 to produce NH4NO3, likely represent an upper limit on nitrate effects.

Improved Sea Level Reconstruction from 1900 to 2019

Abstract Before the satellite era, global sea level reconstructions depended on tide gauge records and in situ hydrographic observations. However, the available global mean sea level (GMSL) reconstructions, using different methods, indicate a spread in sea level trend over 1900–2008 (1.3 ∼ 2.0 mm yr−1). With better understanding of the causes of sea level change, here we implement an improved sea level reconstruction, building upon previous work by Church and White, and include three additional factors: the sea level fingerprints, the sterodynamic sea level (SDSL) climate change patterns, and more complete local vertical land motion (VLM) estimates. The trend of new GMSL reconstruction is 1.6 ± 0.2 mm yr−1 (90% confidence level) over 1900–2019, consistent with the sum of observation-based sea level contributions of 1.5 ± 0.2 mm yr−1. The lower trend from the new reconstruction compared with the earlier Church and White result is mainly due to the updated VLM correction. The inclusion of sea level fingerprints and SDSL climate change patterns are the dominant contributors for the improved skill of regional reconstruction. Despite GMSL budget closure in terms of long-term trend since 1900, our study shows discrepancies between the trends from available GMSL reconstructions and the sum of independent observation-based contributions over different periods in the twentieth century, e.g., the discrepancy at the beginning of the twentieth century, which could be related to possible bias in the land ice component estimate. The reconstruction methodology developed here, as tested with synthetic sea level fields, could provide a promising way to identify potential biases in the individual sea level components constrained by available global tide gauge observations.

State-dependency of dynamic and thermodynamic contributions to effective precipitation changes

Abstract Reliable projections of the future hydrological cycle are needed for designing adaptation and mitigation measures under global warming. However, uncertainties in the projected sign and magnitude of effective precipitation changes (precipitation minus evaporation, P − E) remain high. Here, we examine the state-dependency of circulation, temperature, and relative humidity contributions to P − E changes in simulations of the Last Glacial Maximum (LGM), mid-Holocene, and abrupt quadrupling of the atmospheric carbon dioxide concentration. To this purpose, we apply a moisture budget decomposition and a thermodynamic scaling approximation to CMIP6/PMIP4 simulations with the Earth system model MPI-ESM1.2. We find that the importance of thermodynamic and dynamic contributions to P − E changes and the patterns of dynamic contributions depend strongly on the underlying forcing. Greenhouse gas forcing leads to a stronger thermodynamic than dynamic response. The LGM ice sheets yield a large dynamic contribution with zonally heterogeneous patterns. Orbital forcing induces a predominantly dynamic response with a hemispherically anti-symmetric structure. We also identify state invariant features: the importance of temperature and relative humidity contributions to specific humidity changes is consistent across states, and the wet-get-wetter-dry-get-drier paradigm proposed for global warming holds in almost all regions dominated by thermodynamic contributions. By definition, the P − E budget and the respective thermodynamic, dynamic, and transient eddy contributions vanish in the global mean. Moreover, we find that for increasing length scales, the spatial variability of these contributions decays with similar rates. We suggest repeating our analysis for more models and states which could help constraining hydroclimate projections.

Enhancing prefrontal modulation by phase-locking intermittent theta burst stimulation to a concurrent transcranial alternating current stimulation

Abstract Theta burst stimulation (TBS) modulates cortical excitability by applying bursts of transcranial magnetic stimulation (TMS) in theta rhythms. Individual responses to TBS vary however greatly due to various factors, such as anatomical differences or the phase of the ongoing oscillatory activity in which TBS pulses are applied. To combat this variability, we exploit the ability of transcranial alternating current stimulation (tACS) to shape the state of cortical excitability in a phase-dependent manner. While cortical excitability is increased at crests of the tACS-induced current, applying the TBS triplet pulses at these crests has the potential to produce larger neuronal responses and thus increase the likelihood of LTP. In our randomized sham-controlled study, we focused on enhancing prefrontal cortex excitability by phase-locking intermittent TBS (iTBS) to the crests of an induced 5Hz tACS current. Twenty-seven healthy participants received two iTBS sessions, once paired with sham-tACS and once with active tACS in a cross-over design. We evaluated effects of our stimulation protocol on cortical excitability by comparing TMS-induced activity and resting-state Microstates in the EEG before and after the stimulation as well as between the two sessions. We found significant effect of iTBS on channel-wise, global and oscillatory TMS-induced activity, as well as changes in Microstates. The concurrent, phase-locked tACS-iTBS protocol notably decreased the N100 amplitude of the Global Mean Field Power. We also found that baseline TMS-induced oscillatory activity was a key predictor of changes in TMS-related oscillatory activity. In the case of TMS-related gamma oscillations, a significant interaction between our stimulation protocols and baseline activity was observed, indicating that the relationship between baseline and post-iTBS oscillations was strengthened by the concurrent phase-locked tACS-iTBS stimulation protocol. These findings highlight the potential of phase-locked tACS to enhance the effects of iTBS on prefrontal cortical excitability.

Radiological Assessment of Exposure to Background Ionizing Radiation in Ogbomoso South Local Government, Oyo State, Nigeria

The radiological evaluation of rural areas in developing countries is frequently overlooked due to the assumption of minimal radiation risk and low levels of industrialization. This research aims to fill this gap by assessing the background radiation level in Ogbomoso South Local Government, Oyo State, Nigeria, and estimating the radiological risks. One thousand sampling locations were randomly selected across the ten administrative wards of the study area, and the radiation level was examined using a dosimeter. The recorded dose rates varied between 0.09 and 0.17 μSvh-1, with an overall mean of 0.13 μSvh-1. This average is 48.2% lower than the global mean of 0.27 µSvh-1. The estimated annual effective dose equivalent ranged from 0.15 to 0.30 mSvy-1, with an average of 0.23 mSvy-1, which is 23.1% below the acceptable limit of 1 mSvy-1. The study shows that the current levels of radiation exposure do not present a significant radiological threat. It recommends establishing a regular monitoring program to observe changes over time, with this data serving as a baseline for Ogbomoso South Local Government.

Recent global temperature surge intensified by record-low planetary albedo.

In 2023, the global mean temperature soared to almost 1.5K above the pre-industrial level, surpassing the previous record by about 0.17K. Previous best-guess estimates of known drivers including anthropogenic warming and the El Niño onset fall short by about 0.2K in explaining the temperature rise. Utilizing satellite and reanalysis data, we identify a record-low planetary albedo as the primary factor bridging this gap. The decline is apparently caused largely by a reduced low-cloud cover in the northern mid-latitudes and tropics, in continuation of a multi-annual trend. Further exploring the low-cloud trend and understanding how much of it is due to internal variability, reduced aerosol concentrations, or a possibly emerging low-cloud feedback will be crucial for assessing the current and expected future warming.

Evaluation of the effects of Argo data quality control on global ocean data assimilation systems

A series of observing system experiments (OSEs) were conducted in order to evaluate the effects of Argo data quality control (QC), by using the three global ocean data assimilation systems. During the experimental period between 2015 and 2020, some Argo floats are affected by the abrupt salinity drifts, which caused spurious increasing trend of the global mean salinity in the reanalyses using the observations with only real-time QC applied. The spurious trend is mitigated by applying the gray list provided by the Argo Global Data Assembly Centres (GDAC), and further reduced by assimilating the delayed-mode Argo data of the Argo GDAC instead of the real-time Argo data. These impacts of the Argo QC are generally consistent among the three ocean data assimilation systems. Further investigations in the JMA’s system show that errors in the analyzed salinity with respect to the delayed-mode Argo data are smaller in the OSE with more rigorous QC, and the spatiotemporal variations in the sea-surface dynamic height are reproduced better. Additionally, QC impacts on the analyzed temperatures are shown not to directly reflect the difference in temperature observations among OSEs, and may be affected by difference in the salinity observations among OSEs through the cross-covariance relationship in the data-assimilation systems.

CNN-Based Device-Agnostic Feature Extraction From ONH OCT Scans.

Optical coherence tomography (OCT)-derived measurements of the optic nerve head (ONH) from different devices are not interchangeable. This poses challenges to patient follow-up and collaborative studies. Here, we present a device-agnostic method for the extraction of OCT biomarkers using artificial intelligence. ONH-centered OCT volumes from the Heidelberg SPECTRALIS, ZEISS CIRRUS HD-OCT 5000, and Topcon 3D OCT-1000 Mark I/II and 3D OCT-2000 devices were annotated by trained graders. A convolutional neural network (CNN) was trained on these segmented B-scans and utilized to obtain several ONH biomarkers, such as the retinal nerve fiber layer (RNFL) and the minimal rim width (MRW). The CNN results were compared between different devices and to the manufacturer-reported values using an independent test set. The intraclass correlation coefficient (ICC) for the circumpapillary retinal nerve fiber layer (cpRNFL) at 3.4 mm reported by the CIRRUS and 3D OCT-2000 was 0.590 (95% confidence interval [CI], -0.079 to 0.901), and our CNN resulted in a cpRNFL ICC of 0.667 (95% CI, -0.035 to 0.939). The cpRNFL at 3.5 mm on the CIRRUS, 3D OCT-2000, and SPECTRALIS generated by the CNN resulted in an ICC of 0.656 (95% CI, 0.055-0.922). Comparing the global mean MRWs from the SPECTRALIS between CNN and manufacturer yielded an ICC of 0.983 (95% CI, 0.917-0.997). The CNN ICC for the MRW among the CIRRUS, 3D OCT-2000, and SPECTRALIS was 0.917 (95% CI, 0.947-0.981). Our device-agnostic feature extraction from ONH OCT scans showed a higher reliability than the measures generated by the manufacturers for cpRNFL. MRW measurements compared very well among the manufacturers. This open-source software can robustly extract a wide range of biomarkers from any OCT device, removing the dependency on manufacturer-specific algorithms, which has significant implications for patient follow-up and collaborative research.

Global soil microplastic assessment in different land-use systems is largely determined by the method of analysis: A meta-analysis

Although microplastics (1 μm - 5 mm, MP) are increasingly recognised as a novel entity of pollutants, we still lack a basic understanding of their prevalence in different terrestrial environments. Here, we aimed at performing comparisons of MP concentrations (items kg−1) in different agro-ecosystems, with specific focus on input pathways and land uses, while accounting for the plethora of method variations available, such as analysed MP sizes, sampling depths, density separation solutions, as well as removal of organic matter. We found that the current global means of MP loads, from 89 studies (553 sites), benchmarks 2900 ± 7600 MP items kg−1 soil, substantially more than the global median of 480 MP items kg−1. Roughly 81 % of the studies were conducted in Asia; hence, continent-wider comparisons are still hampered by low study numbers for most regions. Maximum MP numbers were found for soils under both greenhouses and plastic mulching (5200 ± 8300 items kg−1), followed by arable soils with sludge amendments (3700 ± 8800 items kg−1), surprisingly without evidence of elevated MP loads in horticultural fields relative to other agricultural management practices. Intriguingly, global MP loads significantly increased with decreasing levels of urbanisation, i.e., they were highest in rural areas. Yet, quantitative comparisons among sites are biased by the methodology selected for MP analyses. Apart from inconsistencies in sampling depth and size of screened MP particles, across all sites and treatments, largest MP loads were commonly found when using high-density solutions rather than low-density ones, and when soil organic matter removal was performed after and not before the density separation step.

Comparison of LGE-MRI and Local Impedance Data Recorded in Human Left Atria

Abstract Efficient personalized ablation strategies for treating atrial arrhythmias remain challenging. Discrepancies in identifying arrhythmogenic areas using characterization methods, such as late gadolinium enhanced magnetic resonance imaging (LGE-MRI) and electroanatomical mapping, require a comparative analysis of local impedance (LI) and LGE-MRI data. This study aims to analyze correlations as basis for improvement of treatment strategies. 16 patients undergoing left atrium (LA) ablation with LGE-MRI acquisition and LI data recording were recruited. LGE-MRI data and LI measurements were normalized to patient- and modality-specific blood pool references. A global mean shape was generated based on all patient geometries, and normalized local impedance (LIN) and LGE-MRI image intensity ratio (IIR) data points were coregistered for comparison. Data analysis comprised intra-patient and inter-patient assessments, evaluating differences in LIN values among datasets categorized by their IIR. Due to substantial deviations in LIN values, even within the same patient and IIR-category, discerning the presence or absence of a correlation was challenging, and no statistically significant correlation could be identified. Our findings underscore the necessity for standardized protocols in data acquisition, processing, and comparison, to minimize unquantified confounding effects. While immediate substitution of LI for LGE-MRI seems improbable given the significant LIN variations, this preliminary study lays the groundwork for systematic data acquisition. By ensuring data quality, a meaningful comparison between LI and LGE-MRI data can be facilitated, potentially shaping future strategies for atrial arrhythmia treatment.

COMPARISON BETWEEN THE FAST STRATEGIES OF A VIRTUAL REALITY PERIMETRY AND THE HUMPHREY FIELD ANALYZER IN PATIENTS WITH GLAUCOMA.

This study compared the agreement between the Humphrey Field Analyzer (HFA) SITA Fast strategy and a novel virtual reality head-mounted visual perimetry device (VisuALL) in glaucoma patients. This is prospective observational study. This study was conducted on 62 eyes of 39 glaucoma subjects. All participants had visual field testing with the VisuALL AVAFAST strategy and the HFA (24-2, Swedish Interactive Threshold Algorithm FAST). The mean sensitivity of the whole visual field (VF) and each quadrant was compared between both machines. Additionally, the pattern deviation (PD) plot was analyzed to compare the agreement of both devices to detect localized VF defects. Correlation and agreement between the mean sensitivity of the fast strategies from VisuALL and HFA. The global mean sensitivity of the VisuALL and the HFA correlated significantly (r = 0.60, P < 0.001) and was in agreement (r= 0.73, P < 0.001). The detection of visual field defects in all quadrants was also moderately correlated and in agreement. Participants overwhelmingly preferred the VisuALL over the conventional SAP (80%). Although the mean sensitivity and ability to detect localized visual field defects of the VisuALL were correlated and in agreement with the HFA, this was only moderate. This indicates that the VisuALL AVAFast strategy must be used with caution.

Storage capacity estimates and site conditions of potential locations for offshore-wind powered carbon dioxide removal and carbon sequestration in ocean basalt

Negative emission technologies (NETs) are considered essential to keep global warming below 2 °C. Situating wind-powered carbon dioxide removal (CDR) devices offshore and injecting carbon dioxide (CO2) into deep-water sub-seafloor basalt aquifers has the potential to offer large CO2 removal capacity. It also avoids land and water-use competition and provides additional low-risk protections against post-injection leakage compared to terrestrial CO2 storage. This paper seeks to identify locations where offshore wind and potential basalt storage locations exist within close proximity to one another around the globe. A global mean wind power density map at 150 m height was computed using 30 years (1986–2016) of ERA5 hourly wind speed reanalysis data. Offshore regions with mean wind speed greater than 8 m/s were identified. Offshore regions with basalt aquifers along seismic or aseismic ridges which provide potential CO2 storage sites were identified and selected based on sediment thickness, age, and distance from plate boundaries. Four scenarios were constructed to capture a range of constraints with implications for technical, economic and regulatory difficulties. For each scenario, eligible regions for CO2 injection were filled by regularly spaced grid points and the distance to the nearest eligible wind resource was calculated for each point to identify the most promising configurations. Total available storage capacity within reach of wind resources was estimated to be between 4,300Gt and 196,000Gt depending on both uncertainties in porosity and other imposed constraints; even the most conservative estimates represent enormous capacity compared to global targets for negative emissions technologies. Typically, the best areas were found close to the poles due to the greater prevalence of good wind resources in those areas. Site-specific properties such as water depth and distance from shore are computed for the identified locations in order to characterize the conditions in which such locations are typically found.

How could 50 °C be reached in Paris: Analyzing the CMIP6 ensemble to design storylines for adaptation

Reaching a surface temperature of 50 °C in a heavily populated region, like Paris, would have devastating effects. Although such a high value seems far from the present-day record of 42.6 °C, its occurrence cannot be dismissed by the end of the 21st century, due to the continuous increase of global mean temperature. In this paper, we address two questions that were asked by the City of Paris to a group of scientists: When does this event start to be likely? What are the prevailing meteorological conditions? We base our study on the CMIP6 simulation ensemble. Many of the CMIP6 yield biases in temperature. Rather than using methods of bias correction, which are not necessarily adapted to high extremes, we propose a pragmatic approach of model selection in order to seek such high temperature events that are deemed realistic. We analyze the meteorological conditions leading to first occurrences of such hot events and their common atmospheric patterns. This paper describes a simple data mining approach (on a large ensemble of climate model simulations) which could be adapted to other regions of the world, in order to help decision makers anticipating and adapting to such devastating meteorological events.

Simulation of modern and future climate by INM-CM6M

Abstract The paper considers the results of climate change modelling for 1850–2100 using the INM-CM6M climate model of the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. The calculations were performed according to the CMIP6 protocol for modelling the present-day climate for the period from 1850 to 2014 and the IPCC scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 of anthropogenic forcing changes in 2015–2100. We analyse changes in such characteristics of the climate system as global mean near-surface temperature, spatial distributions of near-surface temperature, precipitation, sea level pressure, and radiative forcing characteristics in comparison with observational data and results obtained with the previous (INM-CM5) version of the model. We conclude that the new version of the model is superior to the previous one in terms of the quality of simulation of the observed climate and its changes in 1960–2022. The amplitude of global warming predicted by the INM-CM6M for moderate IPCC scenarios is close to the average value of the CMIP6 model ensemble, and for severe scenarios it is closer to the upper limit of its range. For the SSP3-7.0 and SSP5-8.5 scenarios, INM-CM6M predicts a complete loss of Arctic sea ice in summer. As the rate of global warming increases, so does the amplitude and duration of extreme weather and climate events.

Global Mean Surface Temperature: What Can We Learn from the Trajectory over a Millions-year Span?

Global mean surface temperature serves as a crucial metric in understanding the Earth’s climate dynamics, providing insights into long-term climate trends and variability. Over the course of a million years, the trajectory of global temperatures has been shaped by a multitude of factors, including natural climatic cycles, human activities, and external influences. By examining the historical trends and drivers influencing global mean surface temperature, researchers can gain valuable insights into the past, present, and future climate scenarios. This article delves into the intricate interplay of these factors and explores the lessons we can learn from studying the temperature trajectory over a million-year span.

Quantifying food security and mitigation risks consequential to climate change impacts on crop yields

Abstract Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the IMAGE integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison (GGCMI) of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as Agriculture, Forestry and Other Land Use (AFOLU) emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.

Accelerated Historical and Future Warming in the Middle East and North Africa

AbstractThe present study assesses the climate of the Middle East and North Africa (MENA) in detail, focusing on the historical and future warming trends in the region. The assessment incorporates data from observations, reanalyses, and statistically downscaled climate models from the Coupled Model Intercomparison Project Phases 5 and 6. Since the pre‐industrial era, the observed average climate in the MENA has warmed by 1.5°C and is on the brink of exceeding 2°C. The reanalysis data suggest that the regional warming over some MENA sub‐regions is three times faster than the global average. By the end of the 21st century, the Arabian Peninsula is projected to warm to 2.6°C ± 0.57°C and 7.6°C ± 1.53°C under low and high greenhouse gas emission scenarios, respectively. Distinct warming hotspots emerge over the Arabian Peninsula and Algeria in summer and over Mauritania in West Africa and the Elburz Mountains in Iran in winter. The summer hotspot over the Arabian Peninsula has already warmed by more than 2°C and can potentially warm to approximately 9°C under the high‐emission scenario. As global warming progresses to 1.5, 2.0, 3.0, and 4.0°C, the average temperature over the MENA land is projected to increase by 2.3°C ± 0.18°C, 3.0°C ± 0.22°C, 4.6°C ± 0.26°C, and 6.1°C ± 0.31°C, respectively. The 1.5, 2.0, 3.0, and 4.0°C warming levels over the MENA are expected to predate those of the global mean by two or three decades. Natural climate fluctuations also significantly influence the region's warming, contributing to temperature extremes.