Antarctic ice core records suggest that atmospheric CO2 increased by 15 to 20 ppm during Heinrich stadials (HS). These periods of abrupt CO2 increase are associated with a significant weakening of the Atlantic meridional overturning circulation (AMOC), and a warming at high southern latitudes. As such, modelling studies have explored the link between changes in AMOC, high southern latitude climate and atmospheric CO2. While proxy records suggest that the aeolian iron input to the Southern Ocean decreased significantly during HS, the potential impact on CO2 of reduced iron input combined with oceanic circulation changes has not been studied in detail. Here, we quantify the respective and combined impacts of reduced iron fertilisation and AMOC weakening on CO2 by performing numerical experiments with an Earth system model under boundary conditions representing 40,000 years before present (ka). Our study indicates that reduced iron input can contribute up to 6 ppm rise in CO2 during an idealized Heinrich stadial. This is caused by a 5% reduction in nutrient utilisation in the Southern Ocean, leading to reduced export production and increased carbon outgassing from the Southern Ocean. An AMOC weakening under 40ka conditions and without changes in surface winds leads to a ~0.5 ppm CO2 increase. The combined impact of AMOC shutdown and weakened iron fertilisation is almost linear, leading to a total CO2 increase of 7 ppm. Therefore, this study highlights the need of including changes in aeolian iron input when studying the processes leading to changes in atmospheric CO2 concentration during HS.

Over recent decades, the Southern Ocean (SO) has experienced multi-decadal surface cooling despite global warming. Earlier studies have proposed that recent SO cooling has been caused by the strengthening of surface westerlies associated with a positive trend of the Southern Annular Mode (SAM) forced by ozone depletion. Here we revisit this hypothesis by examining the relationships between the SAM, zonal winds and SO sea-surface temperature (SST). Using a low-frequency component analysis, we show that while positive SAM anomalies can induce SST cooling as previously found, this seasonal-to-interannual modulation makes only a small contribution to the observed long-term SO cooling. Global climate models well capture the observed interannual SAM-SST relationship, and yet generally fail to simulate the observed multi-decadal SO cooling. The forced SAM trend in recent decades is thus unlikely the main cause of the observed SO cooling, pointing to a limited role of the Antarctic ozone hole.

Ocean warming is a key factor impacting future changes in climate. Here we investigate vertical structure changes in globally averaged ocean heat content (OHC) in high- (HR) and low-resolution (LR) future climate simulations with the Community Earth System Model (CESM). Compared with observation-based estimates, the simulated OHC anomalies in the upper 700 m and 2000 m during 1960-2020 are more realistic in CESM-HR than -LR. Under RCP8.5 scenario, the net surface heat into the ocean is very similar in CESM-HR and -LR However, CESM-HR has a larger increase in OHC in the upper 250 m compared to CESM-LR, but a smaller increase below 250 m. This difference can be traced to differences in eddy-induced vertical heat transport between CESM-HR and -LR in the historical period. Moreover, our results suggest that with the same heat input, upper-ocean warming is likely to be underestimated by most non-eddy-resolving climate models.

Preprint, August 28th 2023Authors: Paolo Scussolini1*, Linh N. Luu2,3,4, Sjoukje Y. Philip4, Wouter R. Berghuijs5, Dirk Eilander1,6, Jeroen C.J.H. Aerts1, Sarah F. Kew4, Geert Jan van Oldenborgh4† , Willem H.J. Toonen5, Jan Volkholz7, Dim Coumou1,41Institute for Environmental Studies, Vrije Universiteit Amsterdam, Netherlands2Department of Geography, University of Lincoln, United Kingdom3Vietnam Institute of Meteorology, Hydrology and Climate Change, Hanoi, Vietnam4Royal Netherlands Meteorological Institute, Netherlands5Department of Earth Science, Vrije Universiteit Amsterdam, Netherlands6Deltares, Netherlands7Potsdam Institute for Climate Impact Research, Germany*Corresponding author: [email protected]† Deceased, October 12th2021

The commentary encourages supplementing the geological and natural concept of the Anthropocene with a cultural and political aspect. These two perspectives are not mutually exclusive but are complementary. This approach can facilitate its transition from the language of academic debate to practical and necessary actions at the societal level. According to the authors, the slightly abstract and impersonal Anthropocene should be shown in the context of cultural, economic and political dependencies and choices that created it and continue to reproduce its logic. This turn also opens up a new area for analysing the Anthropocene from the perspective of a critique of political economy (an analysis of the costs of economic policies that reproduce and accelerate successive stages of the ecological catastrophe) as well as of civic culture (research ‘anthropocentric awareness’ or ‘anthropocentric citizenship’ in entire societies). Thus, the authors suggest rejecting the fatalistic determinism of the Anthropocene as a process that, although originally caused by humans, is now often treated as a phenomenon beyond the reach of social action

Atlantic time-mean heat transport is northward at all latitudes and exhibits strong multidecadal variability between about 30N and 55N. Atlantic heat transport variability influences many aspects of the climate system, including regional surface temperatures, subpolar heat content, Arctic sea-ice concentration and tropical precipitation patterns. Atlantic heat transport and heat transport variability are commonly partitioned into two components: the heat transport by the AMOC and the heat transport by the gyres. In this paper we compare three different methods for performing this partition, and we apply these methods to the CESM1 Large Ensemble at 34N, 26N and 5S. We discuss the strengths and weaknesses of each method. One of these methods is a new physically-motivated method based on the pathway of the northward-flowing part of AMOC. This paper presents a preliminary version of our method. This preliminary version works only when the AMOC follows the western boundary of the basin. In this context, the new method provides a sensible estimate of heat transport by the overturning and by the gyre, and it is easier to interpret than other methods. According to our new diagnostic, at 34N and at 26N AMOC explains 120% of the multidecadal variability (20% is compensated by the gyre), and at 5S AMOC explains 90% of multidecadal variability.

A common approach to assessing how polar amplification affects lower latitude climate is to perform coupled ocean-atmosphere experiments in which sea ice is perturbed to a future state. A recent critique by M. England and others uses a simple 1-dimensional energy balance model (EBM) to show that sea ice perturbation experiments add artificial heat to the climate system. We explore this effect in a broader range of models and suggest a technique to correct for the artificial heat post-hoc. Our technique successfully corrects for artificial heat in the EBM and a possible generalization of this approach is developed to correct for artificial heat in an albedo modification experiment in a comprehensive earth system model. However, this technique can not be directly generalized to sea ice perturbation methodologies that employ a "ghost flux" seen only by the sea ice model. Applying the correction to the comprehensive albedo modification experiment, we find stronger artificial warming than in the EBM. Failing to account for the artificial heat also leads to overestimation of the climate response to sea ice loss, and can suggest false or artificially strong "tugs-of-war" between 19 low latitude warming and sea ice loss over some fields, for example Arctic surface temperature and 20 zonal wind.

Climate models robustly project acceleration of the Brewer-Dobson circulation (BDC) in response to climate change. However, the BDC trends derived from comprehensive models do not fully match observations. Additionally, the changing structure of the troposphere and stratosphere has received increasing attention in recent years and to which extent vertical shifts of the circulation are driving the acceleration is under debate. In this study, we present a novel method that enables the attribution of circulation changes to individual kinematic factors. Using this method allows to study the advective BDC trends in unprecedented detail and sheds new light into discrepancies between different datasets (reanalyses and models) at the tropopause and in the lower stratosphere. Our findings provide insights into the reliability of model projections of BDC changes and offer new possibilities for observational constraints.

Continued climate warming, together with the overall evaluation and implementation of a range of climate mitigation and adaptation approaches, has prompted increasing research into proposed solar climate intervention (SCI) methods, such as stratospheric aerosol injection (SAI). SAI would use aerosols to reflect a small amount of incoming solar radiation away from Earth to stabilize or reduce future warming due to increasing greenhouse gas concentrations. Research into the possible risks and benefits of SAI relative to the risks from climate change is emerging. There is not yet, however, an adequate understanding of how SAI might impact human and natural systems. For instance, little to no research to date has examined how SAI might impact environmental conditions critical to the formation of severe convective weather over the United States (U.S.). This study uses ensembles of Earth system model simulations of future climate change, with and without hypothetical SAI deployment, to examine possible future changes in thermodynamic and kinematic parameters critical to the formation of severe weather during convectively active seasons over the U.S. Results show that simulated forced changes in thermodynamic parameters are significantly reduced under SAI relative to a no-SAI world, while simulated changes in kinematic parameters are more difficult to distinguish. Also, unforced internal climate variability is likely to significantly modulate the projected forced climate changes over large regions of the U.S.

The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise-to-noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training dataset is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere-Ocean General Circulation Model (AOGCM) simulations. The neural network utilized for training is a U-Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single-model initial-condition large ensembles (SMILEs), the ensemble mean, and the U-Net’s predictions. The U-Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U-Net encounters challenges in areas dominated by forced variability, such as the Arctic sea ice retreat region. This methodology holds potential for extension to other physical variables, facilitating insights into the enduring changes triggered by external forcings over the long term.

The Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), and related North Atlantic Oscillation (NAO) have been linked to multidecadal, decadal, and/or interannual sea-ice variability in the arctic, but their relative influences are still under evaluation. While instrumental AMO and reliable AO records are available since the mid-1800s and 1958, respectively, satellite sea-ice concentration datasets start only in 1979, limiting the shared timespan to study their interplay. Growth increments of the coralline algae, Clathromorphum compactum, can provide sea-ice proxy information for years prior to 1979. We present a seasonal 210-year algal record from Lancaster Sound in the Canadian Arctic Archipelago capturing low frequency AMO variability and high frequency interannual AO/NAO prior to 2000. We suggest that sea-ice variability here is strongly coupled to these large-scale climate processes, and that sea-ice cover was greater and the AO more negative in the early and late 19th century compared to the 20th.

Dynamical models used in climate prediction often suffer from systematic errors that can deteriorate their predictions. We propose a hybrid model that combines both dynamical model and artificial neural network (ANN) correcting model errors to improve climate predictions. We conducted a series of experiments using the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM) and trained the ANN with input from both atmospheric and oceanic variables and output from analysis increments. Our results demonstrate that the hybrid model outperforms the dynamical model in terms of prediction skill for both atmospheric and oceanic variables across different lead times. Furthermore, we conducted additional experiments to identify the key factors influencing the prediction skill of the hybrid model. We found that correcting both atmospheric and oceanic errors yields the highest prediction skill while correcting only atmospheric or oceanic errors has limited improvement.

AbstractHere, we introduce the concept of “outdoor days” defined as those relatively pleasant days when most people may enjoy outdoor activities such as walking, jogging, and cycling. Although most climate studies primarily focus on changes in climate mean and/or extremes, projecting response of outdoor days to climate change is particularly important given their relevance to quality of life for communities. Here, we project how climate change reshapes seasonality of US outdoor days: relatively large drops in summer, late spring, and early fall; and a significant increase in winter. However, despite of global warming, annual outdoor days are projected to change only slightly, with notable exceptions. Consistent with recent observations, we project relatively large drops in southeast (-23%), south (-19%), and Ohio Valley (-18%), and a significant increase in northwest (14%) towards the end of the century. Our findings have implications for quality of life in different regions, and for nationwide travel and tourism.

Ziping Zuoa, Jimmy C.H. Funga,b, Zhenning Lia,*, Yiyi Huangd, Mau Fung, Wonga, Alexis K.H. Laua,c, Xingcheng Luea Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinab Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinac Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinad Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USAe Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, ChinaCorresponding author: Zhenning Li,[email protected] worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late 21st century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer (June–September) daytime and nighttime temperatures in the 2040s will be 0.7–0.8 °C and 0.9–1.1 °C, respectively. In the 2090s, the mean difference will be 0.5–3.1 °C and 0.7–3.4 °C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. Compared with those in the 2010s, hot extremes are expected to be more frequent, intense, extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4 °C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2–0.4 °C in all three scenarios. In the 2090s, the increase in these values will be 23 days and 36.0 °C in SSP5-8.5. Moreover, a 10-year extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September in the 2090s.SIGNIFICANCE STATEMENTPearl River Delta (PRD) has been experiencing record-shattering heatwaves in recent years. This study aims to investigate the future trends of summer heatwaves in the PRD by modeling three future scenarios including a sustainable scenario, an intermediate scenario, and a worst-case scenario. Except the sustainable scenario, summer temperatures in the intermediate and worst-case scenarios will keep increasing, and heatwaves will become more frequent, intense, extensive, and longer-lasting. In the worst-case scenario, extreme heat events that occurred once in 10 years in the 2010s will shorten to once a month in the 2090s. A better understanding of heatwave trends will benefit implementing climate mitigation methods, urban planning, and improving social infrastructure.

A document by David Gampe. Click on the document to view its contents.

This study presents a data-driven approach to quantify uncertainties in the quantities of interest (QoIs), i.e., electron density, plasma drifts, and neutral winds, in the ionosphere-thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp$<$4) and fixed solar radiation and lower atmospheric conditions representative of March 16th, 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM-IPE) driven by synthetic solar wind drivers generated through a multi-channel variational autoencoder (MCVAE) model are obtained. The means and variances of the QoIs, as well as the sensitivities of the QoIs with respect to the drivers, are estimated by applying the polynomial chaos expansion (PCE) technique. Our results highlight unique features of the IT system’s uncertainty: 1) the uncertainty of the IT system is larger during nighttime; 2) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector which is associated with the low density regions of longitude structure of electron density; 3) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance-based global sensitivity analysis is further conducted. Results suggest that the IMF Bz plays a dominant role in the uncertainty of the electron density when IMF Bz is 0 or southward, while the solar wind speed plays a dominant role when IMF Bz is northward.

The rapid growth of liquefied natural gas (LNG) exports underscores the importance of CO2 monitoring for LNG export terminals. This study presents a method for measuring CO2 emissions using remote sensing imaging spectroscopy applied to LNG terminals. The method is first validated using 47 power plant emission events with in situ measured data, then applied to 22 emission events in Sabine Pass and Cameron LNG terminals. The power plant dataset shows a robust correlation between our emission rate estimates and in situ data, with R2 0.9146 and the average error −2%. At Sabine Pass, 8 point sources are identified with emission rates ranging from 219.69 ± 54.95 to 1083.22 ± 308.06 t/hr. At Cameron, 3 point sources are identified with emission rates ranging from 91.64 ± 25.81 to 265.61 ± 67.80 t/hr. This study illustrates the potential of remote sensing to validate environmental reporting and CO2 inventories for industrial facilities.

Montane snowpack is a vital source of water supply in the Western United States. However, the future of snow in these regions in a changing climate is uncertain. Here, we use a large-ensemble approach to evaluate the consistency across 124 statistically downscaled snow water equivilent (SWE) projections between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and spring snowmelt (15 May) in five western US montane domains. By benchmarking SWE climate change signals using the disparity between snow projections, we identified relationships between SWE projections that were repeatable across each domain, but shifted in elevation. In low to mid-elevations, 15 April average projected decreases to SWE of 48% or larger were greater than the disparity between models. Despite this, a significant portion of 15 April SWE volume (39 – 93%) existed in higher elevation regions where the disparities between snow projections exceeded the projected changes to SWE. Results also found that 15 April and 15 May projections were strongly correlated (r 0.82), suggesting that improvements to the spread and certainty of 15 April SWE projections would translate to improvements in later dates. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify both 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit future snow projections.

Rainfed agriculture is the mainstay of economies across Southern Africa, where most precipitation is received during the austral summer monsoon. Despite that, seasonal precipitation predictability in Southern Africa is less explored. Here we use three natural climate forcings, El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD) – the dominant precipitation variability mode – to construct an empirical model that exhibits significant skill over Southern Africa during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50–75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over Southern Africa. This makes them less effective at predicting monsoon precipitation than the empirical model.

Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow-tourism economies. While the frequency and severity of snow droughts (SD) are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify. Using a 30-member large ensemble from a state-of-the-art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations-based dataset, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5-8.5 (SSP5-8.5) and 5 times more frequent under SSP2-4.5. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track April 15th snowpack across WUS watersheds, finding differences in the onset time of a “no-snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. For example, under SSP5-8.5, SPEAR projects California could experience no-snow anywhere between 2058 and 2096, while in the Pacific Northwest, the earliest transition happens in 2091. We attribute the inter-regional uncertainty to differences in the regions’ mean winter temperature and the intra-regional uncertainty to irreducible internal climate variability. This analysis indicates that internal climate variability will remain a significant source of uncertainty for WUS hydrology through 2100.

During the Great Oxygenation Event (GOE) 2.4 - 2.35 Gyrs ago, Earth experienced a notable shift in its oxidation state. This has been widely attributed to cyanobacteria having “invented” oxygenic photosynthesis.  We present a very different view based on the fact that oxygen is released from rocks during weathering. If so, the GOE must have been caused by an influx of abiogenic O2 into Earth’s surface environment. Minerals forming Earth’s crust contain impurity hydroxyls such as O3SiOH. Pairs of those are known to undergo a common redox conversion, ubiquitously forming peroxy defects plus H2: O3SiOH-HOSiO3 ⇌ O3Si/OO\SiO3 + H2 Being diffusively mobile, most H2 will be lost, leaving behind bound O as peroxy. The peroxy defects release O2 during weathering: O3Si/OO\SiO3 + H2O \(\to\) O3SiOH-HOSiO3 + \(\frac{1}{2}\) O2 We suggest that this abiogenically generated O2 was responsible for the progressive oxidation of the early Earth and that this abiotic process drove the Great Oxygenation Event (GOE).

Changes in the seasonal sea level cycle (SSLC) can modulate the flooding risk along coastlines. Here, we use harmonic analysis to quantify changes in the amplitude and phase of the annual component of the sea level cycle at 663 tide gauge locations along the global coastline where long records are available. We identify coastal hotspots by applying clustering methods revealing coherent regions with similar patterns of variability in the annual sea level cycle (ASLC). Results show that for most tide gauges the annual amplitude reached its maximum after 1970 and its peak typically occurs during the fall season of the respective hemisphere. Many tide gauges exhibit non-stationarity in the annual cycle in terms of amplitude and/or phase. For example, at 125 tide gauges we find significant trends in the amplitude (either increasing or decreasing) while several sites (36 in total), mostly in the Mediterranean and around Pacific islands, experienced phase changes leading to shifts in the timing of the peak of the annual cycle by more than a month. Our results highlight the importance of accounting for potential non-stationarity in seasonal mean sea level (MSL) cycles along coastlines.

Satellite altimetry sea surface height (SSH) measurements from 1993 to 2022 are used to show the strengthened mode-1 M2 internal tides in the past 30 years. Two mode-1 M2 internal tide models M9509 and M1019 are constructed using the data in 1995-2009 and 2010-2019, respectively. The results show that the global mean M2 internal tides strengthened by 6.6% in energy. However, the internal tide strengthening is spatially inhomogeneous. Significantly strengthened internal tides are observed in a number of regions including the Aleutian Ridge and the Madagascar-Mascarene region. Weakened internal tides are observed in the central Pacific. On a global average, M1019 leads M9509 by about 10o (20 minutes in time), suggesting that the propagation speeds of M2 internal tides increased. M9509 and M1019 are evaluated using independent altimetry data in 1993-1994 and 2020-2022. The results show that M9509 and M1019 perform better for the data in 1993-1994 and 2020-2022, respectively.

Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their prominent role in the meridional moisture transport and regional weather extremes, ARs have been studied extensively in recent years. Yet, the representations of ARs and their associated precipitation on a global scale remains largely unknown. In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the geostrophic winds derived from 3D geopotential height field from the combined retrievals of the Atmospheric Infrared Sounder and the Advanced Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables us to develop the first global AR catalog based solely on satellite observations. The satellite-based AR catalog is then combined with the satellite-based precipitation (Integrated Muti-SatellitE Retrievals for GPM) to evaluate the representations of ARs and AR-induced precipitation in reanalysis products. Our results show that the spreads in AR frequency and AR length distribution are generally small across datasets, while the spread in AR width is relatively larger. In terms of the AR-induced precipitation, both AR-induced mean and extreme precipitation are too weak nearly everywhere in reanalyses. However, all reanalyses tend to precipitate too often under AR conditions, especially over low latitude regions. This finding is consistent with the “drizzling” bias which has plagued generations of climate models. Overall, the findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and climate models.