Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

The U.S. rice paddy systems play an increasingly vital role in ensuring food security, which also contribute massive anthropogenic non-CO2 (CH4 and N2O) greenhouse gas (GHG) emissions with expanding cultivation area. Yet, the full assessment of GHG balance, considering trade-offs between soil organic carbon (SOC) sequestration and non-CO2 GHG emissions, is lacking. Integrating an improved agricultural ecosystem model with a meta-analysis of multiple field studies, we found that U.S. rice paddy was a rapidly growing net GHG emission source, increased 138% to 8.88 ± 2.65 Tg CO2eq yr-1 in the 2010s. CH4 emission made the most significant contribution (10.12 ± 2.28 Tg CO2eq yr-1) to this increase in net GHG emissions in the 2010s, but increasing N2O emissions, accounting for ~2.4% (0.21 ± 0.03 Tg CO2eq yr-1), cannot be ignored. SOC sequestration could offset about 14.0% (1.45 ± 0.46 Tg CO2eq yr1) of the climate-warming effects of soil non-CO2 GHG emissions in the 2010s. The aggravation of net GHG emissions stemmed from intensified land use/cover changes, rising atmospheric CO2, and heightened synthetic N fertilizer and manure application. Climate change exacerbated around ~21% of soil N2O emissions and ~10% of soil CO2 release in the 2010s. Nonetheless, adopting no/reduced tillage resulted in a substantial decrease of ~10 % in net soil GHG emissions, and non-continuous irrigation exhibited the potential to mitigate around 39% of soil non-CO2 GHG emissions. Great potential for emissions reduction in the mid-South U.S. by optimizing synthetic N fertilizer and manure ratios, reducing tillage, and implementing non-continuous irrigation.

As sea ice disappears, the emergence of open ocean deep convection in the Arctic has been suggested. Here, using 36 state-of-the-art climate models and up to 50 ensemble members per model, we show that Arctic deep convection is rare even under the strongest warming scenario. Only 5 models have somewhat permanent convection by 2100, while 11 have had convection by the middle of the run. For all, the deepest mixed layers are in the Eurasian basin, by St Anna Trough. When the models convect, that region undergoes a salinification and increasing wind speeds; it is freshening otherwise. We discuss the causality and potential reasons for the opposite trends. Given the model’s different parameterisations, and given that the ensemble members that convect the deepest, most often, are those with the strongest sensitivity, we conclude that differences in deep convection are most likely linked to the model formulation.

Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two-thirds of flood-exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program (NFIP). We study whether repeated exposure to flood events, especially disaster-scale floods expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take-up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take-up rate of insurance by 7% in the following year, but the effect diminishes in subsequent years and is gone after five years. This effect is more short-lived in counties in inland states that do not border the Gulf and Atlantic coasts. The effect of a flood on takeup is substantially larger if there was also a flood in the previous year. We also find that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non-primary residences. Our results provide a more comprehensive understanding of the salience effect of flooding on insurance demand compared to previous studies. Overall, these findings suggest that relying on households to self-adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap.

North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable locations in the world’s oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs do not need additional transfer learning to successfully predict the phasing of multidecadal variability in an observational dataset, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.

High-resolution climate projections provide crucial insights into assessing climate risk and developing climate resilience strategies. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR-ESDM) is a computationally-efficient and flexible approach to generating high-resolution climate projections that can be applied globally using a broad range of predictands and predictors that can be sourced from weather stations, gridded datasets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernal density estimation techniques to decompose and smooth any quasi-Gaussian time series, gridded or point-based, into multi-decadal long-term means and/or trends; static and dynamic annual cycles; and probability distributions of high-frequency variability. Long-term predictor trends are bias-corrected and predictor components are used to map remaining predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high-resolution bias-corrected and downscaled time series at the spatial and temporal scale of the original time series. Comparing STAR-ESDM output with high-resolution daily temperature and precipitation projections generated by a fully dynamical global model demonstrates that the method is extremely robust, capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1-in-1000 hottest and coldest temperatures are less than 0.5°C and biases in the 1-in-1000 wet day precipitation amounts are less than 5 mm/day. As climate impacts intensify, STAR-ESDM represents a significant advance in generating consistent high-resolution projections to comprehensively assess risk and optimize resilience.

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here we construct the first comprehensive bottom-up budgets of CO2, CH4, and N2O across the terrestrial permafrost region using databases of more than 1000 in-situ flux measurements and a land cover-based ecosystem flux upscaling approach for the period 2000-2020. Estimates indicate that the permafrost region emitted a mean annual flux of 0.36 (-620, 652) Tg CO2-C y-1, 38 (21, 53) Tg CH4-C y-1, and 0.62 (0.03, 1.2) Tg N2O-N y-1 to the atmosphere throughout the period. While the region was a net source of CH4 and N2O, the CO2 budget was near neutral with large uncertainties. Terrestrial ecosystems remained a CO2 sink, but emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. Including lateral fluxes, the permafrost region was a net source of C and N, releasing 136 (-517, 821) Tg C y-1 and 3.2 (1.9, 4.8) Tg N y-1. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory.

Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resources management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash-Sutcliffe Efficiency (NSE) coefficient, has been analyzed towards understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components – correlation, conditional bias, and unconditional bias – by four seasons, three lead times (1–12-day, 1-22 day, and 1-32 day), and three models (ECMWF, CFS, NCEP) over the Conterminous United States (CONUS). Application of dry mask is critical as the NSE and correlation are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months, and also performed better in in-land areas. Post-processing using simple Model Output Statistics could reduce both unconditional and conditional bias to zero, thereby offering better skill for regimes with high correlation. Our decomposition results also show efforts should focus on improving model parametrization and initialization schemes for climate regimes with low correlation values.

The long-term net sink of carbon (C), nitrogen (N) and greenhouse gases (GHGs) in the northern permafrost region is projected to weaken or shift under climate change. But large uncertainties remain, even on present-day GHG budgets. We compare bottom-up (data-driven upscaling, process-based models) and top-down budgets (atmospheric inversion models) of the main GHGs (CO2, CH4, and N2O) and lateral fluxes of C and N across the region over 2000-2020. Bottom-up approaches estimate higher land to atmosphere fluxes for all GHGs compared to top-down atmospheric inversions. Both bottom-up and top-down approaches respectively show a net sink of CO2 in natural ecosystems (-31 (-667, 559) and -587 (-862, -312), respectively) but sources of CH4 (38 (23, 53) and 15 (11, 18) Tg CH4-C yr-1) and N2O (0.6 (0.03, 1.2) and 0.09 (-0.19, 0.37) Tg N2O-N yr-1) in natural ecosystems. Assuming equal weight to bottom-up and top-down budgets and including anthropogenic emissions, the combined GHG budget is a source of 147 (-492, 759) Tg CO2-Ceq yr-1 (GWP100). A net CO2 sink in boreal forests and wetlands is offset by CO2 emissions from inland waters and CH4 emissions from wetlands and inland waters, with a smaller additional warming from N2O emissions. Priorities for future research include representation of inland waters in process-based models and compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more in-situ flux observations and improved resolution in upscaling.

We explore the sensitivity of Southern Ocean surface and deep ocean temperature and salinity biases in the FOCI coupled climate model to atmosphere-ocean coupling time step and to lateral diffusion in the ocean with the goal to reduce biases common to climate models. The reference simulation suffers from a warm bias at the sea surface which also extends down to the seafloor in the Southern Ocean and is accompanied by a too fresh surface, in particular along the Antarctic coast. Reducing the atmosphere-ocean coupling time step from 3 hours to 1 hour results in increased sea-ice production on the shelf and enhanced melting to the north which reduces the fresh bias of the shelf water while also strengthening the meridional density gradient favouring a stronger Antarctic Circumpolar Current (ACC). With the shorter coupling step we also find a stronger meridional overturning circulation with more upwelling and downwelling south and north of the ACC respectively, as well as a reduced warm bias at almost all depths. Tuning the lateral ocean mixing has only a small effect on the model biases, which contradicts previous studies using a similar model configuration. We note that the latitude of the surface westerly wind maximum has a northward bias in the reference simulation and that this bias is unchanged as the surface temperature and sea-ice biases are reduced in the coupled simulations. Hence, the surface wind biases over the Southern Hemisphere midlatitudes appear to be unrelated to biases in sea-surface conditions.

The hyporheic zone is a hotspot for biogeochemical cycling where interactions with mineral metals preserve the release and biodegradation of organic matter (OM). A small fraction of OM can still be exchanged between localized sediments and the overlying water column, and recent evidence suggest there exists a longitudinal structuring in sediment dissolved OM (DOM) chemistry across the continental United States (CONUS). In this study, we tested a hypothesis that water extractable sediment DOM chemistry could be explained by sediment metal contents and integrative watershed scale features at the CONUS scale. Crowdsourced samples were characterized for high resolution mass spectrometry and coupled with sediment metals determined via x-ray fluorescence as well as with land cover and soil elemental information obtained from national databases. Our results highlight weak relationships between DOM chemistry and elemental composition at the CONUS scale indicating limited transferability of organo-metal linkages into multi-scale hydrobiogeochemical models.

During atmospheric river (AR) landfalls on the Antarctic ice sheet, the high waviness of the circumpolar polar jet stream allows for sub-tropical air masses to be advected towards the Antarctic coastline. These rare but high-impact AR events are highly consequential for the Antarctic mass balance; yet little is known about the various atmospheric dynamical components determining their life cycle. By using an AR detection algorithm to retrieve AR landfalls at Dumont d’Urville and non-AR analogues based on 700 hPa geopotential height, we examined what makes AR landfalls unique and studied the complete life cycle of ARs to affect Dumont d’Urville. ARs form in the mid-latitudes/sub-tropics in areas of high surface evaporation, likely in response to tropical deep convection anomalies. These convection anomalies likely lead to Rossby wave trains that help amplify the upper-tropospheric flow pattern. As the AR approaches Antarctica, condensation of isentropically lifted moisture causes latent heat release that – in conjunction with poleward warm air advection – induces geopotential height rises and anticyclonic upper-level potential vorticity tendencies downstream. As evidenced by a blocking index, these tendencies lead to enhanced ridging/blocking that persist beyond the AR landfall time, sustaining warm air advection onto the ice sheet. Finally, we demonstrate a connection between tropopause polar vortices and mid-latitude cyclogenesis in an AR case study. Overall, the non-AR analogues reveal that the amplified jet pattern observed during AR landfalls is a result of enhanced poleward moisture transport and associated diabatic heating which is likely impossible to replicate without strong moisture transport.

A new detection and attribution method is presented and applied to the global mean surface air temperature (GSAT) from 1900 to 2014. The method aims at attributing the climate changes to the variations of greenhouse gases, anthropogenic aerosols, and natural forcings. A convolutional neural network (CNN) is trained using the simulated GSAT from historical and single-forcing simulations of twelve climate models. Then, we perform a backward optimization with the CNN to estimate the attributable GSAT changes. Such a method does not assume additivity in the effects of the forcings. The uncertainty in the attributable GSAT is estimated by sampling different starting points from single-forcing simulations and repeating the backward optimization. To evaluate this new method, the attributable GSAT changes are also calculated using the regularized optimal fingerprinting (ROF) method. Using synthetic non-additive data, we first find that the neural network-based method estimates attribuable changes better than ROF. When using GSAT data from climate model, the attribuable anomalies are similar for both methods, which might reflect that the influence of forcing is mainy additive for the GSAT. However, we found that the uncertainties given both methods are different. The new method presented here can be adapted and extended in future work, to investigate the non-additive changes found at the local scale or on other physical variables.

Long-term carbon dioxide (CO2) flux measurements between the atmosphere and the ecosystem have been made since 1993 at a cool-temperate deciduous forest site (Takayama) in Japan influenced by Asian Monsoon, constituting the longest dataset among all the AsiaFlux sites. Interannual variations (IAVs) and trends of the annual carbon budget components and their environmental factors were examined. Annual net ecosystem production (NEP) (mean ± 1σ) during the period of eddy covariance measurement in 1999-2021 was 265 ± 86 gC m-2 yr-1, and its IAV was dependent more on gross primary production (GPP) than on ecosystem respiration. IAVs in annual NEP and GPP were correlated with the IAVs of the monthly mean NEP, GPP and leaf area index (LAI) from June to September, as well as with that of the length of the net carbon uptake period. Significant increasing and decreasing trends in the annual NEP and GPP were detected during 2004-2013 and 2013-2021, respectively; the increasing trends were mainly caused by the vegetation recovery from typhoon disturbances while the decreasing trends were partly influenced by recent extreme weather events. Significant positive correlations of the IAVs between the start and the end of the net carbon uptake period, and between the leaf expansion and leaf fall were found. These may be attributed to biological functions and interseasonal relationship of meteorological parameters associated with ENSO events that can also influence IAVs in annual NEP and GPP.

The effects of contemporary increases in riverine freshwater into the Arctic Ocean are estimated from ocean model simulations, using two runoff data sets. One runoff data set which is based on older climatological data, which has no inter-annual variability after 2007 and as such does not represent the observed increases in river runoff into the Arctic. The other data set comes from a hydrological model developed for the Arctic drainage basin, which includes contemporary changes in the climate. In the pan-Arctic this new data set represents an approximately 11% increase in runoff, compared with the older climatological data. Comparing two ocean model runs forced with the different runoff data sets, overall changes in different freshwater markers across the basin were found to be between 5-10%, depending on the area investigated. The strongest increases were seen from the Siberian rivers, which in turn caused the strongest freshening in the Eastern Arctic.

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here we construct the first comprehensive bottom-up budgets of CO2, CH4, and N2O across the terrestrial permafrost region using databases of more than 1000 in-situ flux measurements and a land cover-based ecosystem flux upscaling approach for the period 2000-2020. Estimates indicate that the permafrost region emitted a mean annual flux of 0.36 (-620, 652) Tg CO2-C y-1, 38 (21, 53) Tg CH4-C y-1, and 0.62 (0.03, 1.2) Tg N2O-N y-1 to the atmosphere throughout the period. While the region was a net source of CH4 and N2O, the CO2 budget was near neutral with large uncertainties. Terrestrial ecosystems remained a CO2 sink, but emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. Including lateral fluxes, the permafrost region was a net source of C and N, releasing 136 (-517, 821) Tg C y-1 and 3.2 (1.9, 4.8) Tg N y-1. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory.

Given the possibility of irreversible changes to the Earth system, technological interventions such as solar radiation management (SRM) are sometimes framed as possible climate emergency brakes. However, little knowledge exists on the efficacy of such disruptive interventions. To fill in this gap, we perform Community Earth System Model 2 (CESM 2) simulations of a SSP5-8.5 scenario on which we impose either gradual early-century SRM to stabilise surface temperatures or a rapid late-century cooling, both realised via stratospheric aerosol injection (SAI). While both scenarios cool Earth’s surface, we find that ocean conditions differ drastically. The rapid-cooling scenario fails to dissipate sub-surface ocean heat content (OHC), ends up in a weaker AMOC state and does not restore an ailing North Atlantic deep convection. Furthermore, the weakened AMOC state mediates the climate response to rapid SAI, thus inducing an interhemispheric temperature asymmetry. Our results advise caution when considering SAI as an emergency intervention.

Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a machine-learned parameterization. We machine-learn the coarsened-fine cloud properties as a function of coarse-grid model state in each grid cell of NOAA’s FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3 km fine-grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under-dispersed, resulting in too many partially-cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS’s radiation scheme, they lead to small biases in global-mean top-of-atmosphere (TOA) and surface radiative fluxes. An unbiased global mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML-derived radiative fluxes are  far more accurate than those obtained with the existing cloud parameterization in the nudged coarse-grid model, as they leverage the accuracy of the fine-grid reference simulation’s cloud properties. 

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: p.scussolini@vu.nl† 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.