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State-of-the-art Earth system models typically employ grid spacings of O(100 km), which is too coarse to explicitly resolve main drivers of the flow of energy and matter across the Earth system. In this paper, we present the new ICON-Sapphire model configuration, which targets a representation of the components of the Earth system and their interactions with a grid spacing of 10 km and finer. Through the use of selected simulation examples, we demonstrate that ICON-Sapphire can (i) be run coupled globally on seasonal timescales with a grid spacing of 5 km, on monthly timescales with a grid spacing of 2.5 km, and on daily timescales with a grid spacing of 1.25 km; (ii) resolve large eddies in the atmosphere using hectometer grid spacings on limited-area domains in atmosphere-only simulations; (iii) resolve submesoscale ocean eddies by using a global uniform grid of 1.25 km or a telescoping grid with the finest grid spacing at 530 m, the latter coupled to a uniform atmosphere; and (iv) simulate biogeochemistry in an ocean-only simulation integrated for 4 years at 10 km. Comparison of basic features of the climate system to observations reveals no obvious pitfalls, even though some observed aspects remain difficult to capture. The throughput of the coupled 5 km global simulation is 126 simulated days per day employing 21 % of the latest machine of the German Climate Computing Center. Extrapolating from these results, multi-decadal global simulations including interactive carbon are now possible, and short global simulations resolving large eddies in the atmosphere and submesoscale eddies in the ocean are within reach.

Natural gas is considered a bridging technology in the energy transition because it produces fewer carbon emissions than coal, for example. However, when leaks exist, methane is released into the atmosphere, leading to a dramatic increase in the carbon footprint of natural gas, as methane is a much stronger greenhouse gas than carbon dioxide. Therefore, we conducted a detailed study of methane emissions from gas-powered end-use appliances and then compared their climate impacts with those of electricity-powered appliances. We used the Munich Oktoberfest as a case study and then extended the study to 25 major natural gas consuming countries. This showed that electricity has been the more climate-friendly energy source at Oktoberfest since 2005, due to the extensive use of renewable electricity at the festival and the presence of methane emissions, particularly caused by the incomplete combustion of natural gas in cooking and heating appliances. By contrast, at the global level, our study shows that natural gas still produces lower end-use carbon emissions than electricity in 18 of the 25 countries studied. However, as the share of renewable energy in the electricity mix steadily increases in most countries, the carbon footprint of electricity will be lower than that of natural gas in these countries in the near future. These findings from our comparison of the total carbon emissions of electric and gas-powered end-use appliances can help inform the debate on how to effectively address climate change., Earth's Future, 11 (2), ISSN:2328-4277

Forests perform many important ecological functions and provide numerous environmental services to humanity. Currently forests are under ever increasing pressures from humans through deforestation, changes in land use, and anthropogenic climate change. Managed forests play an important role in supplying forest products to the global population, necessitating the need to predict how forests will respond to climate change and how this will influence future wood product supplies. In this dissertation I used loblolly pine (Pinus taeda), the most extensively cultivated tree species in the United States, as a study system to simulate how climate change and forest management could alter the dynamics of managed forests in the future. Using the land component (the Community Land Model) of the widely used Community Earth System Model (CESM), I developed and validated a set of tools necessary to simulate the loblolly pine plantation system using the vegetation demography model embedded in CESM (FATES). This included developing a representation of loblolly pine using data from the literature, which was better able to capture forest growth and development observed in field studies than FATES's existing conifer tree representation. I added the ability to simulate several aspects of forest management not previously supported in FATES by creating the Vegetation Management Module, which I showed was able to realistically reproduce the common management practice of stand thinning. I used these new tools to perform simulations of how loblolly pine will grow across the Southeastern United States until the end of the 21st century, under the high and low climate change scenarios developed by the scientific community in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our experiments show that loblolly pine productivity may as much as double by the end of the century, with total wood harvest over that period increasing by almost half. I also showed that different management activities had significant effects on loblolly plantation yields, with mid-rotation stand thinning having an effect under both climate scenarios on par with increases due to the extreme climate change scenario SSP5 RCP8.5. I showed that these changes in wood yields could decrease the forest area in the Southeast required to meet the wood product demands over the rest of the century. These changes in plantation productivity could interact with socioeconomic factors to drive changes in land use and carbon storage in the Southeastern U.S. This work increases our understanding of how managed forests in the U.S.\ will be affected by climate change and how our management choices modulate that response. The techniques and tools developed here open up new areas of research into the role of forest management in the climate system. Doctor of Philosophy Forests benefit humans by regulating Earth's climate and by providing natural resources such as wood. In the Southeastern United States forestry is an important industry. Tree farms of southern pine trees produce a large percentage of the region's wood. Predicting how forests will grow in the future is important for planning and making investments. However, the burning of fossil fuels has increased carbon dioxide in the atmosphere and is changing Earth's climate. This is affecting how fast trees grow and how much wood can be harvested from forests. The methods that foresters have traditionally used to predict how trees will grow in the future do not account for climate change, and thus may not be as accurate in the future. An alternative is to use the computer models that scientists have developed to predict both how global climate will change in the future and how forests are influenced by climate. These computer programs can be used to predict how natural forests will grow in the future, but aren't set up to predict managed forests well. I made changes to one of these programs to make it possible to simulate the managed loblolly pine forests of the Southeastern United States. First, I tested these changes to make sure that simulated forests grew like real forests do today. Then I simulated how pine forests in Southeastern United States could grow over the next century with climate change. I found that pine forests will grow faster and allow more wood to be harvested as carbon dioxide in the atmosphere increases. If climate changes are extreme, loblolly forests could produce 70\% more wood than today by the end of the 21st century. I also showed that the manner in which forests were managed in simulations changes the amount of wood they produced, with some management practices increasing wood harvested by 50\% over the rest of the century. Because climate change could increase the amount of wood that can be produced from a fixed area of forest, I investigated how this might change the area of forest plantation in the Southeastern United States. Based on projections of demand for wood for the rest of the century I calculated how much loblolly pine forest would be needed to produce this wood over the next century. I found that increases in forest productivity due to climate change and forest management could decrease the forest area required to grow the wood we need. This could change how we use forests in the Southeastern United States, which in turn could have impacts on the climate.

Communities in the Pamir Mountains of Central Asia are among the most vulnerable to climate change due to their geographic location and subsistence-based livelihoods. Historically, ecological calendars supported their agropastoral lifestyles which provided anticipatory capacity to seasonal changes. Due to decades of Soviet colonization and socioecological transformations, knowledge of these ecological calendars fell into disuse. In 2016, Savnob and Roshorv, two villages in the Bartang Valley of Tajikistan, began the revitalization of these calendars using a participatory action research process through knowledge co-generation. We undertook a comparative analysis to investigate the importance of context-specificity to ensure food security and reduce their vulnerability to climate change. A preliminary analysis of the temperature regime and local language terms, relating to the positioning and quality of land, framed our methods-of-analysis. We compared the villagers' ecological calendars by focusing on indicator species, potentially threatening weather events, land-use, livelihood activities, and the role of the vernal equinox. Despite their close geographic proximity, context-specificity determined by distinct microecologies influences the timing and practice of these communities' livelihood activities. These villages have different dependencies on biotic and abiotic events, crops, and land-use; all of which affect food security and survival. These differences contributed to mutual support between the two villages, increased the availability of food, and thereby, lowered their vulnerability to climate change. As Savnob's and Roshorv's ecological calendars are updated with changing climate, they can once again enhance their anticipatory capacity while reducing their vulnerability.

Burial driven recycling is an important process in the natural gas hydrate (GH) systems worldwide, characterized by complex multiphysics interactions like gas migration through an evolving gas hydrate stability zone (GHSZ), competing gas-water-hydrate (i.e., fluid-fluid-solid) phase transitions, locally appearing and disappearing phases, and evolving sediment properties (e.g., permeability, reaction surface area, and capillary entry pressure). Such a recycling process is typically studied in homogeneous or layered sediments. However, there is mounting evidence that structural heterogeneity and anisotropy linked to normal and inclined fault systems or anomalous sediment layers have a strong impact on the GH dynamics. Here, we consider the impacts of such a structurally complex media on the recycling process. To capture the properties of the anomalous layers accurately, we introduce a fully mass conservative, high-order, discontinuous Galerkin (DG) finite element based numerical scheme. Moreover, to handle the rapidly switching thermodynamic phase states robustly, we cast the problem of phase transitions as a set of variational inequalities, and combine our DG discretization scheme with a semi-smooth Newton solver. Here, we present our new simulator, and demonstrate using synthetic geological scenarios, (a) how the presence of an anomalous high-permeability layer, like a fracture or brecciated sediment, can alter the recycling process through flow-localization, and more importantly, (b) how an incorrect or incomplete approximation of the properties of such a layer can lead to large errors in the overall prediction of the recycling process. Key Points Structural heterogeneity linked to inclined fault systems or anomalous sediment layers have a strong impact on the gas hydrate dynamics The presence of anomalous high-permeability layers within gas hydrate stability zone alters the recycling process through flow-localization The presented discontinuous Galerkin scheme is able to accurately capture the gas hydrate recycling processes through strongly anisotropic materials

Using the global and coupled ICOsahedral Nonhydrostatic model with the Sapphire configuration (ICON-S) and a grid spacing of 5km, we describe seasonal and diurnal features of the tropical rainbelt and assess the limits of ICON-S in representing tropical precipitation. ICON-S shows that, by resolving meso-beta scale process, the rainbelt structure and its seasonality (zonal and meridional migration and enlargement) is reproduced, with better performance over land than over ocean and with a very high degree of agreement to observations. ICON-S especially struggles in capturing the seasonal features of the tropical rainbelt over the oceans of the Eastern Hemisphere, an issue associated with a cold sea surface temperature (SST) bias at the equator. ICON-S also shows that a perfect representation of the diurnal cycle of precipitation over land is not a requirement to capture the seasonal features of the rainbelt over land, while over the ocean, 5km is sufficient to adequately represent the diurnal cycle of precipitation. The source code for the ICON model in the Sapphire configuration referenced in this publication is stored here: https://zenodo.org/record/7945441#.ZGUA-qVBxoE . Alternatively, refer to the data availability statement under the field 'Additional Notes'. Plain Language Summary Over the tropics, precipitation falls in distinct bands, that span the circumference of the Earth. These bands migrate from the Northern to the Southern Hemisphere and vice versa following the seasonal migration of the sun. Their center of mass also varies east-west, as well as their area. Where rain ends up falling is of key importance but conventional climate models relying on statistical approaches to simulate convection cannot represent these characteristics. Here we report on the results of simulations on a global domain and, to our knowledge, for the first time integrated with an atmosphere-ocean coupled over a full seasonal cycle and with a grid spacing fine enough to explicitly represent convection and Mesoscale Ocean eddies. We show that such simulations can reproduce many aspects of the seasonal migration of the rainbelt over land. For instance, the north-south and east-west migration of the rainbelt as well as its expansion during the summer season are well captured. This is also the case for the rainbelt in the eastern Pacific and the Atlantic, but not in the Eastern Hemisphere, where the poor representation of the sea surface temperature pattern distorts the representation of the rainbelt and its seasonal characteristics. ---------------------------- Acknowledgments The Hans-Ertel Centre for Weather Research under project number 4818DWDP1A funded H. Segura. The European Union's Horizon 2020 research and innovation program project NextGEMS funded C. Hohenegger and B. Stevens under the grant agreement number 101003470. The European Horizon 2020 project CONSTRAIN (project number 493B) and the DKRZ compute time (project bm1235 and bb1153) are acknowledged by the authors. We thank Lukas Kluft for his comments in the internal review process. We thank the two anonymous reviewers for their insightful comments. Open Access funding enabled and organized by Projekt DEAL.

Mesoscale ocean eddies, an important element of the climate system, impact ocean circulation, heat uptake, gas exchange, carbon sequestration and nutrient transport. Much of what is known about ongoing changes in ocean eddy activity is based on satellite altimetry; however, the length of the altimetry record is limited, making it difficult to distinguish anthropogenic change from natural variability. Using a climate model that exploits a variable-resolution unstructured mesh in the ocean component to enhance grid resolution in eddy-rich regions, we investigate the long-term response of ocean eddy activity to anthropogenic climate change. Eddy kinetic energy is projected to shift poleward in most eddy-rich regions, to intensify in the Kuroshio Current, Brazil and Malvinas currents and Antarctic Circumpolar Current and to decrease in the Gulf Stream. Modelled changes are linked to elements of the broader climate including Atlantic meridional overturning circulation decline, intensifying Agulhas leakage and shifting Southern Hemisphere westerlies. Data Availability Geostrophic velocity anomalies derived from satellite altimetry anomalies are publicly available at https://doi.org/10.48670/moi-00148. Model output from AWI-CM-1-1-MR in the CMIP6 framework, including ocean velocity and surface wind is publicly available at https://doi.org/10.22033/ESGF/CMIP6.359 (ref. 35). Daily sea surface height data used in this study is archived at the World Data Center for Climate at the DKRZ. Code Availability The code used to calculate geostrophic velocities and eddy kinetic energy according to the methods described in this paper and to produce the main analysis figures is available on Github GitHub - n-beech/awicm-cmip6-eke at v1.1 or can alternatively also be accessed via n-beech/awicm-cmip6-eke: 09-09-22 | Zenodo ------------------------------ Acknowledgements The work described in this paper has received funding from the Helmholtz Association through the project 'Advanced Earth System Model Capacity' (project leader: T.J., support code: ZT-0003) in the frame of the initiative 'Zukunftsthemen'. The content of the paper is the sole responsibility of the authors and it does not represent the opinion of the Helmholtz Association, and the Helmholtz Association is not responsible for any use that might be made of information contained. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID 995. The CMIP data used in this study were replicated and made available by the DKRZ. This work was supported by the European Union's Horizon 2020 collaborative project NextGEMS (grant number 101003470). This work was supported by the Helmholtz Climate Initiative REKLIM (Regional Climate Change) and the EPICA project in the research theme 'MARE:N—Polarforschung/MOSAiC' funded by the German Federal Ministry for Education and Research with funding number 03F0889A.

Management of hydropower plants strongly influences streamflow dynamics and hence the interaction between surface water and groundwater. As dam operations cause variations in river stages, these can result in changes in the groundwater level at multiple temporal scales. In this work, we study the case of an Alpine aquifer, where weekly fluctuations are particularly pronounced. We consider an area with four river reaches differently impacted by reservoir operations and investigate the influence of these rivers on the common aquifer. Using continuous wavelet transform and wavelet coherence analysis, we show that weekly fluctuations in the groundwater table are particularly pronounced in dry years, in particular in the winter season, although the area of the aquifer impacted by dam operations remains almost unchanged. We thus observe that in Alpine catchments, surface water‐groundwater interaction is sensitive to the conditions determined by a specific hydrological year. We also investigate the influences of the river‐aquifer water fluxes and show that under dry conditions hydropeaking mainly affects their temporal dynamics. Our observations have significant consequences for predicting nutrient and temperature dynamics/regimes in river‐aquifer systems impacted by hydropower plant management., Plain Language Summary: The operation of hydropower plants affects the water level in the downstream part of the river, which in turn can alter the groundwater level. In this work, we study an Alpine aquifer crossed by rivers differently impacted by hydropower production. We use statistical tools to analyze the interaction between the rivers and the groundwater, and observe that this interaction is sensitive to the conditions of the hydrological year, such as dry periods., Key Points: Wavelet power spectrum and coherence analysis is used to study river‐aquifer interactions under dam operations in an Alpine catchment. The impact of reservoir operations on the aquifer is strongest under low flow conditions but the area impacted shows little variation. Under low flow conditions, dam operations considerably influence the frequency of the water exchange between rivers and aquifer., Consejo Nacional de Ciencia y Tecnología http://dx.doi.org/10.13039/501100003141, Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659, Consejo Veracruzano de Investigación Científica y Desarrollo Tecnológico, https://doi.org/10.17632/97jchhz4s8.2

Forests mitigate climate change by storing carbon and reducing emissions via substitution effects of wood products. Additionally, they provide many other important ecosystem services (ESs), but are vulnerable to climate change; therefore, adaptation is necessary. Climate‐smart forestry combines mitigation with adaptation, whilst facilitating the provision of many ESs. This is particularly challenging due to large uncertainties about future climate. Here, we combined ecosystem modeling with robust multi‐criteria optimization to assess how the provision of various ESs (climate change mitigation, timber provision, local cooling, water availability, and biodiversity habitat) can be guaranteed under a broad range of climate futures across Europe. Our optimized portfolios contain 29% unmanaged forests, and implicate a successive conversion of 34% of coniferous to broad‐leaved forests (11% vice versa). Coppices practically vanish from Southern Europe, mainly due to their high water requirement. We find the high shares of unmanaged forests necessary to keep European forests a carbon sink while broad‐leaved and unmanaged forests contribute to local cooling through biogeophysical effects. Unmanaged forests also pose the largest benefit for biodiversity habitat. However, the increased shares of unmanaged and broad‐leaved forests lead to reductions in harvests. This raises the question of how to meet increasing wood demands without transferring ecological impacts elsewhere or enhancing the dependence on more carbon‐intensive industries. Furthermore, the mitigation potential of forests depends on assumptions about the decarbonization of other industries and is consequently crucially dependent on the emission scenario. Our findings highlight that trade‐offs must be assessed when developing concrete strategies for climate‐smart forestry., Plain Language Summary: Forests help mitigate climate change by storing carbon and via avoided emissions when wood products replace more carbon‐intensive materials. At the same time, forests provide many other “ecosystem services (ESs)” to society. For example, they provide timber, habitat for various species, and they cool their surrounding regions. They are, however, also vulnerable to ongoing climate change. Forest management must consider all these aspects, which is particularly challenging considering the uncertainty about future climate. Here, we propose how this may be tackled by computing optimized forest management portfolios for Europe for a broad range of future climate pathways. Our results show that changes to forest composition are necessary. In particular, increased shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs. However, these increased shares also lead to decreases in harvest rates, posing a conflict between wood supply and demand. We further show that the mitigation potential of forests strongly depends on how carbon‐intensive the replaced materials are. Consequently, should these materials become “greener” due to new technologies, the importance of wood products in terms of climate change mitigation decreases. Our study highlights that we cannot optimize every aspect, but that trade‐offs between ESs need to be made., Key Points: Strategies for climate‐smart forestry under a range of climate scenarios always lead to trade‐offs between different ecosystem services (ESs). Higher shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs, but lead to decreased timber provision. The mitigation potential of forests strongly relies on substitution effects which depend on the carbon‐intensity of the alternative products., European Forest Institute (EFI) Networking Fund http://dx.doi.org/10.13039/501100013942, Bayerisches Staatsministerium für Wissenschaft und Kunst, Bayerisches Netzwerk für Klimaforschung (BayKliF) http://dx.doi.org/10.13039/501100004563, Swedish Research Council Formas, German Federal Office for Agriculture and Food (BLE), https://doi.org/10.5281/zenodo.6667489, https://doi.org/10.5281/zenodo.6612953

Detailed imaging of accretionary wedges reveals splay fault networks that could pose a significant tsunami hazard. However, the dynamics of multiple splay fault activation during megathrust earthquakes and the consequent effects on tsunami generation are not well understood. We use a 2‐D dynamic rupture model with complex topo‐bathymetry and six curved splay fault geometries constrained from realistic tectonic loading modeled by a geodynamic seismic cycle model with consistent initial stress and strength conditions. We find that all splay faults rupture coseismically. While the largest splay fault slips due to a complex rupture branching process from the megathrust, all other splay faults are activated either top down or bottom up by dynamic stress transfer induced by trapped seismic waves. We ascribe these differences to local non‐optimal fault orientations and variable along‐dip strength excess. Generally, rupture on splay faults is facilitated by their favorable stress orientations and low strength excess as a result of high pore‐fluid pressures. The ensuing tsunami modeled with non‐linear 1‐D shallow water equations consists of one high‐amplitude crest related to rupture on the longest splay fault and a second broader wave packet resulting from slip on the other faults. This results in two episodes of flooding and a larger run‐up distance than the single long‐wavelength (300 km) tsunami sourced by the megathrust‐only rupture. Since splay fault activation is determined by both variable stress and strength conditions and dynamic activation, considering both tectonic and earthquake processes is relevant for understanding tsunamigenesis., Plain Language Summary: In subduction zones, where one tectonic plate moves beneath another, earthquakes can occur on many different faults. Splay faults are relatively steep faults that branch off the largest fault (the megathrust) in a subduction zone. As they are steeper than the megathrust, the same amount of movement on them could result in more vertical displacement of the seafloor. Therefore, splay faults are thought to play an important role in the generation of tsunamis. Here, we use computer simulations to study if an earthquake can break multiple splay faults at once and how this affects the resulting tsunami. We find that multiple splay faults can indeed fail during a single earthquake due to the stress changes from trapped seismic waves, which promote rupture on splay faults. Rupture on splay faults results in larger seafloor displacements with smaller wavelengths, so the ensuing tsunami is bigger and results in two main flooding episodes at the coast. Our results show that it is important to consider rupture on splay faults when assessing tsunami hazard., Key Points: Multiple splay faults can be activated during a single earthquake by megathrust slip and dynamic stress transfer due to trapped waves. Splay fault activation is facilitated by their favorable orientation with respect to the local stress field and their closeness to failure. Long‐term geodynamic stresses and fault geometries affect dynamic splay fault rupture and the subsequent tsunami., Volkswagen Foundation (VolkswagenStiftung) http://dx.doi.org/10.13039/501100001663, Royal Society (The Royal Society) http://dx.doi.org/10.13039/501100000288, EC | H2020 | H2020 Priority Excellent Science | H2020 European Research Council (ERC) http://dx.doi.org/10.13039/100010663, Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659, National Science Foundation (NSF) http://dx.doi.org/10.13039/100000001, https://github.com/TUM-I5/SWE, https://doi.org/10.5281/zenodo.6969455

We use a statistical emulation technique to construct synthetic ensembles of global and regional sea-air carbon dioxide (CO2) flux from four observation-based products over 1985–2014. Much like ensembles of Earth system models that are constructed by perturbing their initial conditions, our synthetic ensemble members exhibit different phasing of internal variability and a common externally forced signal. Our synthetic ensembles illustrate an important role for internal variability in the temporal evolution of global and regional CO2 flux and produce a wide range of possible trends over 1990–1999 and 2000–2009. We assume a specific externally forced signal and calculate the rank of the observed trends within the distribution of statistically modeled synthetic trends during these periods. Over the decade 1990–1999, three of four observation-based products exhibit small negative trends in globally integrated sea-air CO2 flux (i.e., enhanced ocean CO2 absorption with time) that are within one standard deviation of the mean in their respective synthetic ensembles. Over the decade 2000–2009, however, three products show large negative trends in globally integrated sea-air CO2 flux that have a low rate of occurrence in their synthetic ensembles. The largest positive trends in global and Southern Ocean flux over 1990–1999 and the largest negative trends over 2000–2009 fall nearly two standard deviations away from the mean in their ensembles. Our approach provides a new perspective on the important role of internal variability in sea-air CO2 flux trends, and furthers understanding of the role of internal and external processes in driving observed sea-air CO2 flux variability. Key Points We construct synthetic large ensembles of observed sea-air carbon dioxide flux using a statistical emulation technique The synthetic large ensembles illustrate an important role for internal variability in the temporal evolution of carbon dioxide flux We find a wide range of possible decadal trends in carbon dioxide flux over 1990–1999 and 2000–2009 driven by internal variability

Early Eocene hyperthermals are geologically short-lived global warming events and represent fundamental perturbations to the global carbon cycle and the Earth’s ecosystem due to massive additions of isotopically light carbon to the ocean-atmosphere system. They serve as ancient analogs for understanding how the oceanic carbonate system and surface-ocean ecosystem would respond to ongoing and future climate change. Here, we present a continuous carbonate record across the Eocene Thermal Maximum 2 (ETM2 or H1, ca. 54.1 ​Ma) and H2 (ca. 54 Ma) events from an expanded section at Ocean Drilling Program Site 1258 in tropical Atlantic. The abundant calcareous nannofossils and moderate carbonate content throughout the studied interval suggest this record was deposited above the calcite compensation depth (CCD), but below the lysocline and under the influence of terrestrial dilution. An Earth system model cGENIE is used to simulate the carbon cycle dynamics across the ETM2 and H2 to offer insights on the mechanism of the rapid warming and subsequent recovery in climate and ecosystem. The model suggests moderate changes in ocean pH (0.1–0.2 unit) for the two scenarios, biogenic methane from a rechargeable methane capacitor and organic matter oxidation from thawing of the permafrost. These pH changes are consistent with a recent independent pH estimate across the ETM2 using boron isotopes. The carbon emission flux during the ETM2 is at least an order of magnitude smaller than that during the Paleocene–Eocene Thermal Maximum (PETM) (0.015–0.05 ​Pg ​C yr−1 vs. 0.3–1.7 ​Pg ​C yr−1). The comparable pre- and post-event carbonate contents suggest the lysocline did not over deepen following the ETM2 at this tropical Atlantic site, indicating spatial heterogeneity in the carbonate system due to strong dilution influence from terrestrial weathering and riverine discharge at Site 1258.

The largest mass extinction event in the Phanerozoic, known as the end-Permian mass extinction (or EPME, ca. 252 Ma) is coincident with the main eruption phase of Siberian Traps volcanism (ca. 252 to 250 Ma), a large igneous province (LIP). This LIP is estimated to have a volume larger than 2 × 106 km3 and to have released both mantle carbon dioxide (CO2) through extrusions and thermogenic methane (CH4) and carbon dioxide (CO2) through intrusions. The climatic consequences of these greenhouse gases is detrimental to both marine and terrestrial life and may have delayed the recovery of ecosystems after the extinction. Quantitatively, the amount of CO2 released can be estimated using global carbon (C) cycle model or plant and soil-based proxies with varying time resolution. In light of the recent advances in geochemical proxies of ocean anoxia and acidification, we review the latest uranium isotopes (δ238U) and calcium isotope (δ44/40Ca) records and Earth system modeling results to summarize the environmental response to the forcing of increased atmospheric CO2 concentrations. The extent of increase in oceanic anoxic area can be estimated by δ238U, and the δ44/40Ca records may be used to evaluate ocean acidification. This evidence suggests that excessive nutrient load in the ocean (decreased strength of meridional overturning circulation) and ocean acidification in poorly buffered seawater (potentially triggered by the Siberian Traps LIP) worked together to create the most severe biological crisis and delayed recovery of life in the Earth’s history.

Sediment dynamics play an important role in various aspects of earth system modeling. In this study, we developed a global sediment dynamics model that considers suspended sediment and bedload at short timescales. We validated suspended sediment from four observation stations in the Amazon River basin and over 60 observation stations from around the world based on a variable criteria such as availability of data samples. Our model was able to effectively reproduce seasonality and spatial distribution of suspended sediment flow. However, our global estimate of approximately 4 Bt/a was significantly lower than previous estimates; therefore, we discuss potential causes of this discrepancy, including target time period and discrepancies with previous extrapolated methods. Our newly developed sediment dynamics model could provide a better understanding of global sediment transfer and contributes to various related research fields such as coastal modeling and natural disasters.

Acute exacerbation of chronic obstruction pulmonary disease (AECOPD) as a respiratory disease, is considered to be related to air pollution by more and more studies. However, the evidence on how air pollution affect the incidence of AECOPD and whether there are population differences is still insufficient. Therefore, we select PM10, PM2.5, SO2, NO2, CO, and O3 as representatives combined with daily AECOPD admission data from 1 January 2015 to 26 June 2016 in the rural areas of Qingyang, northwestern China to explore the associations of air pollution with AECOPD. Based on a time‐stratified case‐crossover design, we constructed a distributed lag nonlinear model to qualify the single and cumulative lagged effects of air pollution on AECOPD. Stratified related risks by sex and age were also reported. The cumulative exposure‐response curves were approximately linear for PM2.5, “V”‐shaped for PM10, “U”‐shaped for NO2 and inverted‐“V” for SO2, CO and O3. Exposure to high‐PM2.5 (42 μg/m3), high‐PM10 (91 μg/m3), high‐SO2 (58 μg/m3), low‐NO2 (12 μg/m3), and high‐CO (1.55 mg/m3) increased the risk of AECOPD. Females aged 15–64 were more susceptible under extreme concentrations of PM2.5, SO2, CO, and low‐PM10 than other subgroups. In addition, adults aged 15–64 were more sensitive to extreme concentrations of NO2 compared with the elderly ≥65 years old, while the latter were more sensitive to high‐PM10. High‐SO2, high‐NO2, and extreme concentrations of PM2.5 had the greatest effects on the day of exposure, while low‐SO2 and low‐CO had lagged effects on AECOPD. Precautionary measures should be taken with a focus on vulnerable subgroups, to control hospitalization for AECOPD associated with air pollutants., Key Points Exposure to high‐PM2.5, high‐PM10, high‐SO2, low‐NO2, and high‐CO increased the risk of acute exacerbation of chronic obstruction pulmonary disease (COPD)The cumulative curves were approximately linear for PM2.5, “V”‐shaped for PM10, “U”‐shaped for NO2 and inverted‐”V” for SO2, CO and O3 The nonlinear effects on acute exacerbation of COPD at different lags varied based on the air pollutants, involved gender and age

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts., Key Points Extreme heatwave induced ∼4°C increase in temperature in rural Chicago while increased urban temperature by 2–3°CUrban heat island intensity is estimated to be around 1.44–2.38°CExcessive Heat Factor is higher than 50°C2 in urban Chicago due to heatwave

9 pages; International audience; Particularly dry or wet boreal summer monsoon seasons are major hazards affecting societal vulnerability in India and Africa. Several factors affect monsoon rainfall amount and limit the understanding of possible linkages between monsoon variability and mean climate changes. Here we characterize the multiscale variability of Indian and West African monsoon rain from two simulations of the last 6,000 years. Changes in Earth's orbit cause long‐term monsoon drying trend in India and Africa, but the Indian monsoon is more sensitive to anthropogenic CO2. Variability is characterized by two major ranges of chaotic variability, each related to specific ocean‐atmosphere modes present throughout the period. Combination of random 50‐ to 500‐ and 2‐ to 20‐year variability leads to large events occurring at millennium scale. However, the two regions exhibit opposite trends in rainfall variability due to changes in teleconnection with Pacific sea surface temperature for India and Atlantic sea surface temperature for West Africa at interannual to decadal timescales.

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5. National Science Foundation (NSF)National Science Foundation (NSF); National Center for Atmospheric Research - NSF [1852977]; RUBISCO Scientific Focus Area (SFA) - Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science; Columbia University Presidential Fellowship; U.S. Department of Agriculture NIFA Award [2015-67003-23485]; NASA Interdisciplinary Science Program Award [NNX17AK19G]; U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science programUnited States Department of Energy (DOE) [DE-SC0008317, DESC0016188]; National Science FoundationNational Science Foundation (NSF) [DEB-1153401]; NASA's CARBON program; NASA's TE program; National Aeronautics and Space AdministrationNational Aeronautics & Space Administration (NASA) We would like to thank the reviewers for their insightful comments and helpful suggestions that improved the clarity and presentation of the manuscript. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi: 10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. D. M. L. was supported in part by the RUBISCO Scientific Focus Area (SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science. D. K. and P. G. were supported by Columbia University Presidential Fellowship. G. B., D. L. L., W. R. W., and R. Q. T. were supported by the U.S. Department of Agriculture NIFA Award 2015-67003-23485. W. R. W. and G. K. A. were supported by the NASA Interdisciplinary Science Program Award NNX17AK19G. J. B. F. and M. S. carried out the research in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. All rights reserved. J. B. F. and M. S. were supported in part by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science program under Awards DE-SC0008317 and DESC0016188; the National Science Foundation Ecosystem Science program (DEB-1153401); and NASA's CARBON and TE programs. All model data are archived and publicly available at the UCAR/NCAR Climate Data Gateway (https://doi.org/10.5065/d6154fwh).

How can we understand climate change from a sociological perspective? In asking this question, we assume that Sociology has something to contribute to such an enterprise. The argument that we put forward is twofold: We argue that Sociology provides a much needed alternative to two dominant approaches that have influenced public discourse, behaviorist theories, mainly employed in Economics, and a belief in the centrality of science in policy making (“evidence first”), mainly entertained by physical scientists. We critically discuss both approaches, showing the limits of behaviorism and the linear model of policy making and provide an alternative framework, which emphasizes the role of social action, of organizations, and of structural differences between different social worlds. We then apply our framework to assess the role of evidence at the climate science‐policy interface and the Intergovernmental Panel on Climate Change (IPCC) as a boundary organization in particular. We argue that science and politics follow different logics and that the IPCC is simultaneously both more political and less policy relevant than its self‐description as “policy‐relevant, never policy prescriptive” tries to make us believe. We conclude that a depoliticization of politics by IPCC expert advice and a politicization of climate science occur simultaneously and mutually reinforce one another. The result is a lack of progress in climate policy as science has taken center stage but is unable to offer political solutions.

Recurrent and large forest fires negatively impact ecosystem, air quality, and human health. Moderate Resolution Imaging Spectroradiometer fire product is used to identify forest fires over central India domain, an extremely fire prone region. The study finds that from 2001 to 2020, ∼70% of yearly forest fires over the region occurred during March (1,857.5 counts/month) and April (922.8 counts/month). Some years such as 2009, 2012, and 2017 show anomalously high forest fires. The role of persistent warmer temperatures and multiple climate extremes in increasing forest fire activity over central India is comprehensively investigated. Warmer period from 2006 to 2020 showed doubling and tripling of forest fire activity during forest fire (February–June; FMAMJ) and non‐fire (July–January; JASONDJ) seasons, respectively. From 2015 JASONDJ to 2018 FMAMJ, central India experienced a severe heatwave, a rare drought and an extremely strong El Niño, the combined effect of which is linked to increased forest fires. Further, the study assesses quinquennial spatiotemporal changes in forest fire characteristics such as fire count density and average fire intensity. Deciduous forests of Jagdalpur‐Gadchiroli Range and Indravati National Park in Chhattisgarh state are particularly fire prone (>61 fire counts/grid) during FMAMJ and many forest fires are of high intensity (>45 MW). Statistical associations link high near surface air temperature and low precipitation during FMAMJ to significantly high soil temperature, low soil moisture content, low evapotranspiration and low normalized difference vegetation index. This creates a significantly drier environment, conducive for high forest fire activity in the region., Key Points Compared to 2001–2005, central India domain forest fire activity during 2006–2020 doubled in forest fire season and tripled in non‐fire seasonRole of persistent warmer temperatures and multiple climate extremes in increasing forest fire activity over central India is highlightedDeciduous forests of Jagdalpur‐Gadchiroli Range and Indravati National Park are extremely fire prone and fires are of high intensity

The Energy Exascale Earth System Model (E3SM) Project is an ongoing, state-of-the-science Earth system modeling, simulation, and prediction project developed by the U.S. Department of Energy (DOE). With an emphasis on supporting DOE's energy mission, understanding and quantifying how well the model simulates water cycle processes is of particular importance. Here, we evaluate E3SM version v1.0 for its ability to represent atmospheric rivers (ARs), which play significant roles in water vapor transport and precipitation. The characteristics and precipitation associated with global ARs in E3SM at standard resolution (1° × 1°) are compared to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2). Global pattern of AR frequencies in E3SM show high degrees of correlation (>= 0.97) with MERRA2 and low mean absolute errors (< 1 %) annually, seasonally, and across different ensemble members. However, some large-scale condition biases exist leading to AR biases - most significant of which are: the double-ITCZ, a stronger and/or equatorward shifted subtropical jet during boreal and austral winter, and enhanced northern hemisphere westerlies during summer. By comparing atmosphere-only and fully coupled simulations, we attribute the sources of the biases to the atmospheric component or to a coupling response. Using relationships revealed in Dong et al. (2021), we provide evidence showing the stronger north Pacific jet in winter and enhanced northern hemisphere westerlies during summer associated with E3SM's double-ITCZ and related weaker AMOC, respectively, are the sources of much of the AR biases found in the coupled simulations.

We assess the detectability of COVID‐like emissions reductions in global atmospheric CO2 concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO2 sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon‐concentration feedbacks obscure the detectability of short‐term emission reductions in atmospheric CO2. COVID‐driven changes in the simulated, column‐averaged dry air mole fractions of CO2 are eclipsed by large internal variability. Carbon‐concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions‐driven signal in the atmosphere carbon reservoir and further confound signal detection., Key Points Climate model simulations suggest a lagged response in the global growth rate of atmospheric CO2 due to COVID‐19 emissions reductionsDetection of this reduction in observations is hampered by internal variability combined with reduced ocean and land uptake of CO2 Our results foreshadow the challenges of detecting the effects of CO2 mitigation efforts to meet the Paris climate agreement

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement., Key Points Total exceedance PM2.5 exposure risk, including youths and older adults, was mappedThe hourly exceedance PM2.5 exposure risk (HEPE)psd was more aggregated than the HEPEsm Contribution of HEPEpsd varied geographically with percentage more than 50%

The concept of resilience is multi‐faceted. This commentary builds upon the analytical distinctions of resilience provided by Urquiza et al. (2021, https://doi.org/10.1029/2020EF001508). In response to this article, we emphasize several distinctions between resilience and other systems concepts. These include distinctions between resilience, risk, and vulnerability, the tradeoff between resilience and efficiency, resilience contrasted with robustness, the relationship between resilience and sustainability, and finally methods for building resilience‐by‐design or resilience‐by‐intervention. Improving understanding of these concepts will enable planners to select resilience strategies that best support their system goals. We use examples from the 2020–2021 coronavirus pandemic to illustrate the concepts and the juxtapositions between them., Key Points Resilience is one of many properties of systems affected by threat and resilience differ from robustness, sustainability, and riskBuilding resilience will require tradeoff on efficiency in complex systemsResilience can be build by design and by intervention

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO2 mixing ratio to equilibrate climate, has been implemented in a cloud‐resolving model. In this article, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global‐mean SST. We find that InvCM produces the same climate as normal slab‐ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO2 for SSTs ranging from 290 to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition., Key Points We converge a GCM by varying the CO2 while keeping the global‐mean surface temperature fixed (the inverse climate modeling method)Inverse climate modeling converges about 10 times faster than normal slab‐ocean simulationsThe SST gradient response timescale is the bottleneck on the convergence rate of inverse climate modeling

Following extreme drought during the 2019–2020 bushfire summer, the eastern part of Australia suffered from a week‐long intense rainfall and extensive flooding in March 2021. Understanding how much water storage changes in response to these climate extremes is critical for developing timely water management strategies. To quantify prompt water storage changes associated with the 2021 March flooding, we processed the low‐latency (1–3 days), high‐precision intersatellite laser ranging measurements from GRACE Follow‐On spacecraft and determined instantaneous gravity changes along spacecraft orbital passes. Such new data processing detected an abrupt surge of water storage approaching 60–70 trillion liters (km3 of water) over a week in the region, which concurrently caused land subsidence of ∼5 mm measured by a network of ground GPS stations. This was the highest speed of ground water recharge ever recorded in the region over the last two decades. Compared to the condition in February 2020, the amount of recharged water was similar but the recharge speed was much faster in March 2021. While these two events together replenished the region up to ∼80% of the maximum storage over the last two decades, the wet antecedent condition of soils in 2021 was distinctly different from the dry conditions in 2020 and led to generating extensive runoff and flooding in 2021., Key Points New GRACE Follow‐On gravity data processing quantified prompt changes in water storage by the heavy rainfall and flooding in March 2021The eastern Australia experienced the highest speed of ground water recharge and wet antecedent soils were responsible for extensive flooding in 2021The study demonstrated the feasibility of rapid processing (1–3 days) for immediate assessment of mass changes from extreme events

Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave‐based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point‐based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE—and of corresponding changes to other snowpack properties—to the microwave‐based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships., Key Points Simulated snow properties from SnowModel are used in microwave‐based snow water equivalent (SWE) retrievals by MEMLS3&aBiases in physically modeled SWE can induce larger biases in microwave‐based SWE retrievalsThe challenges can be mitigated when microwave algorithms account for the physical relationship of snow properties

We present a data‐driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et al., 2013, https://doi.org/10.1785/0220130039). Desired features of the data can be selected for clustering, including some subset of displacement or velocity components, uncertainty estimates, station location, and other relevant information. Based on those selections, the clustering procedure autonomously groups the GNSS stations according to a selected clustering method. We have implemented this approach as a Python application, allowing us to draw upon the full range of open source clustering methods available in Python's scikit‐learn package (Pedregosa et al., 2011, https://doi.org/10.5555/1953048.2078195). The application returns the stations labeled by group as a table and color coded KML file and is designed to work with the GNSS information available from GeoGateway (Donnellan et al., 2021, https://doi.org/10.1007/s12145-020-00561-7; Heflin et al., 2020, https://doi.org/10.1029/2019ea000644) but is easily extensible. We demonstrate the methodology on California and western Nevada. The results show partitions that follow faults or geologic boundaries, including for recent large earthquakes and post‐seismic motion. The San Andreas fault system is most prominent, reflecting Pacific‐North American plate boundary motion. Deformation reflected as class boundaries is distributed north and south of the central California creeping section. For most models a cluster boundary connects the southernmost San Andreas fault with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass., Key Points Unsupervised clustering methods provide a data‐driven way of analyzing and partitioning Global Navigation Satellite System observations of crustal deformationDeformation is distributed across the San Andreas fault system but is localized at the creeping section in central CaliforniaThe Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system

25 pags., 14 figs., 2 tabs., Tropospheric ozone ((Formula presented.)) is an important greenhouse gas and a surface pollutant. The future evolution of (Formula presented.) abundances and chemical processing are uncertain due to a changing climate, socioeconomic developments, and missing chemistry in global models. Here, we use an Earth System Model with natural halogen chemistry to investigate the changes in the (Formula presented.) budget over the 21st century following Representative Concentration Pathway (RCP)6.0 and RCP8.5 climate scenarios. Our results indicate that the global tropospheric (Formula presented.) net chemical change (NCC, chemical gross production minus destruction) will decrease (Formula presented.), notwithstanding increasing or decreasing trends in ozone production and loss. However, a wide range of surface NCC variations (from −60 (Formula presented.) to 150 (Formula presented.)) are projected over polluted regions with stringent abatements in (Formula presented.) precursor emissions. Water vapor and iodine are found to be key drivers of future tropospheric (Formula presented.) destruction, while the largest changes in (Formula presented.) production are determined by the future evolution of peroxy radicals. We show that natural halogens, currently not considered in climate models, significantly impact on the present-day and future global (Formula presented.) burden reducing (Formula presented.) 30–35 Tg (11–15 (Formula presented.)) of tropospheric ozone throughout the 21st century regardless of the RCP scenario considered. This highlights the importance of including natural halogen chemistry in climate model projections of future tropospheric ozone., EC, H2020, H2020 Priority Excellent Science, H2020 European Research Council (ERC). Grant Number: ERC-2016-COG726349; NSF, Office of Science of the US Department of Energy, PICT-2016-0714 (ANPCyT) i-COOP-B20331 (CSIC + CONICET)

Most tree species predominantly associate with a single type of mycorrhizal fungi, which can differentially affect plant nutrient acquisition and biogeochemical cycling. Uncertainties in mycorrhizal distributions are non-trivial, and current estimates disagree in up to 50% over 40% of the land area, including tropical forests. Remote sensing capabilities for mycorrhizal detection show promise for refining these estimates further. Here, we address for the first time the impact of mycorrhizal distributions on global carbon and nutrient cycling. Using the state-of-the-art carbon-nitrogen economics within the Community Land Model version 5, we found Net Primary Productivity (NPP) increased throughout the 21st century by 20%; however, as soil nitrogen has progressively become limiting, the costs to NPP for nitrogen acquisition-that is, to mycorrhizae-have increased at a faster rate by 60%. This suggests that nutrient acquisition will increasingly demand a higher portion of assimilated carbon to support the same productivity. U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science program United States Department of Energy (DOE) [DE-SC0008317, DE-SC0016188]; NASA IDS program

As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best‐performing models that are useful for impact assessments., Key Points Simulations reproduce observed drought duration and severity in terms of the Standardized Precipitation Index (SPI) but simulated low precipitation is considerably biasedVariance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zonesGood model performance in terms of SPI does not imply that low precipitation values are well simulated by the climate models

The lightning data products generated by the low‐frequency (LF) radio lightning locating system (LLS) deployed during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation (RELAMPAGO) field campaign in Argentina provide a valuable data set to research the lightning evolution and characteristics of convective storms that produce high‐impact weather. LF LLS data sets offer a practical range for mesoscale studies, allowing for the observation of lightning characteristics of storms such as mesoscale convective systems or large convective lines that travel longer distances which are not necessarily staying in range of regional VHF‐based lightning detection systems throughout their lifetime. LF LLSs also provide different information than optical space‐borne lightning detectors. Lightning measurements exclusive to LF systems include discharge peak current, lightning polarity, and lightning type classification based on the lightning‐emitted radio waveform. Furthermore, these measurements can provide additional information on flash rates (e.g., positive cloud‐to‐ground flash rate) or narrow bipolar events which may often be associated with dynamically intense convection. In this article, the geolocation and data processing of the LF data set collected during RELAMPAGO is fully described and its performance characterized, with location accuracy better than 10 km. The detection efficiency (DE) of the data set is compared to that of the Geostationary Lightning Mapper, and spatiotemporal DE losses in the LF data set are discussed. Storm case studies on November 10, 2018, highlight the strengths of the data set, which include robust flash clustering and insightful flash rate and peak current measures, while illustrating how its limitations, including DE losses, can be managed., Key Points Geolocation and processing of the low‐frequency (LF) data set collected during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation campaign is fully described and characterizedUnique LF observations include discharge peak current, polarity, and possibly type classification based on the emitted radio waveformLF spatiotemporal detection efficiency and storm case studies are discussed and compared to Geostationary Lightning Mapper data

Large-scale co-variations of freshwater fluxes and storages on land can critically regulate green (vegetation) and blue (hydrosphere) water balances, land-atmosphere interactions, and hydroclimatic...

Astronomical cycles are strongly expressed in marine geological records, providing important insights into Earth system dynamics and an invaluable means of constructing age models. However, how various astronomical periods are filtered by the Earth system and the mechanisms by which carbon reservoirs and climate components respond, particularly in absence of dynamic ice sheets, is unclear. Using an Earth system model that includes feedbacks between climate, ocean circulation, and inorganic (carbonate) carbon cycling relevant to geological timescales, we systematically explore the impact of astronomically modulated insolation forcing and its expression in model variables most comparable to key paleoceanographic proxies (temperature, the δ13C of inorganic carbon, and sedimentary carbonate content). Temperature predominately responds to short and long eccentricity and is little influenced by the modeled carbon cycle feedbacks. In contrast, the cycling of nutrients and carbon in the ocean generates significant precession power in atmospheric CO2, benthic ocean δ13C, and sedimentary wt% CaCO3, while inclusion of marine sedimentary and weathering processes shifts power to the long eccentricity period. Our simulations produce reduced pCO2 and dissolved inorganic carbon (DIC) δ13C at long eccentricity maxima and, contrary to early Cenozoic marine records, CaCO3 preservation in the model is enhanced during eccentricity-modulated warmth. Additionally, the magnitude of δ13C variability simulated in our model underestimates marine proxy records. These model-data discrepancies hint at the possibility that the Paleogene silicate weathering feedback was weaker than modeled here and that additional organic carbon cycle feedbacks are necessary to explain the full response of the Earth system to astronomical forcing.

Astronomical cycles are strongly expressed in marine geological records, providing important insights into Earth system dynamics and an invaluable means of constructing age models. However, how various astronomical periods are filtered by the Earth system and the mechanisms by which carbon reservoirs and climate components respond, particularly in absence of dynamic ice sheets, is unclear. Using an Earth system model that includes feedbacks between climate, ocean circulation, and inorganic (carbonate) carbon cycling relevant to geological timescales, we systematically explore the impact of astronomically modulated insolation forcing and its expression in model variables most comparable to key paleoceanographic proxies (temperature, the δ

Spatial estimates of crop evapotranspiration with high accuracy from the field to watershed scale have become increasingly important for water management, particularly over irrigated agriculture in semiarid regions. Here, we provide a comprehensive assessment on patterns of annual agricultural water use over California's Central Valley, using 30‐m daily evapotranspiration estimates based on Landsat satellite data. A semiempirical Priestley‐Taylor approach was locally optimized and cross‐validated with available field measurements for major crops including alfalfa, almond, citrus, corn, pasture, and rice. The evapotranspiration estimates explained >70% variance in daily measurements from independent sites with an RMSE of 0.88 mm day−1. When aggregated over the Valley, we estimated an average evapotranspiration of 820 ± 290 mm yr−1 in 2014. Agricultural water use varied significantly across and within crop types, with a coefficient of variation ranging from 8% for Rice (1,110 ± 85 mm yr−1) to 59% for Pistachio (592 ± 352 mm yr−1). Total water uses in 2016 increased by 9.6%, as compared to 2014, mostly because of land‐use conversion from fallow/idle land to cropland. Analysis across 134 Groundwater Sustainability Agencies (GSAs) further showed a large variation of agricultural evapotranspiration among and within GSAs, especially for tree crops, e.g., almond evapotranspiration ranging from 339 ± 80 mm yr−1 in Tracy to 1,240 ± 136 mm yr−1 in Tri‐County Water Authority. Continuous monitoring and assessment of the dynamics and spatial heterogeneity of agricultural evapotranspiration provide data‐driven guidance for more effective land use and water planning across scales., Key Points A 30‐m daily evapotranspiration product estimated using regionally optimized approach and Landsat Analysis Ready Data Collection 1Assessment of agricultural consumptive water use over Central Valley provides critical guidance for sustainable groundwater managementLarge variation of water use was found, mostly due to crop diversity, age structure, and physiological factors

In 2020, people's health suffered a great crisis under the dual effects of the COVID‐19 pandemic and the extensive, severe wildfires in the western and central United States. Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemic. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and mobility to parks. In this study, we employed the geographically and temporally weighted regression (GTWR) Models to investigate how COVID‐19 and wildfires jointly shaped human mobility to parks, regarding the number of visits per capita, dwell time, and travel distance to parks, during June ‐ September 2020. We detected strong correlations between visitations and COVID‐19 incidence in southern Montana, western Wyoming, Colorado, and Utah before August. However, the pattern was weakened over time, indicating the decreasing trend of the degree of concern regarding the pandemic. Moreover, more park visits and lower dwell time were found in parks further away from wildfires and less air pollution in Washington, Oregon, California, Colorado, and New Mexico, during the wildfire season, suggesting the potential avoidance of wildfires when visiting parks. This study provides important insights on people's responses in recreation and social behaviors when facing multiple severe crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards., Key Points We investigated human mobility patterns to parks under COVID‐19 pandemic and wildfire season in western and central United StatesWe found a general trend of avoidance to the parks with fewer visits and dwell time in the places with high COVID‐19 casesWith special demand of physical activities in pandemic, people travel further and spend longer time at the parks away from the wildfires

Ice shelf fracture is responsible for roughly half of Antarctic ice mass loss in the form of calving and can weaken buttressing of upstream ice flow. Large uncertainties associated with the ice sheet response to climate variations are due to a poor understanding of these fracture processes and how to model them. Here, we address these problems by implementing an anisotropic, nonlocal integral formulation of creep damage within a large‐scale shallow‐shelf ice flow model. This model can be used to study the full evolution of fracture from initiation of crevassing to rifting that eventually causes tabular calving. While previous ice shelf fracture models have largely relied on simple expressions to estimate crevasse depths, our model parameterizes fracture as a progressive damage evolution process in three‐dimensions (3‐D). We also implement an efficient numerical framework based on the material point method, which avoids advection errors. Using an idealized marine ice sheet, we test the creep damage model and a crevasse‐depth based damage model, including a modified version of the latter that accounts for damage evolution due to necking and mass balance. We demonstrate that the creep damage model is best suited for capturing weakening and rifting over shorter (monthly/yearly) timescales, and that anisotropic damage reproduces typically observed fracture patterns better than isotropic damage. Because necking and mass balance can significantly influence damage on longer (decadal) timescales, we discuss the potential for a combined approach between models to best represent mechanical weakening and tabular calving within long‐term simulations., Key Points Our shallow‐shelf creep damage model can represent the full evolution of ice shelf fracture from crevasse initiation to tabular calvingStrongly anisotropic creep damage produces sharp, arcuate rift patterns more consistent with observations than isotropic creep damageThe zero‐stress damage model poorly captures rifting, but a modified form accounts for damage evolution due to mass balance and necking

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers., Key Points Regression logic is reason to doubt the accurate climate sensitivity of predictions whose variability is realistic or higherA suite of models poorly reproduces eddy covariance estimates of Amazon rainforest gross primary productivityHighly seasonal models predict stronger primary productivity responsiveness to meteorology than is likely to be true

We develop a generalized interpolation material point method (GIMPM) for the shallow shelf approximation (SSA) of ice flow. The GIMPM, which can be viewed as a particle version of the finite element method, is used here to solve the shallow shelf approximations of the momentum balance and ice thickness evolution equations. We introduce novel numerical schemes for particle splitting and integration at domain boundaries to accurately simulate the spreading of an ice shelf. The advantages of the proposed GIMPM‐SSA framework include efficient advection of history or internal state variables without diffusion errors, automated tracking of the ice front and grounding line at sub‐element scales, and a weak formulation based on well‐established conventions of the finite element method with minimal additional computational cost. We demonstrate the numerical accuracy and stability of the GIMPM using 1‐D and 2‐D benchmark examples. We also compare the accuracy of the GIMPM with the standard material point method (sMPM) and a reweighted form of the sMPM. We find that the grid‐crossing error is very severe for SSA simulations with the sMPM, whereas the GIMPM successfully mitigates this error. While the grid‐crossing error can be reasonably reduced in the sMPM by implementing a simple material point reweighting scheme, this approach it not as accurate as the GIMPM. Thus, we illustrate that the GIMPM‐SSA framework is viable for the simulation of ice sheet‐shelf evolution and enables boundary tracking and error‐free advection of history or state variables, such as ice thickness or damage., Key Points Our material point method for ice shelf flow enables error‐free advection of history variables, such as damage, and ice front trackingThe method can be readily implemented into existing finite element software (here, Elmer/Ice), and is suitable for large‐scale applicationThe method is verified against analytical solutions for steady‐state flow and front evolution, and tested on an idealized marine ice sheet

Elevated concentrations of atmospheric bromine are known to cause ozone depletion in the Arctic, which is most frequently observed during springtime. We implement a detailed description of bromine and chlorine chemistry within the WRF‐Chem 4.1.1 model, and two different descriptions of Arctic bromine activation: (1) heterogeneous chemistry on surface snow on sea ice, triggered by ozone deposition to snow (Toyota et al., 2011 https://doi.org/10.5194/acp-11-3949-2011), and (2) heterogeneous reactions on sea salt aerosols emitted through the sublimation of lofted blowing snow (Yang et al., 2008, https://doi.org/10.1029/2008gl034536). In both mechanisms, bromine activation is sustained by heterogeneous reactions on aerosols and surface snow. Simulations for spring 2012 covering the entire Arctic reproduce frequent and widespread ozone depletion events, and comparisons with observations of ozone show that these developments significantly improve model predictions during the Arctic spring. Simulations show that ozone depletion events can be initiated by both surface snow on sea ice, or by aerosols that originate from blowing snow. On a regional scale, in spring 2012, snow on sea ice dominates halogen activation and ozone depletion at the surface. During this period, blowing snow is a major source of Arctic sea salt aerosols but only triggers a few depletion events., Key Points Halogen activation and its role in Arctic surface ozone depletion events (ODEs) is modeled using WRF‐ChemTwo halogen activation mechanisms are implemented (1) surface snow and (2) blowing snowA spring 2012 case study indicates that both mechanisms can trigger near‐surface ODEs, but that surface snow dominates

Earth system modeling of climate geoengineering proposals suggests that the physical outcomes of such interventions will depend on the particulars of the implementation. Here, we present a first at...

Funder: U.K. China Research and Innovation Partnership Fund, Funder: National Centre for Atmospheric Science (NCAS); Id: http://dx.doi.org/10.13039/501100000662, Reconstructions of volcanic aerosol radiative forcing are required to understand past climate variability. Currently, reconstructions of pre‐20th century volcanic forcing are derived from sulfate concentrations measured in polar ice cores, mainly using a relationship between the average ice‐sheet sulfate deposition and stratospheric sulfate aerosol burden based on a single explosive eruption—the 1991 eruption of Mt. Pinatubo. Here we estimate volcanic radiative forcings and associated uncertainty ranges from ice‐core sulfate records of eight of the largest bipolar deposition signals in the last 2,500 years using statistical emulation of a perturbed parameter ensemble of aerosol‐climate model simulations of explosive eruptions. Extensive sampling of different combinations of eruption source parameters using the emulators reveals that a very wide range of eruptions in different seasons with different sulfur dioxide emissions, eruption latitudes, and emission altitudes produce ice‐sheet sulfate deposition consistent with ice‐core records. Consequently, we find a large range in the volcanic forcing that can be directly attributed to the unknown eruption source parameters. We estimate that the uncertainty in volcanic forcing caused by many plausible eruption realizations leads to uncertainties in the global mean surface cooling of around 1°C for the largest unidentified historical eruptions. Our emulators are available online (https://cemac.github.io/volcanic-forcing-deposition) where eruption realizations for given ice‐sheet sulfate depositions can be explored.

Contributions are invited to a new journal special collection on the use of new machine learning methodologies and applications of machine learning to Earth system modeling.

Global air pollution and climate change are major threats to planetary health. These threats are strongly linked through the short‐lived climate forcers (SLCFs); ozone (O3), aerosols, and methane (CH4). Understanding the impacts of ambitious SLCF mitigation in different source emission sectors on planetary health indicators can help prioritize international air pollution control strategies. A global Earth system model is applied to quantify the impacts of idealized 50% sustained reductions in year 2005 emissions in the eight largest global anthropogenic source sectors on the SLCFs and three indicators of planetary health: global mean surface air temperature change (∆GSAT), avoided PM2.5‐related premature mortalities and gross primary productivity (GPP). The model represents fully coupled atmospheric chemistry, aerosols, land ecosystems and climate, and includes dynamic CH4. Avoided global warming is modest, with largest impacts from 50% cuts in domestic (−0.085 K), agriculture (−0.034 K), and waste/landfill (−0.033 K). The 50% cuts in energy, domestic, and agriculture sector emissions offer the largest opportunities to mitigate global PM2.5‐related health risk at around 5%–7% each. Such small global impacts underline the challenges ahead in achieving the World Health Organization aspirational goal of a 2/3 reduction in the number of deaths from air pollution by 2030. Uncertainty due to natural climate variability in PM2.5 is an important underplayed dimension in global health risk assessment that can vastly exceed uncertainty due to the concentration‐response functions at the large regional scale. Globally, cuts to agriculture and domestic sector emissions are the most attractive targets to achieve climate and health co‐benefits through SLCF mitigation., Key Points Quantify impacts of deep short‐lived climate forcer (SLCF) emission mitigation by source sector on global temperature and human and ecosystem healthUncertainties due to year‐to‐year meteorological variability in PM2.5 is an important dimension in global human health risk assessmentAchieving planetary health benefits from SLCF mitigation requires ambitious mitigation pathways that tackle multiple source sectors