GRL

Atlantic hurricane season length is important for emergency management preparation, motivating the need to understand its variability and change. We investigated the influence of ocean variability on Atlantic hurricane season length in observations and a future climate simulated by the Energy Exascale Earth System Model (E3SM). We found that multiple factors influence hurricane season length, through their influence on season start and end. Warm western subtropical Atlantic sea-surface temperature anomalies (SSTAs) during boreal spring (before the official hurricane season start) drive early starts to the hurricane season, and vice versa for cool SSTAs. Meanwhile, La Niña in autumn (before the official hurricane season end) drives late ends to the hurricane season, and vice versa for El Niño. E3SM projects a 27-day increase in future Atlantic hurricane season length given La Niña and warm northern tropical Atlantic SSTAs. This research documents sources of predictability for Atlantic hurricane season length.

The reductions in aerosols often exacerbate climate warming. It remains unclear how to effectively alleviate PM2.5 pollution while minimizing the penalty on climate warming. Here we identify the clean air measures in China that are associated with low aerosol climate penalty efficiency (ACPE), which is defined as aerosol radiative forcing per unit PM2.5 concentration reduction. The measures in transportation, residential combustion, and open burning sectors generally caused lower ACPE [0.07, 0.24, and 0.10 (W m−2)/(μg m−3)] than those from other sectors [0.34–0.46 (W m−2)/(μg m−3)]. This is ascribed to relatively small decreases in cloud concentration nuclei per unit PM2.5 reduction in these sectors, which is further attributed to either relatively low aerosol hygroscopicity or relatively small decrease in aerosol number. Most measures in the former three sectors have low ACPE of <0.15 [(W m−2)/(μg m−3)] and thus may be prioritized for synergistically controlling PM2.5 pollution and climate warming.

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic-scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long-wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

Quantifying atmospheric CO2 over long periods from space is crucial in understanding the carbon cycle's response to climate change. However, a single satellite offers limited spatiotemporal coverage, making comprehensive monitoring challenging. Moreover, biases among various satellite retrievals hinder their direct integration. This study proposed a machine learning framework for fusing the column-averaged dry-air mole fraction of CO2 (XCO2) retrievals from Greenhouse Gases Observing Satellite (GOSAT) and OCO-2 satellites. The best model (R 2 = 0.85) presented improved consistency of GOSAT retrievals by reducing 71.5% of the average monthly bias while using OCO-2 retrievals as a benchmark, indicating the fusion data set's potential to enhance observation coverage. Incorporating the adjusted GOSAT XCO2 retrievals into the OCO-2 data set added an average of 84.7 thousand observations annually, enhancing the yearly temporal coverage by 53.6% (from 14 to 21.5 days per grid). This method can be adapted to other satellites, maximizing satellite resources for a more robust carbon flux inversion.

Asian dust delivers highly reactive iron (FeHR) to the Pacific Ocean, affecting marine biogeochemical cycles and Earth's climate. Tracing the source of dust deposited in the Pacific is vital for assessing global nutrient cycles but poses challenges. This work applies the (234U/238U) activity ratio to determine the pre-deposition time and provenance of dust in North Pacific Ocean sediments (Ocean Drilling Program site 1209B). Results indicate a consistent dust pre-deposition time (134 ± 10 ka) over the past 300,000 years, except during Marine Isotope Stage 7 when volcanic ash input shortened it to 31 ± 19 ka. Comparing the dust pre-deposition times to those of the potential source deserts, we identify the dust transported to the North Pacific Ocean was primarily from the Taklamakan Desert, which contains higher FeHR content than other deserts. This finding enhances our understanding of soluble Fe supplied to the oceans, especially in dust circulation models.

Biweekly sea surface temperature (SST) variability significantly contributes to over 50% of the intraseasonal variability in the eastern equatorial Pacific (EEP) and Atlantic (EEA). Our study investigates this biweekly variability, employing a blend of in–situ and reanalysis data sets. The research identifies biweekly signals in SST, meridional wind, and ocean currents, notably in September–November in EEP and June–August in EEA. Biweekly southerly (northerly) winds drive instantaneous northward (southward) ocean currents in EEP, but with a 1–2-day phase delay in EEA. Consequently, these currents lead to SST anomalies with a 3–4-day lag in both EEP and EEA due to the presence of the cold tongue. The study reveals the origin of biweekly wind fluctuations in the western Pacific for EEP and the subpolar Pacific for EEA, connected by atmospheric Rossby waves validated through a linearized non-divergent barotropic model. This research affirms the influence of subtropical and subpolar atmospheric forcing on equatorial SST.

Tropical cyclones (TCs) over the Bay of Bengal (BOB) can interact with the South Branch Trough (SBT) as they move northward and potentially amplify Rossby waves. This study evaluates the features of Rossby waves and their downstream impact on rainfall in South China. Results indicate that TC-SBT interactions primarily occur in May and October-November (Oct-Nov), with probabilities of 59% and 53% respectively. Notably, the Rossby wave train associated with BOB TCs is more pronounced during Oct-Nov due to the stronger subtropical westerly jet, in contrast to May. The downstream atmospheric response results in positive (negative) rainfall anomalies over South China in May (Oct-Nov), particularly on the day following the maximum interaction day. Previous researches concerning TC-extratropical flow interaction mainly focus on other basins where TCs move to higher latitudes, this study provides fresh insights into Rossby waves related to TC-SBT interactions over the southern Tibetan Plateau.

High-resolution records from past interglacial climates help constrain future responses to global warming, yet are rare. Here, we produce seasonally resolved climate records from subarctic-Canada using micron-scale measurements of oxygen isotopes (δ18O) in speleothems with apparent annual growth bands from three interglacial periods—Marine Isotope Stages (MIS) 11, 9, and 5e. We find 3‰ lower δ18O values during MIS 11 than MIS 5e, despite MIS 11 likely being warmer. We explore controls on high-latitude speleothem δ18O and suggest low MIS 11 δ18O values reflect greater contribution of cold-season precipitation to dripwater from longer annual ground thaw durations. Other potential influences include changes in precipitation source and/or increased fraction of cold-season precipitation from diminished sea ice in MIS 11. Our study highlights the potential for high-latitude speleothems to yield detailed isotopic records of Northern Hemisphere interglacial climates beyond the reach of Greenland ice cores and offers a framework for interpreting them.

Grazing dynamics are one of the most poorly constrained components of the marine carbon cycle. We use inverse modeling to infer the distribution of community-integrated zooplankton grazing dynamics based on the ability of different grazing formulations to recreate the satellite-observed seasonal cycle in phytoplankton biomass after controlling for physical and bottom-up controls. We find large spatial variability in the optimal community-integrated half saturation concentration for grazing (K 1/2), with lower (higher) values required in more oligotrophic (eutrophic) biomes. This leads to a strong sigmoidal relationship between observed mean-annual phytoplankton biomass and the optimally inferred grazing parameterization. This relationship can be used to help constrain, validate and/or parameterize next-generation biogeochemical models.

The moist processes of the Madden-Julian Oscillation (MJO) in the Coupled Model Intercomparison Project Phase 6 models are assessed using moisture mode theory-based diagnostics over the Indian Ocean (10°S–10°N, 75°E–100°E). Results show that no model can capture all the moisture mode properties relative to the reanalysis. Most models satisfy weak temperature gradient balance but have unrealistically fast MJO propagation and a lower moisture-precipitation correlation. Models that satisfy the most moisture mode criteria reliably simulate a stronger MJO. The background moist static energy (MSE) and low-level zonal winds are more realistic in the models that satisfy the most criteria. The MSE budget associated with the MJO is also well-represented in the good models. Capturing the MJO's moisture mode properties over the Indian Ocean is associated with a more realistic representation of the MJO and thus can be employed to diagnose MJO performance.

The observed partitioning of poleward heat transport between atmospheric and oceanic heat transports (AHT and OHT) is compared to that in coupled climate models. Model ensemble mean poleward OHT is biased low in both hemispheres, with the largest biases in the Southern Hemisphere extratropics. Poleward AHT is biased high in the Northern Hemisphere, especially in the vicinity of the peak AHT near 40°N. The significant model biases are persistent across three model generations (CMIP3, CMIP5, CMIP6) and are insensitive to the satellite radiation and atmospheric reanalyzes products used to derive observational estimates of AHT and OHT. Model biases in heat transport partitioning are consistent with biases in the spatial structure of energy input to the ocean and atmosphere. Specifically, larger than observed model evaporation in the tropics adds excess energy to the atmosphere that drives enhanced poleward AHT at the expense of weaker OHT.

The electron rolling-pin distribution, showing electron pitch angles primarily at 0°, 90°, and 180°, has been widely studied in the Earth's magnetosphere, but has never been reported in other planetary environments. Here, by utilizing the Jupiter Near-polar Orbiter (Juno) measurements, we report for the first time the electron rolling-pin distribution in Jupiter's magnetosphere. We reveal the energy range of such distribution and find it appears only above 19.5 keV, falling well into the suprathermal energy range. Moreover, we quantitively reproduce the formation processes of such distribution by using an analytical model. Gratifyingly, the distribution derived from the analytical model agrees well with the Juno observations, indicating such distribution is formed by the combination of global-scale Fermi acceleration and local-scale betatron acceleration. These results, demonstrating that the electron rolling-pin distribution exists beyond the Earth, can improve our knowledge of electron dynamics in planetary magnetosphere.

Changes are projected for the boreal biome with complex and variable effects on forest vegetation including drought-induced tree mortality and forest loss. With soil and atmospheric conditions governing drought intensity, specific drivers of trees water stress can be difficult to disentangle across temporal scales. We used wavelet analysis and causality detection to identify potential environmental controls (evapotranspiration, soil moisture, rainfall, vapor pressure deficit, air temperature and photosynthetically active radiation) on daily tree water deficit and on longer periods of tree dehydration in black spruce and tamarack. Daily tree water deficit was controlled by photosynthetically active radiation, vapor pressure deficit, and air temperature, causing greater stand evapotranspiration. Prolonged periods of tree water deficit (multi-day) were regulated by photosynthetically active radiation and soil moisture. We provide empirical evidence that continued warming and drying will cause short-term increases in black spruce and tamarack transpiration, but greater drought stress with reduced soil water availability.

Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long-term field observations over the Southern Great Plains, this investigation revealed that cloud-land interactions are closely connected to cloud-land coupling regimes. Observational evidence supports a dual-mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA-2 and ERA-5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA-5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA-2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land-atmosphere interactions.

Cryospheric responses to climate warming include glacier retreat, altitude-dependent thermal instability, and abundant meltwater, which increase the frequency of catastrophic glacier hazard chain (CGHC) events. Here we investigated the formation mechanism of a special CGHC event in 2018, in the Sedongpu Glacier, Eastern Himalayas, China. Based on the multi-source remote sensing, seismic signal analysis, and numerical simulation, we conducted long-term retrospective analysis and co-event process reconstruction. The results show that the event could be divided into two phases. First, the hanging glacier with a volume of 8.5 × 106 m3 collapsed onto the downstream trunk glacier. Next, ∼1.17 × 108 m3 eroded materials from the impacted glacier transformed into debris flow and traveled downstream 8 km. During the cascading process, ice-rock avalanche momentum and glacier velocity are key factors in determining CGHC formation and eventual volume. Our study helps better understand the domino effects of the CGHC disaster.

The increase in initial Hf isotopes identified in early Mesoarchean detrital zircon is commonly interpreted as a reflection of the geodynamic transition from stagnant-lid to mobile-lid tectonics. However, given the lack of petrogenetic context, interpreting detrital zircon may lead to spurious conclusions. In this contribution, we use zircon U-Pb-Hf-O isotopic and bulk rock compositions of newly identified 3.05–2.9 Ga granitoids from the SW Yangtze Block to posit petrogenesis within an isotopically juvenile magmatic system. A statistical analysis of these data with a global igneous zircon Lu-Hf isotopic compilation reveals an increase in average initial radiogenic Hf isotopes during the Paleoarchean to Mesoarchean transition. We posit that the Earth's continental crust underwent a global rejuvenation across the Paleo-Mesoarchean transition. This rejuvenation can be explained by an independently observed increase in mantle temperatures resulting from mantle thermal evolution and does not require a change in tectonic style.

No abstract is available for this article.

The North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño-Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric-ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions.

Comprehending the temperature distribution within Earth's lithospheric mantle is of paramount importance for understanding the dynamics of Earth's interior. Traditional mineral-based thermobarometers effectively constrain temperature and pressure for particular compositions, but their application is limited at the global scale. Here, we trained machine-learning (ML) algorithms on 985 published high-temperature and high-pressure experiments for use as thermometers and barometers to overcome the limitations of classic methods. We compared our ML models to classic thermobarometers to assess the accuracies of predicted pressures and temperatures. The comparison shows that the ML models outperform classic methods and better fit various mineral pairs. Global application of the ML models unveils mantle conditions beneath cratons. Furthermore, depths to the lithosphere-asthenosphere boundary (LAB) calibrated based on the ML thermobarometry results are generally deeper by ∼40 km than those derived geophysically, implying the existence of melt-bearing or hydrated mineral zones at the LAB.

The viscoelastic lower crust beneath Kyushu Island, influenced by the volcanic arc, interplays with active crustal faults in this region and helps to shape local tectonics. In this study, we employed a three-dimensional viscoelastic finite element model to gain insights into the lithospheric rheology and crustal faulting kinematics, through modeling the postseismic deformation processes of the 2016 Mw 7.1 Kumamoto earthquake. Our model reveals a viscosity of 2 × 1020 Pa s for the lower crust and 2 × 1019 Pa s for the upper mantle. A reduced lower crust viscosity of 2 × 1019 Pa s in the volcanic arc area is required for better reproducing the Global Positioning System data. The stress-driven afterslip decays rapidly over time and is up to 0.3 m within 5 years after the earthquake. We propose additional normal-component afterslip to better explain the complex postseismic deformation in the near field, which may be due to the interaction between the fault and volcano Aso.