Estimates of climate sensitivity rely in part on the magnitude of global cooling during the Last Glacial Maximum (LGM). While ice cores provide reliable LGM temperatures in high-latitude regions, proxy records of sea-surface temperature (SST) disagree substantially in the low latitudes (1-3), and quantitative low-elevation paleotemperature records on land are scarce. Filling this gap, noble gases in groundwater record land surface temperatures via their temperature-dependent solubility in water (4), a direct physical relationship uncomplicated by biological and chemical processes (5-6). Individual groundwater noble gas studies (e.g. 7-8) have shown 5-7 °C LGM cooling, in line with some proxy data (e.g. tropical snowline reconstructions) but larger than notable low-latitude SST reconstructions considering land-sea cooling ratios. To date, limited spatial coverage and the use of different physical frameworks to determine temperature from noble gas data has prevented a comprehensive estimate of low-latitude LGM cooling from noble gases in groundwater. Here we compile four decades of groundwater noble gas data from six continents, all interpreted using a consistent physical framework (9). We evaluate the accuracy of the “noble gas paleothermometer” by comparing noble gas derived temperatures in late Holocene groundwater with modern observations. From LGM noble gas data, we find that the low-elevation, low-to-mid-latitude land surface cooled by 5.8 ± 0.6 °C during the LGM (9). The ratio of our land cooling estimate to a recent SST reconstruction (1) that found 4.0 °C cooling over the same low latitude band is consistent with the inter-model mean land-sea cooling ratio of 1.45 °C from PMIP4 simulations (10). Together, these recent land- and sea-surface LGM temperature reconstructions indicate greater low-latitude cooling and thus climate sensitivity than prior studies, with implications for projections of future climate. 1) Tierney et al. (2020). Nature. 2) CLIMAP Project Members (1976). Science. 3) MARGO Project Members (2009). Nat. Geosci. 4) Jenkins et al. (2019). Mar. Chem. 5) Aeschbach et al. (2000). Nature. 6) Kipfer et al. (2002). Rev. Mineral. Geochem. 7) Stute et al. (1995). Science. 8) Weyhenmeyer et al. (2000). Science. 9) Seltzer et al. (2021). Nature. 10) Kageyama et al. (2021). Clim. Past.

Key Points: • We use Sentinel-1 Synthetic Aperture Radar (SAR) data to derive a finite fault model for the 2021 M7.4 Maduo (Qinghai, China) earthquake • The along-strike averaged coseismic slip has a maximum at depth of 3-4 km, with an amplitude of ∼2.5 m. • Up to ∼0.1 m of afterslip occurred on the fault trace in the first month following the earthquake. Abstract The 2021 Maduo earthquake ruptured a 150 km-long left-lateral fault in the northeast Tibet. We used Synthetic Aperture Radar data collected by the Sentinel-1A/B satellites within days of the earthquake to derive a finite fault model and investigate the details of slip distribution with depth. We generated coseismic interferograms and pixel offsets from different look directions corresponding to the ascending and descending satellite orbits. At the eastern end the rupture bifurcated into two sub-parallel strands, with larger slip on the northern strand. Inversions of coseismic displacements show maximum slip to the east of the epicenter. The averaged coseismic slip has a peak at depth of 3-4 km, similar to slip distributions of a number of shallow strike-slip earthquakes. Postseismic observations over several weeks following the Maduo earthquake reveal surface slip with amplitude up to 0.1 m that at least partially eliminated the coseismic slip deficit in the uppermost crust. Plain language summary A large earthquake occurred in a remote area of northeast Tibet (Qinghai Province, China) on May 21, 2021. The earthquake produced a 150 km-long rupture with surface offsets up to several meters. We used data collected by orbiting satellites to map motions of the Earth’s surface that occurred during and shortly after the earthquake. The measured surface displacements were used to constrain the rupture geometry and slip distribution at depth. Best-fitting models suggest that rupture occurred on a sub-vertical fault steeply dipping to the north, with most of slip occurring to the east of the earthquake epicenter. The maximum coseismic slip occurred in the uppermost crust, in the depth interval of 3-4 km below the Earth’s surface. A decrease in the fault offsets toward the Earth’s surface is likely caused by an increased frictional resistance of the shallow layer to rapid coseismic slip. Satellite observations made in the first month after the earthquake reveal that the shallow part of the fault is slowly catching up with a deeper part to make up for the difference in the amount of slip produced during the earthquake.

Water stored in lakes and underground is a crucial component of the global hydrological cycle, with impacts on climate and sea level. However, long-term changes in the global distribution of this water are not well understood. Here we present the Water Table Model (WTM), which is capable of computing water-table elevation at large spatial scales and over long temporal scales. The WTM comprises two components: groundwater and dynamic lakes. The inclusion of a dynamic lake component allows us to incorporate surface-water movement and evaporation into water-table elevation estimates. We share sample results from both an artificial topography, and for the North American continent. These results indicate the close interactions between changes to water levels in lakes and the surrounding groundwater tables. The open-source code for the WTM is available on Github and Zenodo.

Large ensembles of model simulations require considerable resources, and thus defining an appropriate ensemble size for a particular application is an important experimental design criterion. Utilizing the recently developed CLIVAR ENSO Metrics Package (Planton et al., 2021), we estimate the ensemble size (N) needed to assess a model’s ability to capture observed ENSO behavior. Using the larger ensembles available from CMIP6 and the CLIVAR Large Ensemble Project, we find that larger ensembles are needed to robustly capture baseline ENSO characteristics (N > 65) and physical processes (N > 50) than the background climatology (N ≥ 12) and remote ENSO teleconnections (N ≥ 6). While these results vary somewhat across metrics and models, our study highlights that ensembles are required to robustly evaluate simulated historical ENSO behavior, and provide initial guidance for designing model ensembles to reliably evaluate and compare ENSO simulations.

The compound extremes of hydrometeorological variables might not only affect as an extreme from a single variable, but might also result due to their interaction in time and space. This occurrence is called compound unusual events. A statistical approach based on the data depth function is being used to identify these occurrences in the multivariate dataset. To illustrate the methodology, the daily historical series of precipitation and mean temperature series from several selected observation stations in Germany will be used. The zero values of precipitation and the anomalies of mean temperature are used in this study. This study will help in answering the occurrence of unusual events for both variables on temporal and spatial scales.

The Advanced Topographic Laser Altimetry System (ATLAS) is the sole instrument on the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2). Without some method of reducing the transmitted data, the volume of ATLAS telemetry would far exceed the normal X-band downlink capability or require many more ground station contacts. The ATLAS Onboard Flight Science Receiver Algorithms (hereinafter Receiver Algorithms or Algorithms) control the amount of science data that is telemetered from the instrument, limiting the data volume by distinguishing surface echoes from background noise, and allowing the instrument to telemeter data from only a small vertical region about the signal. This is accomplished through the transfer of the spacecraft’s location and attitude to the instrument every second, use of an onboard Digital Elevation Model, implementation of signal processing techniques, and use of onboard relief and surface type reference maps. Extensive ground testing verified the performance of the Algorithms. On-orbit analysis shows that the Algorithms are working as expected from the ground testing; they are performing well and meeting the mission requirements.

The Dead Sea Fault (DSF) is a plate-boundary where large earthquakes are expected and largely overdue due to the lack of such events in the instrumental era. Sequences of earthquakes along the DSF are documented by historical evidence, one of the most devastating occurred in the mid-8th century CE. Here we describe site-specific archaeoseismological observations at the ancient Tiberias city, on the western shore of the Sea of Galilee. We map Roman and Byzantine relics faulted in the mid-8th century CE by a pure normal fault. We use geophysical, geomorphological and structural analyses integrated with published data, to assess the seismic hazard of the Jordan Valley Western Boundary Fault (JVWB). We propose that the normal JVWB can rupture the surface along its ~45 km trace running from Tiberias toward the S crossing Bet Shean, Tel Rehov and Tel Teomim. The JVWB, parallel to the main strike-slip Jordan Valley Fault segment, might be regarded as a major earthquake source in this region. We test the hypotheses of both single fault and multi-faults rupture scenarios, which result in an expected range of Mw from 6.9 (single rupture of the JVWB) to 7.6 (multiple rupture of the JVWB and Jordan Valley Fault). Our results suggest that seismic source characterization in the Sea of Galilee region must include normal faults capable of surface rupturing, despite the absence of such events in the instrumental catalogue.

The influence of aerosol particles on cloud reflectivity remains one of the largest sources of uncertainty in our understanding anthropogenic climate change. Commercial shipping constitutes a large and concentrated aerosol perturbation in a meteorological regime where clouds have a disproportionally large effect on climate. Yet, to date, studies have been unable to detect climatologically-relevant cloud radiative effects from shipping, despite models indicating that the cloud response should produce a sizable negative radiative forcing (perturbation to Earth’s energy balance). We attribute a significant increase in cloud reflectivity to enhanced cloud droplet number concentrations within a major shipping corridor in the southeast Atlantic. Prevailing winds constrain emissions around the corridor, which cuts through a climatically-important region of expansive low-cloud cover. We use universal kriging, a classic geostatistical method, to estimate what cloud properties would have been in the absence of shipping. In the morning, cloud brightening is consistent with changes in microphysics alone, whereas in the afternoon, increases in cloud brightness from microphysical changes are offset by decreases in the total amount of cloud water. We find a radiative forcing in the southeast Atlantic shipping corridor two orders of magnitude greater than previous observational estimates. Approximately five years of data are required to identify a clear signal. Extrapolating our results globally, we calculate an effective radiative forcing due to aerosol-cloud interactions in low clouds of -0.62 W/m2 (-1.23 to -0.08 W/m2). The unique setup in the southeast Atlantic could be an ideal test for the representation of aerosol-cloud interactions in climate models.

Sorghum is an important cereal crop grown across the globe for its grain and biomass value. It can also efficiently use resources such as nitrogen, and multiple varieties that are nitrogen-use and light-capture efficient are constantly being developed. This study focuses on using the spectral signature of sorghum varieties to predict flowering days, which could be used as a proxy for plants’ growth/productivity and development trends, thus helping breeders make quick decisions about what varieties to move to the next stage. Multiple sorghum varieties from the sorghum association panel were planted in a replicate-design field experiment with the variable supply of nitrogen. The flowering days were monitored and recorded. The hyperspectral reflectance data were collected and used to build a sorghum flowering days predictive model. Although regression models such as partial least square have been used to predict plants’ phenotypes, the non-parametric ensemble machine learning model turned out to perform better on flowering days with an accurate model up to 5 days.

Rubble pile asteroids such as (162173) Ryugu have large bulk porosities, which are believed to result from void spaces in between the constituent boulders (macroporosity) as well as void spaces within the boulders themselves (microporosity). In general, both macroporosity and microporosity are estimated based on comparisons between the asteroid bulk density and both the bulk and grain density of meteorite analogues, and relatively large macroporosities are usually obtained. Here we use semi-empirical models for the macroporosity of multi-component mixtures to determine Ryugu’s macroporosity based on the observed size-frequency distribution of boulders on the surface. We find that Ryugu’s macroporosity can be significantly smaller than usually assumed, as the observed size-frequency distribution allows for an efficient packing of boulders, resulting in a macroporosity of $16 \pm 3$~\%. Therefore, { we confirm that} Ryugu’s high bulk porosity is a direct consequence of a very large boulder microporosity. Furthermore, using estimates of boulder microporosity of around { 50~\%} as derived from in-situ measurements, the average grain density in boulders is { $2848 \pm 152$ kg m$^{-3}$, similar to values obtained for CM and the Tagish lake meteorites}. Ryugu’s bulk porosity corresponding to the above values is { 58~\%.} { Thus, the macroporosity of rubble pile asteroids may have been systematically overestimated in the past.}

Specification and forecast ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionosphere research. Neural networks (NNs) emerge as a powerful modeling tool for Ne prediction. However, heavy manual attention costs time to determine the optimal NN structures. In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to address this problem of NN models. NAS aims to find the optimal network structure through the alternated optimization of the hyperparameters and the corresponding network parameters. A total of 16-year data from Millstone Hill incoherent scatter radar (ISR) are used for NN models. One single-layer NN (SLNN) model and one deep NN (DNN) model are trained with NAS, namely SLNN-NAS and DNN-NAS, for Ne prediction and compared with their counterparts without NAS from previous studies, denoted as SLNN and DNN. Our results show that SLNN-NAS and DNN-NAS outperformed SLNN and DNN, respectively. NN models can reveal more finer details than the empirical ionospheric model developed using traditional data fitting approaches. DNN-NAS yields the best prediction accuracy measured by quantitative metrics and rankings of daily pattern prediction. The limited improvement of NAS is likely due to the network complexity and the limitation of fully connected NN without a memory mechanism.

Newly formed oceanic crust interacts with penetrating seawater, resulting in the formation of secondary minerals. Sediment cover can potentially change the redox conditions of underlying basaltic crusts, significantly affecting the types of secondary minerals and element transfer during alteration. However, previous studies have not revealed the quantitative regional variation and controlling factors of seafloor alteration using altered samples taken from different sites. We present a novel approach for the quantitative analyses of element mobility related to seafloor alteration based on a regional dataset of altered basalt bulk compositions and highlights the effects of the redox state and duration on alteration. The protolith reconstruction models (PRMs), machine learning-based element mobility analyses, were applied to the compositional data of the basaltic crusts from the South/Northwest Pacific region. The analyses revealed that altered basalts with older ages showed higher element mobility, particularly characterized by an enrichment of Rb and K, which were associated by up to 100 times with the formation of secondary minerals. In the oxidative settings of the South Pacific region, enrichment of Ba, U, and Pb and depletion of P were observed in samples with intense alteration. In contrast, under reductive conditions in the Northwest Pacific region, alterations associated with carbonate veins caused U enrichment. Our research suggests that sediment thickness is a key factor in the redox conditions during alteration, which changes the characteristics of element transfer and secondary minerals. Additionally, seafloor alteration likely persisted for at least 30 Myr, irrespective of whether the environment was oxidative or reductive.

It is well-established that positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impacts land-atmosphere interactions, disrupts the global carbon cycle, and accelerates climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impact. Currently, few earth system models account for permafrost carbon feedback mechanisms. This research identifies, interprets, and explains the feedback sensitivities attributed to permafrost degradation and terrestrial carbon cycling imbalance with in situ and flux tower measurements, remote sensing observations, process-based modeling simulations, and deep learning architecture. We defined and formulated high-resolution polymodal datasets with multitemporal extents and hyperspatiospectral fidelity (i.e., 12.4 million parameters with 13.1 million in situ data points, 2.84 billion ground-controlled remotely sensed data points, and 36.58 million model-based simulation outputs to computationally reflect the state space of the earth system), simulated the non-linear feedback mechanisms attributed to permafrost degradation and carbon cycle perturbation across Alaska with a process-constrained deep learning architecture composed of cascading stacks of convolutionally layered memory-encoded recurrent neural networks (i.e., GeoCryoAI), and interpreted historical and future emulations of freeze-thaw dynamics and the permafrost carbon feedback with a suite of evaluation and performance metrics (e.g., cross-entropic loss, root-mean-square deviation, accuracy). This framework introduces ecological memory components and effectively learns subtle spatiotemporal covariate complexities in high-latitude ecosystems by emulating permafrost degradation and carbon flux dynamics across Alaska with high precision and minimal loss (RMSE: 1.007cm, 0.694nmolCH4m-2s-1, 0.213µmolCO2m-2s-1). This methodology and findings offer significant insight about the permafrost carbon feedback by informing scientists and the public on how climate change is accelerating, strategies to ameliorate the impact of permafrost degradation on the global carbon cycle, and to what extent these connections matter in space and time.

Large earthquakes rapidly denude hillslopes by triggering thousands of coseismic landslides. The sediment produced by these landslides is initially quickly mobilised from the landscape by an interconnected cascade of processes. This cascade can dramatically but briefly enhance local erosion rates. Hillslope and channel processes, such as landsliding and debris flows, interact to influence the total mass, calibre, and rate of sediment transport through catchments. Calculating the sediment budget of an earthquake lends insight into the nature of these interactions. Using satellite imagery derived landslide inventories, channel surveys and a literature review combined with a Monte Carlo simulation approach we present a constrained sediment budget of the first decade after the 2008 Mw7.9 Wenchuan earthquake. With this sediment budget we demonstrate that debris flows are dominant process for delivering sediment into channels and that large volumes of sediment remain in the landscape. In our study area over 88% (469 Mega tonnes) of the coseismically generated sediment remains on the hillslopes in 2018. Of the 12% of the sediment that was mobilised, 69% (40.7 14 Mt) was mobilised by debris flows. Despite the large proportion of sediment remaining on the hillslope, the frequency of debris flows declined significantly over our observation period. The reduction in debris-flow frequency is not correlated to reductions in the frequency of triggering storms, suggesting changes in the mechanical properties of hillslope sediment may drive this observation. The stabilisation of coseismically generated sediment greatly extends its residence time and may influence catchment sediment yields for centuries or millennia.

Unpaved roads are ubiquitous features of anthropogenic landscapes that facilitate the use and extraction of coveted resources. Until recently, unpaved roads had been overlooked as significant drivers of erosion and sediment yields in the Northeastern Caribbean. This paper summarizes findings of two decades of work documenting the role human disturbance on surface erosion and sediment yields. Project objectives have been to: (1) quantify the role of roads on surface erosion and watershed sediment yields; (2) identify controlling factors; (3) evaluate the effectiveness of mitigating measures; and (4) assess road hydrological and sediment connectivity. In the small, dry-tropical coastal watersheds of the U.S. Virgin Islands and Puerto Rico, land use change is driven by low-density urban development associated to tourism. In this setting, ephemeral streams deliver runoff to coral-bearing waters only ~4–5 times every year. However, unpaved roads covering only ~0.1–3% of the land surface can increase runoff delivery up to ~40 times per year. In this setting, road erosion may be up to four orders of magnitude above background and they contribute 80–99% of sediment yields. Mitigation strategies have included road drainage improvements, road paving, and sediment detention ponds. In the sun-grown coffee growing region of the wet tropical highlands of Puerto Rico, roads are a key source of the sediment affecting downstream water resources. A high erosion potential exists due to its steep relief, copious rainfall (1.6-2.1 m y-1), and considerable soil exposure. Here, watershed-scale sediment yields are 3–30 Mg ha-1 y-1, yet surface erosion rates under its natural forested cover are only ~0.25 Mg ha-1 y-1. Unpaved road erosion on these settings is 14–780 times faster than on forested hillslopes. Gravel reduces road erosion rates by 66 – 90%, but the effect seems to fade after one to two years. Cultivated lands contribute 5–63% of farm-scale surface erosion, and roads are responsible for the remainder—even though they cover 8–15% of the total farm area. In the 45-km2 Lucchetti Watershed, surface erosion from coffee farms equals 1–18% of its long-term sediment yield. The residual sediment is believed to originate from sources being quantified presently; these include road cutslopes, gullies, and landslides.

Monitoring in season crop conditions is critical when assessing the food security situation and prompting action to mitigate adverse outcomes in largely rain-fed agricultural systems. The Group of Earth Observation for Global Agricultural Monitoring (GEOGLAM) has been supporting global assessments for partners in the Agricultural market information system (AMIS) and in countries that are at risk of food insecurity with the Crop Monitor for AMIS and Crop Monitor for Early Warning respectively. Africa, and specifically Kenya is dependent on rain-fed agricultural production and with climate variability and change, timely, relevant and accurate information on crop conditions is necessary to ensure appropriate and sometimes lifesaving responses. By implementation of national crop monitors countries can synthesize and customize information to suit their reporting metrics and provide detailed sub-regional assessments in a standardized format that can inform global and regional assessments. Successful implementation and publication of The Kenya Crop Monitor Bulletin by the Kenyan Ministry of Agriculture and Irrigation is already influencing agricultural decision making. The bulletin is being used to inform internal decision making in the ministry; and through dissemination on the ministry’s website, as an important source of information for food security agencies. The report which combines earth observation data and field reports from county officers, is improving the way food security decisions are being made. Specifically, monitoring the spread of diseases and pests such as the Fall Army Worm, assessing the implications of extreme events such as floods and droughts on production, providing expected trends based on the prevailing conditions and the expected yield outlook at the end of the season. Through the bulletin, the government has also been able to assess the impact of subsidies on production and as an early warning report to prompt for mitigation and other responses.

In a recent study, Sosa-Gutierrez et al. (2022, https://doi.org/10.1029/2021GL097484) evaluated the potential impacts of tropical cyclones (TCs) on the Atlantic pelagic Sargassum using satellite-based Sargassum maps, 86 hurricane tracks during 2011 – 2020, and statistical analysis. The results showed an average drop of 40% in Sargassum coverage under TC trajectories, attributed to Sargassum sinking. However, there appear two issues: 1) the Sargassum maps contain large uncertainties due to methodology used in developing the maps. The impacts of these uncertainties on change detection are largely unknown, especially along the TC trajectories where cloud cover prevails; 2) there is a lack of a “control” experiment in the logic to infer causality. Based on these observations and arguments, while it is possible that TCs may have significant impacts, either positively or negatively, on pelagic Sargassum, a revisit appears necessary to use improved Sargassum maps and better experimental design before drawing conclusions.

The projected response of the atmospheric circulation to changes driven by increasing greenhouse gas concentrations is highly uncertain. One of the primary reasons for this is that the state-of-the-art models we employ to investigate these responses struggle to represent basic features of the midlatitude circulation such as storm tracks, jets and blocking. Biases also have detrimental effects on predictive skill for dynamically driven fields at climate prediction time scales of seasons to decades. Despite this, physical understanding of the controls on these features and the drivers of their biases is still limited. Here we investigate a hierarchy of large ensemble climate reanalysis and hindcast simulations performed by the Norwegian Earth System Model (NorESM). Each ensemble is 30 members and was run from 1985-2010. For the reanalysis runs various data-assimilation strategies were employed. These are: SST only, SST plus hydrographic profiles, SST plus hydrographic profiles plus sea-ice concentration. The assimilation was performed monthly after which the model runs freely. These are compared to both free runs and AMIP-style simulations with ERA-Interim serving as ground truth. We evaluate the North Pacific and North Atlantic jets in winter and summer. We also identify where the observations lie within the predictive distribution of the ensemble. Results show that the North Atlantic jet is too zonal, extends too far into Europe and is shifted northwards. Virtually the entire North Atlantic sector lies outside the predictive distribution of the ensemble and performance actually degrades in the simulations with tighter constraints on the assimilation. By contrast the North Pacific jet is rather better represented in all aspects both with respect to pattern as well as magnitude of the biases. This is likely due to the better-represented teleconnections between the tropical and extratropical Pacific. Comparison of these ensembles with AMIP simulations suggests that the errors in the midlatitude circulation reside in the atmospheric component of the model. We also present results from hindcast simulations where NorCPM was initialized at different times of the year and then run forward 12 months. Implications and causes of the varying behavior among the ensembles are discussed as well as prospects for the future.