Accurate simulations of high latitude ecosystems are critical for confident Earth system model (ESM) projections of carbon cycle feedbacks to global climate change. Land surface model components of ESMs, including the E3SM Land Model (ELM), simulate vegetation growth and ecosystem responses to changing climate and atmospheric CO2 concentrations by grouping heterogeneous vegetation into like sets of plant functional types (PFTs). Such models often represent high-latitude vegetation using only two PFTs (shrub and grass), thereby missing the diversity of vegetation growth forms and functional traits in the Arctic. Here, we use field observations of biomass and leaf traits across a gradient of plant communities on the Seward Peninsula in northwest Alaska to replace the original ELM configuration for the first time with nine arctic-specific PFTs. The PFTs that are new to the model include: 1) nonvascular mosses and lichens, 2) deciduous and evergreen shrubs of various height classes, including an alder PFT, 3) graminoids, and 4) forbs. Improvements relative to the original model configuration included greater belowground biomass allocation, persistent fine roots and rhizomes of nonwoody plants, and better representation of variability in total plant biomass across sites with varying plant communities and depth to bedrock. Simulations through 2100 using the RCP8.5 climate scenario showed alder-dominated plant communities gaining more biomass and lichen-dominated communities gaining less biomass compared to default PFTs. Our results highlight how representing the diversity of arctic vegetation and confronting models with measurements from varied plant communities improves the representation of arctic vegetation in terrestrial ecosystem models.

The prevailing hypothesis to explain pCO2 rise at the last glacial termination calls upon enhanced ventilation of excess respired carbon that accumulated in the deep sea during the glacial. Recent studies argue lower [O2] in the glacial ocean is indicative of increased carbon respiration. The magnitude of [O2] depletion was 100-140µmol/kg at the glacial maximum. Because respiration is coupled to d13C of dissolved inorganic carbon (DIC), [O2] depletion of 100-140µmol/kg from carbon respiration would lower deep water d13CDIC by ~ 1‰ relative to surface water. Prolonged sequestration of respired carbon would also lower the amount of 14C in the deep sea. We show that Pacific Deep Water d13CDIC did not decrease relative to the surface ocean and D14C was only ~50‰ lower during the late glacial. Model simulations of the hypothesized ventilation change during deglaciation lead to large increases in d13CDIC, D14C and ε14C that are not recorded in observations.

Oblique propagation of gravity waves (GWs) refers to latitudinal propagation (or vertical propagation away from their source) from the low latitude troposphere to the polar mesosphere. This propagation is not included in current gravity wave parameterization schemes, but may be an important component of the global dynamical structure. Previous studies have revealed a high correlation between observations of GW Momentum Flux (GWMF) from monsoon convection and Polar Mesospheric Clouds (PMCs) in the northern hemisphere. In this work, we report on data and model analysis of the effects of Stratospheric Sudden Warmings (SSWs) in the northern hemisphere, on the oblique propagation of GWs from the southern hemisphere tropics, that in turn influence PMCs in the southern summer mesosphere. In response to SSWs, vertical propagation of GWs from high-latitude winter hemisphere is at mid latitudes and appears more slanted toward the equator with increasing altitude, following the weaker stratospheric eastward jet. The oblique propagation of GWs from southern monsoon regions tends to start at higher altitudes with a sharper poleward slanted structure towards the summer mesosphere. The correlation between PMCs in summer southern hemisphere and the zonal GWMF from 50°N to 50°S exhibits a high-correlation pattern that connects the winter stratosphere with the summer mesosphere, indicating the influence of inter-hemispheric coupling mechanism. Temperature and wind anomalies suggest that the dynamics in winter hemisphere can influence the equatorial region, which in turn, can influence the oblique propagation of monsoon GWs.

Both ground- and satellite-based airglow imaging have significantly contributed to our understanding of the low-latitude ionosphere, especially of the morphology and dynamics of the equatorial ionization anomaly (EIA). The NASA Global-scale Observations of the Limb and Disk (GOLD) mission focuses on far-ultraviolet airglow images from a geostationary orbit at 47.5°W. This region is of particular interest at low magnetic latitudes because of the high magnetic declination (i.e., about -20°) and proximity of the South Atlantic magnetic anomaly. Nighttime airglow images from GOLD reveal an exciting feature of the EIA. Using observations from 5 October 2018 to 30 June 2020, we characterize a wave-like structure of few thousands of kilometers seen as poleward and equatorward displacements of the nighttime EIA-crests. Initial analyses show that the mesoscale structure is symmetric about the dip equator and appears nearly stationary with time over the night. In quasi-dipole coordinates, maxima poleward displacements of the EIA-crests are seen at about ±12° latitude and around 20° and 60° longitude (i.e., in geographic longitude at the dip equator, about 53°W and 14°W). The wave-like structure presents typical zonal wavelengths of about 6.7x10^3 km and 3.3x10^3 km. The structure’s occurrence and wavelength are highly variable on a day-to-day basis with no apparent dependence on geomagnetic activity. In addition, a cluster or quasi-periodic wave train of equatorial plasma depletions (EPDs) is often detected within the mesoscale structure. We further outline the difference in observing these EPDs from FUV images and in situ measurements during a GOLD and Swarm mission conjunction.

Understanding the flood generating mechanisms that influence flood seasonality in a region provides information on setting up relevant contingency measures. While former studies had estimated flood seasonality at regional/continental scale, limited/no studies had investigated the climate/basin drivers that influence the changes in flood seasonality. Considering this, the current study performed two analysis i) estimated the changes in the seasonality of annual maximum floods (AMF) between pre- and post-1970 across Hydroclimate Data Network basins over the coterminous United States, and ii) identified the predictors that influence the change in the seasonality from a set of climate and geomorphic variables. Significant changes in the AMF seasonality were noted for approximately half of the basins in the eastern US while low to no change was found in a majority of the basins in the central/western US. We found that a decrease (increase) in the seasonality index, indicating floods arriving more uniformly (more concentrated in time), is typically associated with an increase in the precipitation (temperature) in basins where a strong change in flood seasonality occurs. Elevation has a more dominant role as compared to the drainage area in changing the flood seasonality as the former affects the form of precipitation in basins in higher elevations. This is particularly true for western US where floods arrive more distributed over the year (i.e., decrease in flood seasonality index), which potentially indicates increased warming resulting in early snowmelt.

The pandemic in 2020 caused an abrupt change in the emission of anthropogenic aerosols and their precursors. We provide the first estimate of the associated change in the aerosol radiative forcing at the top of the atmosphere and the surface. To this end, we perform new simulations with the contemporary Earth system model EC-Earth3 participating in CMIP6, and created new data on the anthropogenic aerosol optical properties and an associated effect on clouds for the implemented aerosol parameterization, MACv2-SP. Our results highlight the small impact of the pandemic on the global aerosol radiative forcing in 2020 compared to the baseline of the order of +0.04Wm-2, which is small compared to the natural year-to-year variability in the radiation budget. Natural variability also limits the ability to detect a meaningful regional difference in the anthropogenic aerosol radiative effects. We identify the best chances to find a significant change in radiation at the surface during cloud-free conditions for regions that were strongly polluted in the past years. The new post-pandemic recovery scenarios indicate a spread in the aerosol forcing of -0.68 to -0.38Wm-2 for 2050, which translates to a difference of +0.05 to -0.25Wm-2 compared to the baseline. This spread falls within the present-day uncertainty in aerosol radiative forcing and the CMIP6 spread in aerosol forcing at the end of the 21st century. We release the new MACv2-SP data for studies on the climate response to the pandemic and the recovery scenarios.

In Gravity Recovery and Climate Experiment (GRACE) Follow-on (GRACE-FO) mission, similar to its predecessor GRACE, the twin satellites are equipped with three-axis accelerometers, measuring the non-gravitational forces. After one month in orbit, during the in-orbit-checkout phase, the noise on GRACE-D accelerometer measurements elevated and results in systematical degradation of the data. For this reason, the GRACE-D data needs to be replaced by synthetic data, the so-called transplant data, officially generated by the GRACE-FO Science Data System (SDS). The SDS transplant data is derived from the GRACE-C accelerometer measurements, by applying time and attitude corrections. Furthermore, model-based residual accelerations due to thruster firings on GRACE-D were added, proven to improve the data quality in gravity field recovery. However, preliminary studies of GRACE-FO data during the single accelerometer months show that the low degree zonal harmonics, in particular C20 and C30, are sensitive to the current transplant approach. In this work, we present a novel approach to recover the GRACE-D ACT1B data by incorporating non-gravitational force models and analyze its impact on monthly gravity field solutions. The results show the improved ACT1B data not only contributed to a noise reduction but also improve the estimates of the C20 and C30 coefficients. The application of this new approach demonstrates that the offset between Satellite Laser Ranging (SLR) and GRACE-FO derived C30 time series can be reduced by the use of the alternative accelerometer product.

Current stochastic rupture modeling techniques do not consider the influence of first order fault zone characteristics. One such key characteristic is fault slip deficit or inter-seismic locking which has shown correlation between areas of high coupling and areas of greater slip in many recent large ruptures globally. Therefore, it is reasonable to assume that it should be considered as prior information in rupture modeling. Here, we first present a mathematical formalism to introduce locking models as prior information into stochastic rupture modeling. We then focus on how introducing slip deficit information into the stochastic rupture models influences slip distributions for the Cascadia Subduction Zone (CSZ). We compare rupture models created with two end member models of locking, one with a fully locked zone extending to the trench and another with locking deeper downdip, along with models created without a prior knowledge of locking. Large variations occur and correlate well with areas with the largest differences in slip deficit. To exemplify their impacts, the ruptures are then used for probabilistic tsunami hazard assessment. We find that overall the tsunami amplitudes generated are much more hazardous in the northern extent of the CSZ where differences in locking distribution are more prevalent. Although large uncertainties are present in accuracy of locking, imposing either constraint created very different hazard estimations when compared to the hazards where no prior locking information was used. This highlights the necessity to expand seafloor instrumentation and to consider first order fault information like locking in future authoritative hazard assessments.

The Barents Sea attracts year-around human activity as the winter sea ice cover retreats, creating a need for short and long term prediction of environmental conditions in the region. Previous studies have shown that local ocean heat content and heat transport at the Barents Sea Opening provide interannual to decadal predictability of Barents Sea ice cover. Part of this predictability is suggested to originate from thermodynamic anomalies propagating along the Norwegian Atlantic Current. To better understand this source of predictability, and the relevant timescales, we use models (Coupled Model Intercomparison Project Phase 6; CMIP6) and satellite observations, to study the linear response of the monthly mean Barents Sea ice cover to downstream sea surface temperature anomalies. We show that in March the sea ice response is strongest on short lead times (<2 year), vanishing towards ~7 year timescale and that the linear sea ice response function can be reconstructed using an advective-diffusive ‘leaky-pipe’ model with multiple propagation timescales. The sea surface temperature based sea ice predictability is linked to decadal and longer timescale variability. Our results also show that sea surface temperatures close to the sea ice edge provide the best predictability at short timescales, but with a skill that approaches that of the sea surface temperatures further away at long timescales.

We report the results of two Earth System Model (ESM) configurations which differ in their ocean physics around New Zealand. The first is a global low-resolution configuration of UKESM1.0 while the second model, NZESM has an eddy-permitting ocean embedded around New Zealand. The nominal ocean resolution of the UKESM is 1 degree and that of the NZESM is 0.2 degrees. Near New Zealand, total cloud amount is negatively correlated with temperature. This relationship is reversed near the seasonal sea ice edge where increased evaporation results from open ocean which was previously covered in sea ice. In the simulations, the change to the cloud amount is dominated by changes to stratocumulus cloud and the resulting improvement to shortwave cloud radiative effect - with respect to CERES-EBAF observations - is statistically significant at the 95% level across the Southern Ocean, assuming a normally distributed control ensemble. The near-surface air temperature in the vicinity of the nested ocean model is also improved, when compared to ERA5 reanalysis data. In general, clouds and their radiative effects over the Southern Ocean are not well simulated by Earth System Models and the changes made here improve both near-surface temperature near New Zealand and zonal mean shortwave cloud radiative effect across the Southern Ocean. Noting that the development of climate models always involves an element of ‘tuning’, changing the regional ocean physics without doing any further tuning (as is the case here), will tend to remove some compensating bias and therefore make the model-observation agreement in some regions less good.

Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow-slip within the more ductile portion of the crust beneath the seismogenic zone. Monitoring efforts over 15 years recorded >1 million LFEs with >70 per day. We apply machine learning (ML) to statistical features describing the seismic waveforms and estimate the LFE daily intensity. Using 4 years of independent data, the ML model produces a 0.68 correlation. The burst-like LFE behavior is reproduced and the largest misfit occurs during the low-amplitude daily undulations. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational-intensive template-matching catalog. Similarities are found between detecting LFEs and tremors, which provides evidence tremors are composed of LFEs. The approach reveals by ML the rich information contained in the features of continuous seismic waveforms.

The Maranhão reservoir (~180 hm3) is mainly used for energy production and irrigation of 15360 ha in the Sorraia Valley, in southern Portugal. The necessity to save water in the Autumn/Winter rainfall season to be used for irrigation of Spring/Summer crops creates a sensible management situation regarding the control of floods and their impact on downstream areas in years with extreme precipitation events. Streamflow forecast is then essential to improve the reservoir’s management regarding water storage. This study addresses the estimation of the daily streamflow in the watershed draining to the Maranhão reservoir (2311 km3) following two different approaches. Firstly, the physically based hydrological model, MOHID-Land, was calibrated/validated for estimating daily streamflow in the study area following physically processes and using a finite volume approach, which require considerable amount of input data. Secondly, a data-driven model composed of an artificial neural network (ANN) was used with the same purpose. This ANN model was selected from a previous work where different models (multi-layer perceptron, convolutional neural network, and long short-term memory model) were tested with different scenarios for the combination of input variables. Both models were optimized considering the observed streamflow in the Ponte Vila Formosa station, which drains 30% of the Maranhão watershed, being after applied to the entire domain. The results for the Maranhão watershed were then validated considering a mass balance considering the reservoir’s outflow, level, and water consumed values. The ANN model had a better performance predicting streamflow than the physically based model, and with less calibration effort. However, the physically based model can give much more information about the entire system. Lastly, it is expected that a physically based model can correctly estimate the streamflow for extreme events not considered in the calibration and validation datasets, but for the ANN models this question should be carefully addressed since data-driven models are event-based.

Computational software for planetary science and astrobiology is vital for understanding how complex systems interact, and for the analysis of sparse spacecraft data. PlanetProfile is an open source framework for modeling the interior of planetary bodies, especially ocean worlds, and is available in Matlab. We have rebuilt PlanetProfile in Python to improve its accessibility, as Python is free and widely used in scientific computing. PlanetProfile features such as the calculation of induced magnetic fields and seismic properties permit wider availability of these important tools for research purposes. An upcoming release of PlanetProfile contains the converted and streamlined software. New features such as a graphical user interface are in development that will support new users in taking advantage of these valuable tools.

We reveal a slip-partitioning rupture on the North Hovsgol Fault during the 2021 Mw6.7 Lake Hovsgol, Mongolia earthquake. Left-lateral motions on the Mondy Fault north of the epicenter likely controlled the observed slip partitioning pattern. The earthquake highlights the non-negligible role of the bounding strike-slip fault in the formation and evolution of oblique rift.

Among the most complex activities in water companies is extracting accurate operational data of Urban Water Infrastructure (UWI). Hence, the use of data-based methods would be faced limitations for asset management of the UWI. This shortcoming could be improved using knowledge-based decisions. In this study, using this method, Maintenance-Rehabilitation Planning (MRP) of urban water pipes is assessed. Hence, the criteria effective on the MRP of the pipes were firstly determined. Then, two groups of experts in Iran and Brazil were invited to rank the determined criteria. For this purpose, a Multi-Criteria Group Decision-Making (MCGDM) method was developed based on the nominal group technique. This MCGDM method was implemented based on two approaches; first, the viewpoints of non-weighted decision-makers were considered; and second, the experts were weighted to rank the criteria. Finally, the results of these approaches in the two countries were pairwise compared. This comparison led to improving the literature and management in water companies of Iran and Brazil by identifying the effective criteria for the MRP of the pipes based on knowledge-based decisions. Further in this field, it was found that weighting the experts have no decisive effect on the results; and this is only effective in groups where the decision-makers weights are identically distributed. Moreover, it was found that there is a relation between the similarity of results and the similarity of experts’ profiles in knowledge-based decision groups of various countries such as Iran and Brazil.

The rate of rocket launches is accelerating, driven by the rapid global development of the space industry. Rocket launches emit chemically and radiatively active species into the stratosphere, where they impact ozone. We create a per-vehicle inventory of geographically-resolved stratospheric emissions for 2019, accounting for flight profiles and all major fuel types in active use. The inventory is used to simulate an intensive near-future scenario (120 launches/year at 17 current spaceports) with a chemistry-climate model. These gas-phase rocket emissions produce an overall 0.5% decrease in global annual-mean total column ozone. Compared to a reference scenario, Antarctic springtime ozone decreases by up to 9%. Arctic springtime ozone decreases by up to 5%; equivalent to half of the depletion observed over this region due to chlorofluorocarbons in the late 20th century. Our findings reiterate the need for assessment and international cooperation regarding the impact of space industrialization on Earth’s systems.

Warmer and drier climate has contributed to increased occurrence of large, high severity wildfires in the Pacific Northwest, drawing concerns for water quality and ecosystem recovery. While nutrient fluxes generally increase post-fire, the composition of organic matter (OM) transported to streams immediately following a fire is poorly constrained, yet can play an integral role in downstream water quality and biogeochemistry. Here, we quantified spatiotemporal patterns of dissolved OM (DOM) chemistry for five streams burned by wildfires in Oregon, USA in 2020. We sampled over a 24-hour storm event one month after the fire, revealing variable temporal behavior in DOM dynamics. DOM chemistry was directly related with burn severity spatially. Specifically, nitrogen and aromatic character of DOM increased in streams burned at greater severity. Our results suggest a spatial overprinting of DOM dynamics immediately following fire activity and highlight a key gap in our knowledge of post-fire DOM transport to streams.

Most remote sensing-based surface energy balance (SEB) models are limited by data availability and physical constraints to fully capture the non-linear and temporally varying nature of atmospheric, biophysical, and environmental controls on evapotranspiration (ET). As such, currently, no single SEB model is considered to work best under all conditions particularly in irrigated croplands where surface moisture conditions could change dramatically in a short amount of time. Hence, irrigation water management based on a single remotely sensed ET model is often required to cope with model limitations and data latency issues, which could lead to unsustainable and unreliable accounting of water use over time. The recent inception of ensemble-based ET modeling takes the advantage of the strengths of the several SEB models under different conditions and is found to perform better as compared to an Individual model. Yet, challenges remain in how high-temporal ET outputs from different models are accurately assembled in a way that yields the most reliable estimates of ET across any environmental and surface conditions. Specifically, existing simple or Bayesian average and machine learning-based ensemble approaches have not been able to optimally utilize the comprehensive suite of existing SEB models and the availability of multiple remotely sensed datasets. Here, we discuss the utility of convolutional neural networks (CNNs) to assemble the outputs from a host of SEB models that can robustly capture the non-linear dynamics of ET under all conditions. We will also discuss the advantage and potential limitations of using the CNN-based ensemble ET modeling framework with respect to the individual, simple or Bayesian average, and other machine learning approaches and their implications for use in allocating water use across critically dry regions. Several ensemble models will be trained using eddy covariance flux data globally and will be evaluated based on their ability to estimate ET from MODIS and Landsat sensors with both individual and fused products and minimal weather inputs. The results can provide useful insights into how multiple datasets and SEB models could be optimally utilized to accurately monitor crop water status and support sustainable water resource management in drylands.