The atmospheric drag and the Radiation Pressure are the dominant forces acting on LEO satellites. Many different approaches have been followed for the modelling of these non-gravitational forces, based on the physics and the satellite characteristics, but in many cases large inconsistencies are present between the models and the accelerometer measurements. Atmospheric drag is considered as the most difficult force to model, and the Radiation Pressure models show large deviations from the measurements depending on the b′ angle and the position of the satellite near the entrance and the exit from the Earth’s shadow. Numerous models have been presented for GRACE satellites but none for GRACE-FO. The innovation of this study is the development of an atmospheric drag and a Radiation Pressure data-driven model based only on the accelerometer measurements of GRACE-C satellite, using least squares principles. The atmospheric drag is modelled using accelerometer measurements from the shadow segment of the orbit. An additional weighted constraint is that near the middle of the sun segment of the orbit, the drag in the x-direction should be equal to the actual measurements due to Radiation Pressure being nearly zero. Subsequently, we subtract the modelled drag from the real measurements in order to estimate the Radiation Pressure which, consequently, is modelled using a least squares frequency-domain analysis. The residual series proceeded from the subtraction of these two models from the actual measurements of GRACE-C accelerometer, are analyzed by taking into consideration the local time, the spatial information and the variations of b΄ angle, as well as their connection with electromagnetic changes in the upper atmosphere. The proposed models have been tested for different time periods in the last three years of GRACE C and the rms of the residual series along the x and the z axes of the accelerometer is ~2.5 nm/s2, while the y-axis exhibits an rms of ~1 nm/s^2.

We validate a new method to determine surface-wave attenuation from seismic ambient noise, both numerically and by application to recordings from a dense broadband array. We generate synthetic recordings of numerically simulated ambient seismic noise in several experimental setups, characterized by different source distributions and different values of attenuation coefficient. We use them to verify that: (I) “cross-terms” cancel out, as predicted by the theory; (II) the source spectrum can be reconstructed from ambient recordings, provided that the density of sources and the attenuation coefficient are known; (III) true attenuation can be retrieved from normalized cross correlations of synthetic signals. We then apply the so validated method to real continuous recordings from 33 broadband receivers distributed within the Colorado Plateau and Great Basin. A preliminary analysis of the signal-to-noise ratio as a function of azimuth reveals a SW-NE preferential directionality of the noise sources within the secondary microseism band (6-29 8 s), as previously reported by other authors. By nonlinear inversion of noise data we find the attenuation coefficient in the area of interest to range from ∼ 1 × 10 −5 m−1 at 0.3 Hz to ∼ 4.5 × 10 −7 m−1 at 0.065 Hz, and confirm the statistical robustness of this estimate by means of a bootstrap analysis. This result is compatible with previous observations made on the basis of both earthquake-generated and ambient Rayleigh waves. In this regard, the new method proves to be promising in accurately quantifying surface wave attenuation at relatively high frequencies.

GRACE-FO (GFO) and Swarm are two LEO missions that, among others, provide non-gravitational acceleration measurements required for geopotential model development and modelling of non-gravitational forces acting upon them. Unfortunately, the performance of the accelerometers on board for both missions is not the expected. Measurements from both missions present dominant bias jumps that occur on all accelerometer axes and they have been linked to the satellites’ entrance to and exit from the Earth’s shadow. These jumps are estimated and corrected at Level 1A of GFO C and at Level 2 of Swarm C in an optimal way using Least Squares methodology. The corresponding variances of the jumps are also calculated. Furthermore, the measurements contain spurious signals and dominant spikes mostly connected with thruster activation, mainly in the equatorial region or high temperature sensitivity. These disturbances have a significant impact on the data analysis. We propose an alternative weighting filter methodology to generate Level 1B data from Level 1A for GFO C that includes the attenuated spikes and their corresponding variances and does not involve the removal of the spikes nor does it include any interpolation to fill data gaps. This methodology is used for Swarm C accelerometer Level 2 dataset as well. Using spectral domain methods, we show that the newly generated GFO Level 1B and Swarm Level 2 data are not contaminated by the presence of spikes and data jumps. In the polar regions, mostly at the South pole, spikes in the measurements are connected to magnetic disturbances when the satellites enter these regions. Our proposed methodology contains an optimal and unbiased dataset of non-gravitational acceleration measurements that can be used for the estimation of geopotential models and also for the investigation of the accelerometer’s response to electromagnetic disturbances and the modelling of other non-gravitational accelerations to derive thermospheric neutral densities.

Dryland play a major role in the global carbon cycle. The US Southwest is experiencing fewer, larger precipitation events and longer dry intervals between rainfalls. These longer droughts are likely driving physiological, phenological, morphological, and community-level responses of dryland vegetation with unknown feedbacks to atmospheric CO2. It remains unclear how seasonal drought intensity and duration affect the magnitude, duration, and direction of dryland vegetation carbon cycling and atmospheric feedbacks. To address this question, we integrated the measurements of soil hydrology, plant community, and carbon fluxes from a new rainfall manipulation experiment site (RainManSR) in the Santa Rita Experimental Range of Southeast Arizona, US into the Community Land Model (CLM5). This field experiment imposed four precipitation treatments (S1–S4), each with the same summer growing season total rainfall (205 mm) but packaged into a range of many/small to few/large rainfall events. This experiment enabled a comprehensive evaluation and parameterization of drought tolerance of semiarid grassland plant functional types (i.e. deep-rooted perennials and shallow-rooted annuals) and their effects on climate extreme-carbon cycles feedbacks. The ability of the improved CLM model to capture dryland productivity and carbon fluxes was then validated at larger scales with observed carbon fluxes from closeby AmeriFlux sites in the US Southwest, such as the semi-arid Kendall grassland site (US-WKG). Applying this model in the Arizona grassland sites indicated that high tolerances of dryland plants to relatively low soil water potential maintains the growing season length of the dryland ecosystem under drought conditions, whereas the acclimation of carbon assimilation and root dynamics to drought mitigate drought effects on vegetation productivity and interannual variability of carbon exchange.

This study assesses the sub-seasonal predictability of the weekly mean geopotential height anomaly at 500 hPa and its relationship to teleconnections over the Northern Hemisphere in winter. The skill over the North Pacific, Canada, and Greenland is higher than over other areas for week-3 and -4 forecasts. These peaks correspond to the centers of action for the Pacific–North American (PNA) pattern and the North Atlantic Oscillation (NAO). PNA (NAO phase) predictions are better for El Niño years at lead times of 4 weeks (2–4 weeks). The effects of La Niña forcing on PNA and NAO forecasts are small compared with the El Niño forcing. Numerical models tend to predict a negative PNA at lead times of 3–4 weeks in La Niña years. The improvement in the mid-latitude upper-level jet rather than in the atmospheric response to ENSO forcing in the tropics is important for better S2S prediction.

Understanding mechanisms, rates, and drivers of carbonate formation provides insight into the chemical evolution of Earth’s oceans and atmosphere. We paired geological observations with elemental and isotope geochemistry to test potential proxies for calcium-to-alkalinity ratios (Ca:ALK). Across diverse carbonate facies from Pleistocene closed-basin lakes in Owens Valley, CA, we observed less δ44/40Ca variation than theoretically predicted (>0.75‰) for the very low Ca:ALK in these systems. Carbonate clumped isotope disequilibria implied rapid carbonate growth—kinetic isotope effects, combined with the diverse carbonate minerals present, complicated the interpretation of ∆δ44/40Ca as a paleo-alkalinity proxy. In contrast, we observed that high phosphate concentrations are recorded by shoreline and lake bottom carbonates formed in eleven Pleistocene lakes at orders of magnitude greater concentrations than marine carbonates. Because the maximum phosphate content of water depends on Ca:ALK, we propose that carbonate P/Ca can inform phosphate levels and thereby Ca:ALK of aqueous environments in the carbonate record.

Improved understanding of how our coasts will evolve over a range of time scales (years-decades) is critical for effective and sustainable management of coastal infrastructure. Globally, sea-level rise will result in increased erosion, with more frequent and intense coastal flooding. Understanding of current and future coastal evolution requires robust knowledge of the wave climate. This includes spatial, directional and temporal variability, with recent research highlighting the importance of wave climate directionality on coastal morphological response, for example in UK, Australia and California. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980–2017) and show that 73% are directionally bimodal. We find that winter-averaged expressions of six leading atmospheric indices are strongly correlated with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Coastal classification through hierarchical cluster analysis and stepwise multi-linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (r= 0.45–0.8; p<0.05). We demonstrate for the first time the significant explanatory power of leading winter-averaged atmospheric indices for directional wave climates, and show that leading seasonal forecasts of the NAO skillfully predict wave climate in some regions.

Uncertainty quantification and characterization in changing climate scenarios can have a direct impact on the efforts to mitigate and adapt. Chaotic and non-linear nature of atmospheric processes results in high sensitivity to initial conditions resulting in considerable variability. Multiple model ensembles of Earth System Models are often used to visualize the role of parametric uncertainties in mean and extreme attributes of precipitation trends in various time horizons. However, studies quantifying the role of internal variability in controlling extreme precipitation statistics in decadal and interdecadal scales are limited. In this study, we use a thirty one-member ensemble of Community Earth System Model Large ensemble project and thirty-one ensembles from Coupled Model Intercomparison Project 5 (CMIP5) to quantify the relative contribution of uncertainty due to internal variability in the depth and volatility of Indian Summer Monsoon Rainfall extremes of different durations and frequencies. We find that in the short-term and long-term, the role of internal variability in extreme precipitation indices is comparable to the uncertainty arising from structural differences in the model captured through multiple model ensembles. Further, we show that combining outputs from multiple initial condition runs generated to span the range of internal climate variability can help us reduce uncertainty in infrastructure design relevant Depth Duration and Frequency (DDF) curves.

Recent exceptionally hot droughts in Amazonia have highlighted the potential role of global warming in driving elevated fire risk and forest dieback. The previous generation of global climate models projected that eastern Amazonia would receive less future rainfall while western Amazonia would receive more rainfall, but many of these models disagreed on the sign of future precipitation trends in the region. Here Coupled Modeling Intercomparison Project, Phase 6 (CMIP6) models are used to examine the shifting risk of eastern Amazonian droughts under climate change. This new generation of models shows better agreement that the entire Amazonian basin will receive less future rainfall, with particularly strong agreement that eastern Amazonia will dry in the 21 century. These models suggest that global warming may be increasing the likelihood of exceptionally hot drought in the region, and by mid-century with unabated global warming, recent particularly warm and severe droughts will become more common. However, Amazonia is a region with a relatively sparse instrumental record that makes it difficult to test the ability of model simulations to reproduce observed long-term rainfall trends, and climate models have traditionally struggled to reproduce satellite-era observed trends in the region. These shortcomings highlight the need to improve confidence in global climate models’; ability to simulate future drought, even if more CMIP6 models agree on the sign of future rainfall trends.

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to a variety of satellite systems. While various models of the relativistic electron flux have been developed for geostationary orbit (GEO), the behaviour of the medium energy (120-600 keV) electrons below GEO remains poorly quantified. In this paper we present a Medium Energy electRon flux In Earth’s outer radiatioN belt (MERLIN) model based on the Light Gradient Boosting (LightGBM) machine learning algorithm. The MERLIN model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained and validated on $>$15 years of the GPS electron flux data, and tested on more than $1.5$ years of measurements. 10-fold cross validation (CV) yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes of flux. Evaluation on the test set yields high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide Space Weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment and surface charging analysis.

The present study examines the characteristics of the MJO events represented in the Energy Exascale Earth System Model version 1 (E3SMv1), DOE’s new Earth system model. The coupled E3SMv1 realistically simulates the eastward propagation of precipitation and Moist Static Energy (MSE) anomalies associated with the MJO. As in observation, horizontal moisture advection and longwave radiative feedback are found to be the dominant processes in E3SMv1 that lead to the eastward movement and maintenance of the MSE anomalies, respectively. Modulation of the diurnal cycle of precipitation in the Maritime Continent region by the MJO is also well represented in the model despite systematic biases in the magnitude and phase of the diurnal cycle. On the midlatitude impact of the MJO, E3SMv1 reasonably captures the pattern of the MJO teleconnection across the North Pacific and North America, with improvement in the performance in a high-resolution version, despite the magnitude being a bit weaker than the observed feature. About interannual variability of the MJO, the El Niño-Southern Oscillation (ENSO) modulation of the zonal extent of MJO’s eastward propagation, as well as associated changes in the mean state moisture gradient in the tropical west Pacific, is well reproduced in the model. However, MJO in E3SMv1 exhibits no sensitivity to the Quasi-Biennial Oscillation (QBO), with the MJO propagation characteristics being almost identical between easterly QBO and westerly QBO years. Processes that have been suggested as critical to MJO simulation are also examined by utilizing recently developed process-oriented diagnostics.

We present evidence of permeability enhancement from hydraulic stimulation experiments in fractured crystalline rock. A total of almost 10m3 was injected in two fractured intervals of a 300 m long borehole. Ground Penetrating Radar (GPR) measurements in the same borehole were carried out prior to and following the stimulation. The initial measurements revealed fractures in the vicinity of the borehole that could be traced up to distances of 50 meters away. The data measured post-stimulation were used in a difference-imaging approach to illuminate changes in the GPR reflections caused by the stimulations. The changes delineate the enhancement of a large and complex fracture network. These changes likely correspond to changes in local aperture, thus permeability. Our results indicate that borehole GPR yields unique information on subtle changes in hydraulic properties within a relatively large volume and provides a new perspective on the characterization and monitoring of deep geothermal reservoirs.

A time-based complex network analysis on the seismic activity along the coast of Chile was made. The coast of Chile was divided on windows of 300 km in Latitude, from Arica up to Los Angeles. The window was moved 100 km from north to south. We built a time-based earthquake complex network, on each window and we studied a directed and undirected complex network. Assuming a self-similar data network, for the directed network we found the value of the power-law characteristic exponent γ for the connectivity probability distribution. For the directed network, we analyzed the average path length, measured through steps and meters, of the data set. For each window, we obtained a similar relation between the value of γ and the average path length. The exponents and values were evaluated for the data set with the total number of seismic events with a depth greater than 200 km and of magnitude greater than Mw=3.0.

We design a new strategy to load-balance high-intensity sub-grid atmospheric physics calculations restricted to a small fraction of a global climate simulation’s domain. We show why the current parallel load balancing infrastructure of CESM and E3SM cannot efficiently handle this scenario at large core counts. As an example, we study an unusual configuration of the E3SM Multiscale Modeling Framework (MMF) that embeds a binary mixture of two separate cloud-resolving model grid structures that is attractive for low cloud feedback studies. Less than a third of the planet uses high-resolution (MMF-HR; sub-km horizontal grid spacing) relative to standard low-resolution (MMF-LR) cloud superparameterization elsewhere. To enable MMF runs with Multi-Domain CRMs, our load balancing theory predicts the most efficient computational scale as a function of the high-intensity work’s relative overhead and its fractional coverage. The scheme successfully maximizes model throughput and minimizes model cost relative to precursor infrastructure, effectively by devoting the vast majority of the processor pool to operate on the few high-intensity (and rate-limiting) HR grid columns. Two examples prove the concept, showing that minor artifacts can be introduced near the HR/LR CRM grid transition boundary on idealized aquaplanets, but are minimal in operationally relevant real-geography settings. As intended, within the high (low) resolution area, our Multi-Domain CRM simulations exhibit cloud fraction and shortwave reflection convergent to standard baseline tests that use globally homogenous MMF-LR and MMF-HR. We suggest this approach can open up a range of creative multi-resolution climate experiments without requiring unduly large allocations of computational resources.

Data assimilation (DA) is integrated with machine learning in order to perform entirely data-driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional numerical forecast model, the forecast error covariance matrix, and the tangent linear and adjoint models. It is shown how these RNNs can be initialized using DA methods to directly update the hidden/reservoir state with observations of the target system. The results indicate that these techniques can be applied to estimate the state of a system for the repeated initialization of short-term forecasts, even in the absence of a traditional numerical forecast model. Further, it is demonstrated how these integrated RNN-DA methods can scale to higher dimensions by applying domain localization and parallelization, providing a path for practical applications in NWP.

Modeling air-sea interactions during cold air outbreaks poses a major challenge because of the vast range of scales and physical processes involved. Using the Polar WRF model, we investigate the sensitivity of downstream air mass properties to (a) model resolution, (b) the sharpness of the marginal-ice zone (MIZ), and (c) the geometry of the sea ice edge. The resolved sharpness of the MIZ strongly affects peak heat fluxes and the atmospheric water cycle. For sharper MIZs, roll convection sets in closer to the sea ice edge, increasing both evaporation and precipitation. This yields an increased heat transfer into the atmosphere while the net effect on the atmospheric moisture budget is small. Consequently, higher atmospheric resolution increases the probability that a cold-air outbreak triggers deep convection in the ocean. The geometry of the sea ice edge can induce convergence or divergence zones that affect the air-sea exchange.

The air-sea exchanges of momentum which are often parameterized by the variation of drag coefficient (CD) and wind speed (un), is largely uncertain at typhoon-force extreme values of un. This paper investigated the relationship between CD and extreme values of un for five different heights using the eddy covariance method from two coastal observation towers during the landfall of Super Typhoon Maria. In particular, our observations include 10-min average un extended to high wind speed of 42.27 m s-1. The relationship between CD and un was parabolic from regression analysis, and the “roll-off” of CD and u* appeared at about 28 m s-1. From the bottom to top heights, the corresponding un of CD extreme were 9.95, 12.72, 22.32, 32.57 and 38.04 m s-1, respectively. The trend of negative tangential turbulent flux with un was found to be the main cause for the CD extreme drifting with heights.

As global mean sea level (GMSL) continues to rise, thresholds corresponding to coastal inundation impacts are exceeded more frequently. This paper aims to relate sea-level rise (SLR) observations and projections to their physical on-the-ground impacts. Using a large coastal city as an example, we show that in Sydney, Australia, frequencies of minor coastal inundation have increased from 1.6 to 7.8 days per year between 1914 and present day. We attribute over 80% of the observed coastal inundation events between 1970 and 2015 to the predominantly anthropogenic increases in GMSL. Further, we find that impact-producing coastal inundation will occur weekly by 2050 under high- and medium- emission/sea-level rise scenarios, and daily by 2100 under high emissions. The proportion of tide-only coastal inundation events (i.e. where no storm surge is required to exceed flood thresholds) will increase with SLR, such that most coastal inundation events, including those considered historically severe, will become a predictable consequence of SLR and astronomical tides. These findings are important for coastal managers as frequency, severity and predictability of inundation impacts can all now be related to the amount of SLR (e.g. a planning allowance or SLR projection). By incorporating known historical inundation events, this allows contextualization, visualization and localization of global SLR and the changing nature of future coastal inundation risk.