There is no consensus on the physical mechanisms controlling the scale at which convective activity organizes near the Equator, where the Coriolis parameter is small. High resolution cloud-permitting simulations of non-rotating convection show the emergence of a dominant length scale, which has been referred to as convective self-aggregation. Furthermore, simulations in an elongated domain of size 12228km x 192km with a 3km horizontal resolution equilibrate to a wave-like pattern in the elongated direction, where the cluster size becomes independent of the domain size. These recent findings suggest that the size of convective aggregation may be regulated by physical mechanisms, rather than artifacts of the model configuration, and thus within the reach of physical understanding. We introduce a diagnostic framework relating the evolution of the length scale of convective aggregation to the net radiative heating, the surface enthalpy flux, and horizontal energy transport. We evaluate these length scale tendencies of convective aggregation in twenty high-resolution cloud-permitting simulations of radiative-convective equilibrium. While both radiative fluxes contribute to convective aggregation, the net longwave radiative flux operates at large scales (1000-5000 km) and stretches the size of moist and dry regions, while the net shortwave flux operates at smaller scales (500-2000 km) and shrinks it. The surface flux length scale tendency is dominated by convective gustiness, which acts to aggregate convective activity at smaller scales (500-3000 km). We further investigate the scale-by-scale radiative tendencies in a suite of nine mechanism denial experiments, in which different aspects of cloud radiation are homogenized or removed across the horizontal domain, and find that liquid and ice cloud radiation can individually aggregate convection. However, only ice cloud radiation can drive the convective cluster to scales exceeding 5000 km, because of the high optical thickness of ice, and the increase in coherence between water vapor and deep convection with horizontal scale. The framework presented here focuses on the length scale tendencies rather than a static aggregated state, which is a step towards diagnosing clustering feedbacks in the real world. Overall, our work underscores the need to observe and simulate surface fluxes, radiative and advective fluxes across the 1km-1000km range of scales to better understand the characteristics of turbulent moist convection.

Hydrological distributed modeling is a key point for a comprehensive assessment of the feedback between the dynamics of the hydrological cycle, climate conditions, and land use. Such modeling results are markedly relevant in the fields of water resources management. Here TopModel (TOPography based hydrological MODEL) is employed for the hydrological modeling of an area in the Middle Magdalena Valley (MMV), a tropical basin located in Colombia. This study is located in the intertropical convergence zone and is characterized by special meteorological conditions, with fast water fluxes over the year. It has been subject to significant land use changes, as a result of intense economic activities, i.e., agriculture, energy and oil & gas production. The model employees a record of 12 years of: • Daily precipitation database from observed gauges • Daily evapotranspiration database from temperature data • Streamflow database as observed data from calibration Calibration is performed using data from 2000 to 2008, and validation is performed with data from 2009 to 2012. The Nash-Sutcliffe coefficient is used to assess the robustness of our calibration process.(values of this metric being 0.62 and 0.53, respectively for model calibration and validation). The results reveal high water storage capacity in the soil, and a marked subsurface runoff, consistent with the characteristics of the soil types in the regions. The calibrated model provides relevant indications about recharge in the region, which is important to quantify the interaction between surface water and groundwater, especially during the dry season, which is more relevant in climate-change and climate-variability scenarios.

We analysed crystal-preferred orientation of c-axis and microstructure data from the Alpine ice core KCC at an unprecedented resolution and coverage of any Alpine ice core. We find that an anisotropic single-maximum fabric develops as early as 25 m depth in firn under vertical compression and strengthens under simple shear conditions towards the bedrock at 72 m depth. The analysis of continuously measured intervals with subsequent thin section samples from several depths of the ice core reveals a high spatial variability in the crystal orientation and crystal size on the 10 cm-scale as well as within a few centimeters. We quantify the variability and investigate the possible causes and links to other microstructural properties. Our findings support the hypothesis that the observed variability is a consequence of strain localisation on small spatial scales with influence on fabric and microstructure. From a methodological perspective, the results of this study lead us to challenge whether single thin sections from ice cores provide representative parameters for their depth to be used to infer the fabric development in a glacier on the large scale. Previously proposed uncertainty estimates for fabric and grain size parameters do not capture the observed variability. This might therefore demand a new scale-sensitive statistical approach.

In this study, we examine the nonlinear interdependence between the parameters of the solar wind influencing geomagnetic activity (proton density and solar wind dynamic pressure, IMF Bz and solar wind dynamic pressure) and geomagnetic activity (AE and SYM-H) during pre-storm, storm and post-storm of intense, major, moderate and minor geomagnetic storms. Nonlinear analytical tools comprising cross recurrence plot (CRP) and Recurrence Rate (RR) was used to capture the features of nonlinear interdependence in the time series data of the solar wind parameter (solar wind dynamic pressure, IMF Bz and proton density) and geomagnetic indices (AE and SYM-H). The pearson correlation coefficient was also used to reveal the features of linear dependence between the parameters of the solar wind during the different categories of geomagnetic storms. Our result shows that during the storm, the CRPs of the solar wind parameters investigated pictured a strong deterministic structure of CRP which is an indication of strong interdependence. In particular, the results of our analysis showed that the solar wind dynamic pressure and proton density depicted very strong interdependence features. On the other hand, during pre-storm and post-storm, the CRP of solar wind dynamic pressure in relation to IMF Bz and proton density pictured a rare deterministic structure signifying weak interdependence. Furthermore, the values of RR were very high (an indication of strong interdependence between the parameters of the solar wind) during the storm while at pre-storm and post-storm, the values of RR declined significantly in most of the periods which further strengthened the evidence of weak interdependence in the parameters of solar wind. The CRP and RR of the geomagnetic indices (AE and SYM-H) reveals no significant difference between pre-storm, storm and post-storm of the different categories of geomagnetic storms. However, strong interdependence between the geomagnetic indices were observed in most of the periods investigated. Finally, the pearson correlation coefficient reveals high values of linear dependence between the solar wind parameters without any significant difference between pre-storm, storm and post-storm periods during intense, major, moderate and minor geomagnetic storms.

Air-sea flux variability has contributions from both ocean and atmosphere at different spatio-temporal scales. Atmospheric synoptic scales and the air-sea turbulent heat flux that they drive are well represented in climate models, but ocean mesoscales and their associated variability are often not well resolved due to non-eddy-resolving spatial resolutions of current climate models. We deploy a physics-based stochastic subgrid-scale parameterization for ocean density, that reinforces the lateral density variations due to oceanic eddies, and examine its effect on air-sea heat flux variability in a comprehensive coupled climate model. The stochastic parameterization substantially modifies sea surface temperature (SST) and latent heat flux (LHF) variability and their correlations, primarily at scales near the resolution of the ocean model grid. Changes in the SST-LHF anomaly correlations indicate that the ocean-intrinsic component of the air-sea heat flux variability improves with respect to the satellite observational product, especially in western boundary current extensions.

Extraction of critical hydrologic features from high-resolution topographic data is challenging using existing grid coarsening approaches, such as surface flow path, river network, and slope, which limits the application of hydrological models. In this research, the influence of various grid coarsening techniques on the prediction outcomes was measured by a numerical experiment based on the integrated hydrological model ParFlow-Common Land Model (ParFlow.CLM). Three grid coarsening methods (Nearest Neighbor Coarsening, Majority Coarsening, Hydrography-Driven Coarsening) were applied to simulate evapotranspiration(E), soil temperature(ST), streamflow, soil moisture(SM) and latent(LE) heat fluxes in central China’s Sixi Valley, a classic example of a karstic basin. As a result, the three grid coarsening methods perform uniform in simulating latent heat fluxes and soil temperature. However, their ability to predict soil moisture surface flow and evapotranspiration are more diverging. The hydrography-driven coarsening extracts significantly more accurate valleys, rivers network, and slopes closer to the actual terrain than existing coarsening strategies. Slopes derived from hydrography-driven coarsening methods can be used to predict more accurately the top soil moisture, evapotranspiration, and streamflow dynamics processes. This study stresses that a hydrography-driven coarsening strategy is advocated for all those cases in which topographic slope extracted using a coarse-grid digital elevation model is an important influence on the ParFlow.CLM simulation of essential hydrographic features.

Under anthropogenic warming, future changes to climate variability beyond specific modes such as the El Niño-Southern Oscillation (ENSO) have not been well-characterized. In the Community Earth System Model version 2 Large Ensemble (CESM2-LE) climate model, the future change to sea surface temperature (SST) variability is spatially heterogeneous. We examined these projected changes (between 1960-2000 and 2060-2100) in the North Pacific using a local linear stochastic-deterministic model, which allowed us to quantify the effect of changes to three drivers on SST variability: ocean “memory” (the SST damping timescale), ENSO teleconnections, and stochastic noise forcing. The ocean memory declines in most areas, but lengthens in the central North Pacific. This change is primarily due to changes in air-sea feedbacks and ocean damping, with the shallowing mixed layer depth playing a secondary role. An eastward shift of the ENSO teleconnection pattern is primarily responsible for the pattern of SST variance change.

We develop a Hamiltonian Monte Carlo (HMC) sampler which solves a multi-parameter elastic full-waveform inversion (FWI) in a probabilistic setting for the first time. This gives novel access to the full posterior distribution for this type of highly non-linear inverse problem. Typically, FWI has focused on using gradient descent methods with proper regularization to iteratively update models to a minimum misfit value. Non-uniqueness and uncertainties are mostly in this approach. Bayesian inversions offer an alternative by assigning a probability to each model in model space given some data and prior constraints. The drawback is the need to evaluate a very large number of models. Random walks from Markov chains counter this effect by only exploring regions of model space where probability is significant. HMC method additionally incorporates gradient information, i.e. local structure, typically available for numerical waveform tomography experiments. So far, HMC has only been implented for acoustic FWI. We implement HMC for multiple 2D elastic FWI set-ups. Using parallelized wave propagation code, wavefields and kernels are computed on an regular numerical grid and projected onto basis functions. These gradients are subsequently used to explore the posterior space of different target models using HMC. The free parameters in these experiments are P and S velocity, and density. Although simulating Hamiltonian dynamics in the resulting phase space is approximated numerically, the results of the Markov chain are nevertheless very insightful. No prior tuning of kernels, data or model space is required, under the constraint that the sampler is properly tuned. After a burn in phase during which the mass matrix is iteratively optimized, the Markov chain is run on multiple nodes. After approximately 100,000 samples (combined from all nodes) the Markov chain mixes well. The resulting samples give access to the full posterior distribution, including the mean and maximum-likelihood models, conditional probabilities, inter-parameter correlations and marginal distributions.

Land surface/Earth System models depend upon accurate simulation of evapotranspiration (ET) to avoid excessive biases in simulated energy, water, and carbon cycles. The Canadian Land Surface Scheme including biogeochemical Cycles (CLASSIC), the land surface scheme of the Canadian Earth System Model (CanESM) shows reasonable ET fluxes globally, but CLASSIC’s partitioning into evaporation (E) and transpiration (T) can be improved. Specifically, CLASSIC exhibited a high soil evaporation (Es) bias in sparsely vegetated areas during wet periods, which can deplete soil water and decrease photosynthesis and T later in the year. A dry surface layer (DSL) parameterization was implemented to address biases in Es through an increased surface resistance to water vapour and heat fluxes. In arid/semi-arid regions, the DSL decreased Es, leading to improved seasonality of ET and increased gross primary productivity (GPP) due to an increase in soil moisture. The DSL simulations significantly (t-test, p<0.01) increased T/ET from 0.25 in baseline CLASSIC to 0.30 in the DSL simulations. T/ET was further increased to 0.41 (p<0.01), comparable to the CMIP5 model mean, by allowing T to occur from the dry canopy fraction while water evaporates from the wet fraction. This mainly affected densely vegetated areas, where T and ET increased significantly (p<0.01) and canopy E was reduced (p<0.01). In seasonally dry tropical forests, higher T and ET reduced GPP. Despite increases in arid/semi-arid regions, the reduced GPP in tropical forests resulted in ∼1.6% lower global GPP (p=0.018) than baseline CLASSIC. Including these modifications in CanESM might reduce biases in climate.

To improve surface mass balance (SMB) estimates for the Greenland Ice Sheet (GrIS), we develop a spatially and temporally high resolution polar regional climate model combining the Japan Meteorological Agency Non-Hydrostatic atmospheric Model and the Snow Metamorphism and Albedo Process model (NHM–SMAP). At present, the horizontal resolution of the model is set to be 5 km with the temporal interval of 1-hour. The model forced by the Japanese 55year Reanalysis (JRA-55) has been evaluated in the GrIS during the 2011–2014 mass balance years using in-situ surface meteorological and stake measurement data [1]. Results show that the model reproduced the measured features of the GrIS climate, diurnal variations, and even a meso-scale strong wind event. In particular, it reproduces the GrIS surface melt area extent well. Recently, near real-time calculations with the model are conducted operationally. Here we introduce the concept and framework of the model, then present performance of the model from various aspects. In addition, some new results from the operational near-real time calculations will be shown and discussed. Reference [1] Niwano et al. (2018) NHM-SMAP: Spatially and temporally high resolution non-hydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet. The Cryosphere, 12, 635-655, doi:10.5194/tc-12-635-2018.

To study the contributions of cusp outflow coming through the lobes and nightside auroral outflow to the O+ in the plasma sheet, we performed a statistical study of tailward streaming O+ in the lobes, the plasma sheet boundary layer (PSBL) and the plasma sheet (PS), using MMS/HPCA data. Similar spatial distributions demonstrate the entry of cusp-origin O+ from the lobes to the plasma sheet through the PSBL. There is an energy dependence in the lobe O+ spatial distribution, with low-energy O+ streaming near the center in YGSM while high energy (1-3 keV) O+ streams near the flanks. Low energy (< 100 eV) O+ from the nightside auroral oval can be identified in the near-Earth PSBL/PS with high-density (> 0.03 cm-3), and energetic (> 3 keV) streaming O+ with similar density (~0.02 cm-3) is seen further out on the duskside of the PSBL/PS. The rest of the nightside auroral O+ in the PSBL is mixed with O+ coming in from the lobe, and difficult to distinguish. We estimated the inflow and outflow of ions in the plasma sheet between 7-17 RE, using data extracted from previous studies and this work. Comparisons between the estimated fluence suggest that the majority of near-Earth plasma sheet H+ are from cusps and Earthward convection from the distant tail. The O+ in the same region, on the other hand, has a mixed source, with auroral outflow giving the highest contribution.

The Atlantic Meridional Overturning Circulation (AMOC) is one of the most essential mechanisms influencing our climate system. By comparing constant depth (z-AMOC) and density (ρ-AMOC) frameworks under pre-industrial, historical and abrupt 4xCO2 scenarios we analyze how the circulation mean state and variability differ amongst them. Water mass transformations are also assessed as a matter of analyzing surface-induced and interior-mixing-induced transformations. As expected, both location and strength of AMOC maxima are deeply affected by the framework choice, with the AMOC reaching a maximum transport of 21 Sv at around 35°N under constant depth coordinates, as opposed to ∼25 Sv at 55°N when diagnosed from density surfaces for both pre-industrial and historical climate. When quadrupling the CO2, both frameworks exhibit an abrupt AMOC weakening followed by a steady recovery to maximum values of 10-15 Sv. The z-AMOC maxima timeseries correlates more with those at 26°N (r ∼0.7) than with the ρ-AMOC maxima (r ∼-0.3), due to the flatter isopycnals in the z framework even in the subpolar North Atlantic, where isopycnals are, in fact, steeper. Based on this discrepancy, we argue that the density framework is more coherent to the physics of this circulation by directly incorporating water mass transformations and their density structure. We suggest that more analysis across timescales and under different conditions must be performed with density surface outputs being provided by as many models as possible, to enable a more comprehensive analysis of these two frameworks and their applications.

Gravity Recovery And Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) global monthly measurements of Earth’s gravity field have led to significant advances in the quantification of mass transfer on Earth. Yet, a long temporal gap between missions prevents interpretation of long-term mass variations. Moreover, instrumental and processing errors translate into large non-physical stripes polluting geophysical signals. We use Multichannel Singular Spectrum Analysis (M-SSA) to overcome both issues by exploiting spatio-temporal information of multiple Level-2 GRACE/GRACE-FO solutions. We statistically replace missing data and outliers using iterative M-SSA on Equivalent Water Height (EWH) time series processed by CSR, GFZ, GRAZ, and JPL to form a combined evenly spaced solution. Then, M-SSA is applied to retrieve common signals between each EWH time series and its neighbours to reduce residual spatially uncorrelated noise. We develop a complementary filter, based on the residual noise between fully processed data and a parametric fit to observations, to further reduce persisting stripes. Comparing GRACE/GRACE-FO M-SSA solution with SLR low-degree Earth’s gravity field and hydrological model demonstrates its ability to statistically fill missing observations. Our solution reaches a noise level comparable to mass concentration (mascon) solutions over oceans, without requiring \textit{a priori} information or regularisation. While short-wavelength signals are hampered by filtering of spherical harmonics solutions or challenging to capture using mascon solutions, we show that our technique efficiently recovers localized mass variations using well-documented mass transfers associated with reservoir impoundments.

We will present signal processing techniques based on shape analysis of the time-frequency signatures of chorus elements in the Van Allen Radiation Belts. Specifically, we will employ the Radon transform and the Distance Transform, which are well-known for isolating local shapes and patterns in the image processing literature, to achieve automated detection of the spectral morphology of chorus elements. In particular, we will introduce “shrink-wrapping” techniques that quantify the salient features defining the microstructure of chorus elements. We will present preliminary results based on case studies of the EMFISIS data from the Van Allen Radiation Belts mission.

A document by Andrew J. Pershing. Click on the document to view its contents.

Climate change has been a common interest and the forefront of crucial political discussion and decision-making for many years. Shallow clouds play a significant role in understanding the Earth’s climate but they are challenging to interpret and represent in a climate model. By classifying these cloud structures there is a better possibility of understanding the physical structures of the clouds which would improve the climate model generation resulting in a better prediction of climate change or forecasting weather update. Clouds organise in many forms which makes it challenging to build traditional rule-based algorithms to separate cloud features. In this paper classification of cloud organization patterns was performed using a new scaled-up version of Convolutional Neural Network (CNN) named as EfficientNet as the encoder and UNet as decoder where they worked as feature extractor and reconstructor of fine grained feature map and was used as a classifier which will help experts to understand how clouds will shape the future climate. By using a segmentation model in a classification task it was shown that with a good encoder alongside UNet it is possible to obtain good performance from this dataset. Dice coefficient has been used for the final evaluation metric which gave the score of 66.26% and 66.02% for public and private (test set) leaderboard on Kaggle competition respectively.

How do formal and informal land use changes have the potential to create unique, and potentially synergistic, risks to communities? Traditionally environmental geochemists privilege certain analytical approaches to evaluate the risk in a system, but when using a participatory approach, the emphasis is instead placed on using the “just right” tools co-discovered with members of the community. In this study we partnered with the LEAD Agency, an environmental advocacy group with a long history of co-designing research agendas to address community concerns to study trace legacy metals in floodplain soils at the Tar Creek Superfund Site in Ottawa County, Oklahoma. At Tar Creek, large mine waste (chat) piles and acid mine seepage have contaminated surrounding communities with zinc, lead, and cadmium. Heavy metal contamination of floodplains at mining sites like Tar Creek involve a complex set of biogeochemical interactions that are controlled by land use patterns (e.g. reworking chat piles and downstream dams), transport pathways, and changing climate making it difficult to prioritize interventions aimed at reducing exposure. Using a participatory research approach, this study integrates (1) community initiated geochemical investigation of wind transportable Pb, (2) monitoring of nutrient loading to assess potential for eutrophication, which would increase metal transport and mobility in Tar Creek, and (3) examines the connections between social and political issues at Tar Creek and how these affect both scientific research and what remediation strategies are tenable. Our action based research aims to support our community partners in their goals:(1) to establish the Rights of Tar Creek through LEAD’s Clean Water Protection Ordinance, which would establish the right to clean water and legally recognize the rights of Tar Creek to exist, regenerate, and flourish, and (2) to expand the EPA’s definition of OU5, which would increase funding for remediation in Ottawa County.

We compute stress drops from P and S phase spectra for 534 earthquakes in the source region of the 2014 MW 8.1 Iquique megathrust earthquake in the northern Chilean subduction zone. An empirical Green’s function based method is applied to suitable event pairs selected by template matching of eight years of continuous waveform data. We evaluate the parameters involved in the stress drop estimation, consider the effect of the local velocity structure and apply an empirical linear relation between P and S phase related geometry factors (k values). Data redundancy produced by multiple EGFs and the combination of P and S phase spectra leads to a substantial reduction of uncertainty and robust stress drop estimates. The resulting stress drop values show a well-defined log-normal distribution with a median value of 4.36 MPa; most values range between 0.1-100 MPa. There is no evidence for systematic large scale lateral variations of stress drop. A detailed analysis reveals several regions of increased median stress drop, an increase with distance to the interface, but no consistent increase with depth. This suggests that fault regime and fault strength have a stronger impact on the stress drop behavior than absolute stresses. Interestingly, we find a weak time-dependence of the median stress drop, with an increase immediately before the April 1, 2014 MW 8.1 Iquique mainshock, a continuous reduction thereafter and a subsequent recovery to average values. Additionally, the data set indicates a relatively strong dependence of stress drop on magnitude which extends over the entire analyzed magnitude range.