Widespread valley networks (VNs) on Mars and other evidence point to an early warm and wet climate. However, ongoing debates still exist about VN’s formation processes and associated climatic conditions. The power law relationship between basin length and area (Hack’s Law) can be diagnostic of different fluvial processes related to climatic conditions. Past studies of Hack’s Law on Mars at local sites have produced inconclusive results.  Here we used a parameter-free method to delineate watersheds globally on Mars based on mapped VNs and extracted their Hack’s Law exponent (h). The majority of h values on Mars are similar to those in arid areas on Earth, suggesting similar runoff processes and arid conditions for VN formation on early Mars. Statistical analyses show that the spatial distribution of h on Mars is not random, but with a few clustered high and low values, likely controlled by local conditions (e.g., regional topographic slope).

Plant phenology regulates the carbon cycle and land-atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950-2014, 1981-2014, and 2015-2099, thermal-based indicators estimate spring advances of -0.7±0.2, -1.4±0.4, and -2.4±0.7 days/decade, while LAI-based indicators estimate -0.4±0.3, -0.1±0.3, and -1±1.1 days/decade. Thereby, LAI-based indicators exhibit later spring and weaker trends toward earlier onset, leading to index-related uncertainty being as large or larger than model uncertainty for a given index. Reconciling index-related discrepancies is therefore critical for understanding future changes in spring onset.

Climate change is both a physical and social phenomenon in which individual understandings are contextualized within broader considerations: individuals are not ‘blank slates’ receiving information about climate change, but that information is always and inevitably filtered through values and worldviews. Personal experience, local knowledge, and social-learning influence climate risk perception and vary substantially among countries and regions. Likewise, they differently affect individuals and social groups at the regional and local scale, among whom exposures, attitudes, and capacities to manage risks vary greatly. A climate storyline approach is hence well-suited to study human observations, compound climate risks, and inform and conceptualize human–water systems interactions. Narrative storylines are used as input drivers to climate models, to represent different development pathways, which are usually characterized and applied at national and sub-national scales. Storylines aim to provide new social scenarios that address local human cognition uncertainties and improve human behavior modelling and robustness when addressing decision-making processes. Climate risks and hazards understanding can be communicated by presenting the experiences or a sequence of events, facts, and observations that are plausible and potentially critical for the system under study. Methods guiding storytelling are usually focused on conducting interviews with stakeholders, carrying out collective workshops, developing appropriate focal questions, and iterating between model results and key stakeholders. Therefore, can other data collection tools be used to reduce uncertainty in physical aspects of climate change from individuals’ local experience and perception? This contribution presents a triple-loop survey to detail the core elements of farmers’ perception and behavior when addressing climate change risk. We collect first-hand observations from northern Italian farmers about how climate change affects their activity and how extreme events are conditioning their adaptation capacity. Emphasis is placed on understanding the driving factors (risk awareness, perceived impacts, and adaptation measures and barriers) involved in the physically self-consistent past events and the plausibility of those factors. Moreover, we want to test if these factors can provide relevant implications for appropriately modelling storylines in decision-making processes. Tentative results can be useful to discuss the methodological framework of storylines building and narratives modelling, and at which point surveys can be an alternative and complementary way of dealing with deep uncertainty within climate risk management and social scenarios modelling.

Accurate prediction of precipitation intensity is of crucial importance for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is sub-grid scale cloud structure and organization, which affects precipitation intensity and stochasticity at the grid scale. Here we show, using storm-resolving climate simulations and machine learning, that by implicitly learning sub-grid organization, we can accurately predict precipitation variability and stochasticity with a low dimensional set of variables. Using a neural network to parameterize coarse-grained precipitation, we find mean precipitation is predictable from large scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ∼ 0.4) and underestimates precipitation extremes. Performance is significantly improved when the network is informed by our novel organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ∼ 0.95). The organization metric is implicitly learned by training the algorithm on high-resolution precipitable water, encoding organization degree and humidity amount at the subgrid-scale. The organization metric shows large hysteresis, emphasizing the role of memory created by sub-grid scale structures. We demonstrate this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing sub-grid scale convective organization in climate models to better project future changes in the water cycle and extremes.

Climate change is arguably the most severe and complex challenge facing today’s society, a cross-cutting issue affecting many sectors and connected to other global challenges, such as ensuring sustainable water management and food security. Agricultural systems are adversely influenced by climate change through increased water stress, change in run-off patterns, seasonality fluctuation, and temperature variations. Farmers are, hence, a valuable source of first-hand observations of climate change as they may provide a deeper understanding of their manifestation, relevance, and effects. Social and behavioural sciences have investigated the influence of farmers’ experiences in increasing climate change adaptation capability and improving decision-making processes at the system level. The conclusion is that local perceptions provide sufficient baseline information for understanding individual and collective exposure to climate risks, an essential element for effective policy formulation and implementation. Traditional management approaches based on simple, linear growth optimization strategies, overseen by command-and-control policies, have proven inadequate for effective adaptation to climate change. Conversely, accurate bottom-up approaches focused on social learning can complement the system transformation by building collaborative problem solving among individuals, stakeholders, and decision-makers. In this context, deepening social perception becomes fundamental for two main reasons: i) it is a key component of the socio-political context, and ii) it is an essential step for behaviour transformation and attitude change. In this line, associative processing methods, such as interviews and surveys, have been discussed for their ability to monitor the nature, extent, significance, and influence of personal experience on climate change adaptation. Also, modelling techniques have been recognized in social sciences as effective mechanisms to simulate the social influence in decision-making processes. System dynamics (e.g., causal loop diagrams, CLD) and Agent-Based Models (ABM) can include feedback between social and physical environments, define individuals’ and stakeholders’ narratives, and map the social network with agents’ interactions. This proposal aims at testing how qualitative data can enable policy-makers and managers to understand and re-think water management and climate change policies at the local level, which is essential to address agricultural risks. From a system dynamics approach, we examine how ABMs can most effectively integrate behavioural data collected from semi-structured interviews and surveys to increase robustness in decision-making processes while attending to farmers’ behaviour on climate change adaptation. We surveyed 460 farmers and semi-structured interviews with 13 irrigation consortiums from northern Italy to deepen a triple loop analysis on climate change awareness, perceived impacts, and adaptive capacity.

The discovery of slow earthquakes illuminates the existence of a strange depth dependence of seismogenesis, which contradicts the common understanding of smooth brittle/seismic-ductile/aseismic transition as going deeper into the Earth’s surface layers. However, within the transitional layer on plate interfaces, observations have clarified slip velocities of slow earthquakes changing from those slower to faster with increasing depth, as described by the “seismogenic inversion layer.” We propose a new mechanical model that can consistently explain the classic brittle-ductile transition and this inversion phenomenon by considering the heterogeneous fault zone composed of brittle blocks in the ductile matrix. The key mechanism is the interplay between the volumetric fraction of brittle blocks and the viscosity of the surrounding plastically deformed matrix, where the former and the latter decrease with increasing temperature. This model is extended to shallow-slow earthquakes. Our results open a new pathway to infer the deformation mechanisms underlying slow earthquakes.

Mars’ polar layered deposits record critical information about its climate history. Here, I numerically model formation of alternating layers of CO2 and H2O ice in Mars’ south polar Massive CO2 Ice Deposit to reconstruct its H2O ice depositional history over the past 510 thousand years. Statistical analyses of ~10^9 model runs favor a best-fit historical H2O ice deposition function that exponentially decreases with obliquity, with ~1, 0.1, and 0.01 mm yr^-1 rates for 20, 24, and 28 deg. obliquity, respectively. Recovery of a south polar H2O-ice-deposition-versus-obliquity function is novel and important for elucidating Mars’ global water cycle; previous south polar layer analyses were limited to calculation of net average deposition rates over millions of years.

Fourier spectra are powerful tools to analyse the scale behavior of turbulent flows. While such spectra are mathematically based on regular periodic data, some state-of-the-art ocean and climate models use unstructured triangular meshes. Observational data is often also available only in an unstructured fashion. In this study, scale analysis specifically for the output of models with triangular meshes is discussed and the representable wavenumbers for Fourier analysis are derived. Aside from using different interpolation methods and oversampling prior to the computation of Fourier spectra, we also consider an alternative scale analysis based on the Walsh--Rademacher basis, i.e. indicator functions. It does not require interpolation and can be extended to general unstructured meshes. A third approach based on smoothing filters which focus on grid scales is also discussed. We compare these methods in the context of kinetic energy and dissipation power of a turbulent channel flow simulated with the sea ice-ocean model FESOM2. One simulation uses a classical viscous closure, another a new backscatter closure. The latter is dissipative on small scales, but anti-dissipative on large scales leading to more realistic flow representation. All three methods clearly highlight the differences between the simulations as concerns the distribution of dissipation power and kinetic energy over scales. However, the analysis based on Fourier transformation is highly sensitive to the interpolation method in case of dissipation power, potentially leading to inaccurate representations of dissipation at different scales. This highlights the necessity to be cautious when choosing a scale analysis method on unstructured grids.

In this work we describe the compilation and homogenization of an extensive dataset of aerosol iodine field observations in the period between 1963 and 2018 and we discuss the spatial and temporal dependences of total iodine in bulk aerosol by comparing the observations with CAM-Chem model simulations. Total iodine in aerosol shows a distinct latitudinal dependence, with an enhancement towards the northern hemisphere (NH) tropics and lower values towards the poles. This behavior, which has been predicted by atmospheric models to depend on the global distribution of the main oceanic iodine source (which in turn depends on the reaction of surface ozone with aqueous iodide on the sea water-air interface, generating gas-phase I2 and HOI), is confirmed here by field observations for the first time. Longitudinally, there is some indication of a wave-one profile in the Tropics, which peaks in the Atlantic and shows a minimum in the Pacific, following the wave-one longitudinal variation of tropical tropospheric ozone. New data from Antarctica show that the south polar seasonal variation of iodine in aerosol mirrors that observed previously in the Arctic, with two equinoctial maxima and the dominant maximum occurring in spring. While no clear seasonal variability is observed in NH middle latitudes, there is an indication of different seasonal cycles in the NH tropical Atlantic and Pacific. A weak positive long-term trend is observed in the tropical annual averages, which is consistent with an enhancement of the anthropogenic ozone-driven global oceanic source of iodine over the last 50 years.

The optical and VHF instrumentation on the FORTE satellite is used to document the combined phenomenology evolution of a lightning “megaflash” - mesoscale lightning that propagates laterally over exceptional distances. We identify a FORTE flash whose maximum extent was 82 km and inferred length over multiple distinct branches exceeded 100 km. This flash lasted 1.2 s and produced 250 optical and 591 RF events. We find that the channel development mapped by FORTE’s pixelated lightning imager (LLS) occurred at a typical speed of 2.6x10^5 m/s and was accompanied by sustained periods VHF emission that could individually exceed 100 ms in duration. The impulsive IC events generated by the flash indicate that this development occurred at altitudes between 3 and 8 km. Four +CG strokes were identified in the VHF waveform data that are responsible for two of the three highly-radiant LLS groups (the third radiant group came from a possible -CG while 2 of the +CGs were not as optically bright as the others). These strokes occurred at different locations throughout the flash footprint with the most distant strokes separated by approximately 50 km. These space-based observations match previous observations of megaflashes from space as well as ground-based measurements of slow negative leader development during “spider” lightning, suggesting that FORTE is sensing the same phenomena.

As climate-driven risks for the world’s coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change and informed coastal management choices. Artificial Intelligence, especially deep learning, is a powerful technology that has been rapidly evolving over the last couple of decades and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of deep learning models were tested, one set relying on localized modelling outputs or localized data sources and one set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing. The second set of reduced-dependency models provides regression values greater than 0.84 and 0.76 for training and testing. Both model types require a running time of the order of minutes, compared to the several hours of running times of the hydrodynamic models. Our results highlight the potential of deep learning and statistical models for coastal applications.

Amazon rainforest has been subject to intensive deforestation in the last decades, for example, illegal logging and creating pasture areas. A characteristic pattern of deforestation seen from space is the “fishbone” shape, which usually appears near roads, rivers and its tributaries. Indeed, others, more subtle, still need to be identified. These fishbone images are spatiotemporal patterns that need to be more explored with feature extraction methods. In computer vision, morphological features such as flatness, compactness, circularity, perimeter, area, and centroid are well-known to characterize the appearance of an object. In this work, we aim to characterize the shapes of deforestation in its early stages and its evolution in time, particularly in the Amazon rainforest. Thus, we propose to analyze satellite images of these regions to crop and segment by using shape features.

Identifying subsurface contamination is challenging as the sources are not directly perceivable. Aquifer contamination only gets noticed when it is measured in one of the observation wells. As remediation of the contaminated sites is expensive and time-consuming, it is essential to locate sources of contamination for efficient remediation design and water resources management. Simulation-optimization approach is popularly used for identification of contaminant sources. However, the numerous runs required for the simulation model by utilizing the optimization algorithm makes this approach computationally expensive. Alternatively, the simulation model can be substituted by a surrogate model which can significantly reduce the computational cost. In this study, a deep neural network (DNN) based surrogate model is proposed for simulating the transport of a reactive contaminant Tritium in a hypothetical aquifer. The DNN is trained by considering injection rates at possible source locations as inputs and concentration at observation wells simulated by meshfree Radial Point Collocation Method (RPCM) as output. RPCM efficiently handles instabilities associated with advection and reaction dominant problems in comparison to grid/mesh-based methods. The backpropagation approach is used to optimize the weights and biases of the DNN using adaptive moment estimation (ADAM) as an optimizer. The performance evaluation of the surrogate model yields Mean Squared Error (MSE) close to zero and correlation coefficient (R2) of 0.99. An inverse model is developed by linking the DNN surrogate model and Particle Swarm Optimizer (PSO). The application of the inverse model show that the DNN-PSO model can predict the injection rates at possible source locations accurately.

Remote sensing approaches based on VIS-NIR spectroscopy can be used for getting near real-time information about soil fertility. However, the main challenge limiting the application of spectroscopy in soil fertility evaluation is finding suitable data pre-processing and calibration strategies. We have compared various pre-processing techniques using the reflectance spectra obtained from AVIRIS-NG hyperspectral images, for quantification of organic carbon (OC), available phosphorus (P) and available potassium (K) in the surface soils of Surendranagar area (Western parts of India) and Raichur (Southern parts of India). Surface (0 - 0.15 m) soil samples were collected from these two areas synchronously with the dates of the AVIRIS-NG campaign. The soil samples were air dried, sieved to <2 mm, and analyzed for OC, P, and K using standard methods. The AVIRIS spectra (spectral range of 380-2500 nm with an interval of 5 nm) corresponding to soil sampling points were extracted. The pre-processing steps were used in the order: Continuum Removal (Yes/No), Moving Window Abstraction (Yes/No), No transformation or Euclidean Normalization or Standard Normal Variate (SNV), No transformation or Savitsky-Golay (SG) first-order smoothing, and No transformation or first derivative OR second derivative. We have used the partial least squares regression (PLSR) to calibrate the model from pre-processed spectra. The PLSR with Continuum Removal, SNV, SG first-order smoothing, and first derivative was selected as the best algorithm for estimating soil properties from the Western parts of India, and the corresponding R2 were 0.77 for OC, 0.79 for P and 0.83 for K (RMSE <0.3 for all the parameters). The PLSR with Moving Window Abstraction, SG first-order smoothing, and second derivative were selected as the best algorithm for estimating soil properties from the Southern parts of India, and the corresponding R2 were 0.54 for OC, 0.49 for P and 0.56 for K (RMSE <0.3 for all the parameters). These results suggest that the optimization of AVIRIS spectra using various pre-processing techniques and modeling approaches is required for rapid and non-destructive assessment and monitoring of soil health for precision agriculture.

Polygonal Fault Systems (PFS) are an enigmatic class of small non-tectonic extensional faults. PFS are predominantly hosted in fine-grained sedimentary tiers and are prevalent along many continental margin basins. The genesis of PFS is widely debated and little is known about the timeframe for polygonal fault growth. We present the first measurements of throw rates for polygonal faults by measuring the vertical offset of seven age-calibrated horizons mapped using three-dimensional seismic reflection data from the Norwegian Sea. Individual polygonal faults exhibit a range of throw rate profiles through time, ranging from near linear to singly or multiply stepped. The stepped profiles have a range of short-term throw rates ranging from 0 to 18 m/Ma. Time-averaged throw rates of 180 polygonal faults over the entire 2.61-0 Ma interval are normally distributed and range between 1.4-10.9 m/Ma. We convert our PFS throw rates to displacement rates and compare these to published displacement rates for gravity driven and tectonic normal faults. We find that the displacement rates of polygonal faults mark the lower limit of a continuous spectrum of extensional fault displacement rates; they are up to two orders-of-magnitude slower than gravity-driven faults, and up to three orders-of-magnitude slower than the fastest growing tectonic faults. We attribute the ultra-slow kinematic behaviour to the non-tectonic nature of polygonal faults where throw accumulates primarily through dewatering of the largely fine-grained sediments comprising the host layers for the PFS, and differential volumetric strain between the fault footwalls and hangingwalls.

Being an agent of environmental hazard, wild fire is a major concern, especially in Mediterranean countries, where warm and dry summers may lead to high levels of vegetation stress. Vegetation fire, whether caused naturally or by humans, its widespread and occurrence depends on land cover (LC) type and state, climate and meteorological conditions, land use. The main concern of the study is quantification the physical processes related to vulnerability of biomass burning across the climatic gradient for Eastern Mediterranean Europe (Bulgaria) using satellite information, ground observations and model outputs for land surface state. Sixteen years (2004-2019) satellite data from LSASAF FRP-PIXEL product at MSG resolution were processed in order to determine the distribution of vegetation fires for the most risky period July-September. The work examines the relationships of wildfire statistics (number of fires and radiative energy released) to surface temperature (according LSASAF LST product), and soil moisture availability, SMA (according SVAT model) as energy-moisture related climate variables. Regression analyses for the main aggregated vegetation types (forest, shrubs, cultivated) reveal statistically significant dependences: - In short-term climatic aspect: a high correlation between monthly mean positive LST anomalies and fire activity as well as negative correlation between SMA anomalies and fire activity; - In long-term climatic aspect: the contribution of different LC types for fire activity is considered. Identified specific fire regime with location of spots with highest fire activity provides knowledge about fire-climate relations on a regional scale, which is important to define more risky regions as well as for projection the effects of global environmental change.

The Ocean City Inlet was created in the early 1930s by a strong hurricane mobilizing the sediment and thus separating Ocean City from Assateague Island, making the Maryland coastal bay a two-inlet system, with Chincoteague Inlet 60 km to the south. Ecological and hydrodynamic impacts of tropical storms on coastal regions have been well-documented. However, little work has been conducted comparing the impacts of extratropical winter storms with hurricanes, specifically at two-inlet systems. Observed winds, water levels, and waves from NOAA’s National Data Buoy Center (NDBC) and predicted data from the North American Regional Reanalysis (NARR) and Finite Volume Community Ocean Model (FVCOM) models were compiled for 2016 to 2018 to compare the magnitude of forces driving volumetric flow during tropical and extratropical storms. Although the inlets are around 60 km apart, the water level responses were similar during the three blizzard events examined. During each blizzard, the water level increased at the initial arrival of the low-pressure system and then decreased at both inlets over the course of the storm, potentially owing to sustained wind pushing water out of the inlets. The wind and wave forcing on both inlets will be compared using field data to validate modeled data for one blizzard and hurricane per each year. This study, along with the validated hydrodynamic models that were utilized, will assist in predicting environmental stressors and potential influences on shoreline zones under varying storm intensities in two-inlet systems.

The hydrologic cycle in California is strongly influenced by wet season (November to April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet season precipitation variability over both northern and southern California. This mode, named as the “California precipitation” (CPM), is statistically distinct from other well-known modes of pressure variability such as the Pacific-North American pattern. In addition to controlling wet-season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well-known modes of pressure variability. The models also reproduce the CPM’s strong association with California wet season precipitation, giving confidence in the models’ dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally over-simulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics.