Reliable estimates of occurrence probabilities of sea level extremes are required in coastal planning (e.g. design floods) and to mitigate risks related to flooding. Probabilities of specific extreme events have been traditionally estimated from the observed extremes independently at each tide gauge location. However, this approach has shortcomings. Firstly, sea level observations often cover a relatively short historical time period and thus contain only a small number of extreme cases (e.g. annual maxima). This causes substantial uncertainties when estimating the distribution parameters. Secondly, exact information on sea level extremes between the tide gauge locations and incorporation of depen- dencies of adjacent stations is often lacking in the analysis. A partial remedy to these issues is to exploit spatial dependencies exhibited by the sea level extremes. These dependencies emerge from the fact that sea level variations are often driven by the same physical and dynamical factors at the neighboring stations. Bayesian hierarchical modeling offers a way to model these dependencies. The model structure allows to share information on sea level extremes between the neighboring stations and also provides a natural way to represent modeling uncertainties. In this study, we use Bayesian hierarchical modeling to estimate return levels of annual sea level maximum in the Finnish coastal region, located along the north-east part of the Baltic Sea. As annual maxima are studied, we use the generalized extreme value (GEV) distribution as the basis of our model. To tailor the model specifically for the target region, spatial dependencies are modeled using physical covariates which reflect the distinct geometry of the Baltic Sea. We illustrate the added value of the hierarchical model in comparison to the traditional one using the available long-term tide gauge time series in Finland. Careful analysis of the sources of uncertainties is necessary when extrapolating the return level estimates into the future. This work is a part of project PREDICT (Predicting extreme weather and sea level for nuclear power plant safety) that supports nuclear power plant safety in Finland.

Areas covered in compact sea ice are often assumed to prohibit upper ocean photosynthesis. Yet under-ice phytoplankton blooms (UIBs) have increasingly been observed in the Arctic, driven by anthropogenic changes to the optical properties of Arctic sea ice. Here we show the Southern Ocean can also support widespread UIBs. Using under ice-enabled BGC-Argo float data, we detail numerous high phytoplankton biomass events below compact sea ice preceding seasonal ice retreat, and classify 12 distinct UIB events. Using joint light, sea ice, and ocean conditions obtained from the ICESat-2 laser altimeter and 11 climate model contributions to CMIP6, we find that more than 4 million square kilometers of the compact-ice-covered Southern Ocean could support these events in late spring and early summer.

Aerosol can affect clouds in various ways. Beside the micro-physical impact of aerosol particles on cloud formation, the interference of aerosol with atmospheric radiation leads to changes in local heating, surface fluxes and thus meso-scale circulations all of which may also modify clouds. Rather little is known about these so-called semi-direct effects in realistic settings-a reason, why this study investigates the impact of absorbing aerosol particles on cloud and radiation fields over Germany. Using advanced high-resolution simulations with grid spacings of 312 and 625 m, numerical experiments with different aerosol optical properties are contrasted using purely-scattering aerosol as control case and realistic absorbing aerosol as perturbation. The combined effect of surface dimming and atmospheric heating induces positive temperature and negative moisture anomalies between 800 and 900 hPa impacting low-level cloud formation. Decreased relative humidity as well as increased atmospheric stability below clouds lead to a reduction of low-level cloud cover, liquid water path and precipitation. It is further found that direct and semi-direct effects of absorbing aerosol forcing have similar magnitudes and equally contribute to a reduction of net radiation at the top of the atmosphere .

We highlight a mechanism for the co-production of research with local communities as a means of elevating the social relevance of the geosciences, increasing the potential for broader and more diverse participation. We outline the concept of an “equitable exchange” as an ethical framework guiding these interactions. This principled research model emphasizes that “currencies”- the rewards and value from participating in research - may differ between local communities and geoscientists. For those engaged in this work, an equitable exchange emboldens boundary spanning geoscientists to bring their whole selves to the work, providing a means for inclusive climates and rewarding cultural competency.

We investigate the Chukchi and the Beaufort seas, where salty and warm Pacific Water flows in from the Bering Strait and interacts with the sea ice, contributing to its summer melt. For the first time, thanks to in-situ measurements recorded by two saildrones deployed during summer 2019 and to refined sea ice filtering in satellite L-Band radiometric data, we demonstrate the ability of satellite Sea Surface Salinity (SSS) observed by SMOS and SMAP to capture SSS freshening induced by sea ice melt, referred to as meltwater lenses (MWL). The largest MWL observed by the saildrones during this period occupied a large part of the Chukchi shelf, with a SSS freshening reaching -5 pss. it persisted for up to one month, to this MWL, induced low SSS pattern which restricted the transfer of air-sea momentum to the upper, as illustrated by measured wind speed and vertical profiles of currents. Combined with satellite-based Sea Surface Temperature, satellite SSS provides a monitoring of the different water masses encountered in the region during summer 2019. Using sea ice concentration and estimated Ekman transport, we analyse the spatial variability of sea surface properties after the sea ice edge retreat over the Chukchi and the Beaufort seas. The two MWL captured by both, the saildrones and the satellite measurements, result from different dynamics. Over the Beaufort Sea, the MWL evolution follows the meridional sea ice retreat, whereas in the Chukchi Sea, a large persisting MWL is generated by advection of a sea ice filament.

We evaluate several high-order quadrature schemes for accuracy and efficacy in obtaining orientation-averaged single-scattering properties (SSPs). We use the highly efficient MIDAS to perform electromagnetic scattering calculations to evaluate the gain in efficiency from these schemes. MIDAS is shown to be superior to DDSCAT, a popular discrete dipole approximation (DDA) method. This study is motivated by the fact that quality physical precipitation retrievals rely on using accurate orientation-averaged SSPs derived from realistic hydrometeors as input to radiative transfer Models (RTMs). The DDA has been a popular choice for single-scattering calculations, due to its versatility with respect to target geometry. However, being iterative-solver-based (ISB), the most used DDA codes, e.g. DDSCAT and ADDA, must solve the scattering problem for each orientation of the target separately. As the size parameter and geometric anisotropy of the hydrometeor increase, the number of orientations needed to obtain accurate orientation-averages can increase drastically and so does the computation cost incurred by the ISB-DDA methods. MIDAS is a Direct-Solver-Based (DSB) code, its decomposition of the original large matrix with a high rank into multiple more manageable smaller matrices of lower ranks makes it much more computationally efficient while maintaining excellent accuracy. In addition, direct solvers consider all requested orientations at once, giving MIDAS further advantage over popular ISB-DDA methods. MIDAS, when combined with high-order quadrature for orientation averaging, can be greater than three orders of magnitude more efficient in obtaining RTM-ready SSPs of complex-shaped hydrometeors than existing ISB-DDA methods, with the native quadrature schemes they offer.

This study isolates the influence of sea ice mean state on pre-industrial climate and transient 1850-2100 climate change within a fully coupled global model: The Community Earth System Model version 2 (CESM2). The CESM2 sea ice model physics is modified to increase surface albedo, reduce surface sea ice melt, and increase Arctic sea ice thickness and late summer cover. Importantly, increased Arctic sea ice in the modified model reduces a present-day late-summer ice cover bias. Of interest to coupled model development, this bias reduction is realized without degrading the global simulation including top-of-atmosphere energy imbalance, surface temperature, surface precipitation, and major modes of climate variability. The influence of these sea ice physics changes on transient 1850-2100 climate change is compared within a large initial condition ensemble framework. Despite similar global warming, the modified model with thicker Arctic sea ice than CESM2 has a delayed and more realistic transition to a seasonally ice free Arctic Ocean. Differences in transient climate change between the modified model and CESM2 are challenging to detect due to large internally generated climate variability. In particular, two common sea ice benchmarks - sea ice sensitivity and sea ice trends - are of limited value for comparing models with similar global warming. More broadly, these results show the importance of a reasonable Arctic sea ice mean state when simulating the transition to an ice-free Arctic Ocean in a warming world. Additionally, this work highlights the importance of large initial condition ensembles for credible model-to-model and observation-model comparisons.

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California’s population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.

The assumptions of paleointensity experiments are violated in many natural and archaeological materials, leading to Arai plots which do not appear linear and yield inaccurate paleointensity estimates, leading to bias in the result. Recently, paleomagnetists have adopted sets of “selection criteria” that exclude specimens with non linear Arai plots from the analysis, but there is little consensus in the paleomagnetic community on which set to use. In this paper, we present a statistical method we call Bias Corrected Estimation of Paleointensity (BiCEP), which assumes that the paleointensity recorded by each specimen is biased away from a true answer by an amount that is dependent a single metric of nonlinearity (the curvature parameter $\vec{k}$) on the Arai plot. We can use this empirical relationship to estimate the recorded paleointensity for a specimen where $\vec{k}=0$, i.e., a perfectly straight line. We apply the BiCEP method to a collection of 30 sites for which the true value of the original field is well constrained. Our method returns accurate estimates of paleointensity, with similar levels of accuracy and precision to restrictive sets of paleointensity criteria, but accepting as many sites as permissive criteria. The BiCEP method has a significant advantage over using these selection criteria because it achieves these accurate results without excluding large numbers of specimens from the analysis. It yields accurate, albeit imprecise estimates from sites whose specimens all fail traditional criteria. BiCEP combines the accuracy of the strictest selection criteria with the low failure rates of the less reliable ‘loose’ criteria.

The Parker Solar Probe (PSP) spacecraft completed its second Venus gravity assist maneuver (VGA2) on 26 December 2019. For a 20 minute interval surrounding closest approach, the PSP/FIELDS Radio Frequency Spectrometer (RFS) was set to ‘burst mode’, recording radio spectra from 1.3–19.2 MHz at sub-second cadence. We analyze this burst mode data, searching for signatures of radio frequency ‘sferic’ emission from lightning discharges. During the burst mode interval, only 4 spectra were observed with strong impulsive signals, and all 4 could be attributed to saturation of the RFS high gain stage by \emph{in situ} electrostatic plasma waves. These RFS measurements during VGA2 are consistent with previous non-detection of radio frequency lightning signals from Venus reported by Gurnett et al. (2001).

We applied a polarization analysis of InSight seismic data to estimate the temporal variation and frequency dependence of the Martian ambient noise field. Low-frequency (<1 Hz) P-waves show a diurnal variation in their dominant back-azimuths that are apparently related to wind and the direction of sunlight in a distant area. Low-frequency Rayleigh waves (0.25–1 Hz) show diurnal variations and a dominant back-azimuth related to the wind direction in a nearby area. Low-frequency signals that are derived mainly from wind may be sensitive to subsurface structure deeper than the lithological boundary derived from an autocorrelation analysis. On the other hand, dominant back-azimuths of high-frequency (>1 Hz) waves point toward the InSight lander, especially in daytime, indicating that wind-induced lander noise is dominant at high frequencies. These results point to the presence of several ambient noise sources as well as geologic structure at the landing site.

Repeated periods of inflation‐deflation in the vicinity of Mt. Thorbjorn‐Svartsengi, SW‐Iceland, were detected in January–July, 2020. We used seismic ambient noise and interferometry to characterize temporal variations of seismic velocities (dv/v, %). This is the first time in Iceland that dv/v variations are monitored in near real‐time during volcanic unrest. The seismic station closest to the inflation source center (∼1 km) showed the largest velocity drop (∼ 1%). Different frequency range measurements, from 0.1 to 2 Hz, show dv/v variations, which we interpret in terms of varying depth sensitivity. The dv/v correlates with deformation measurements (GPS, InSAR), over the unrest period, indicating sensitivity to similar crustal processes. We interpret the velocity drop to be caused by crack opening triggered by intrusive magmatic activity. We conclude that single‐station cross‐component analyses provide the most robust solutions for early detection of magmatic activity.

This study focuses on the projections and time of emergence (TOE) for temperature extremes over Australian regions in the phase 6 of Coupled Model Intercomparison Project (CMIP6) models. The model outputs are based on the Shared Socioeconomic Pathways (SSPs) from the Tier 1 experiments (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) in the Scenario Model Intercomparison Project (ScenarioMIP), which is compared with the Representative Concentration Pathways (RCPs) in CMIP5 (i.e., RCP2.6, RCP4.5 and RCP8.5). Furthermore, two large ensembles (LEs) in CMIP6 are used to investigate the effects of internal variability on the projected changes and TOE. As shown in the temporal evolution and spatial distribution, the strongest warming levels are projected under the highest future scenario and the changes for some extremes follow a “warm-get-warmer” pattern over Australia. Over subregions, tropical Australia usually shows the highest warming. Compared to the RCPs in CMIP5, the multi-model medians in SSPs are higher for some indices and commonly exhibit wider spreads, likely related to the different forcings and higher climate sensitivity in a subset of the CMIP6 models. Based on a signal-to-noise framework, we confirm that the emergence patterns differ greatly for different extreme indices and the large uncertainty in TOE can result from the inter-model ranges of both signal and noise, for which internal variability contributes to the determination of the signal. We further demonstrate that the internally-generated variations influence the noise. Our findings can provide useful information for mitigation strategies and adaptation planning over Australia.

The Indian Ocean is warming rapidly, with widespread effects on regional weather and global climate. Sea-surface temperature records indicate this warming trend extends back to the beginning of the 20th century, however the lack of a similarly long instrumental record of interior ocean temperatures leaves uncertainty around the subsurface trends. Here we utilize unique temperature observations from three historical German oceanographic expeditions of the late 19th and early 20th centuries: SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907). These observations reveal a mean 20th century ocean warming that extends over the upper 750 m, and a spatial pattern of subsurface warming and cooling consistent with a 1°–2° southward shift of the southern subtropical gyre. These interior changes occurred largely over the last half of the 20th century, providing observational evidence for the acceleration of a multidecadal trend in subsurface Indian Ocean temperature.

The Chilean central Andes are known for its variety of cryospheric landforms, which have included almost every kind of glacier since their first exploration back in the XIX century. However, there has been a severe reduction of the glacierized area since the 1950s, driven by climate change and enhanced due to the megadrought, which has endured for over a decade in the region. Such decline in glacier volume combined with temperature increasing and precipitation reduction can lead to different types of instabilities. In mountainous regions of high public affluence, glacial instabilities are considered as potential hazards leading to the loss of lives and infrastructure. Here we analyze the Rio Volcan basin (-32.82/-70.00), located 40 km east of Santiago city in the international border with Argentina. The region is known for its closeness to the capital, which favors outdoor activities and hydroelectric power development. Elevation ranges from 3380 to over 6000 m a.s.l. at the San José Volcanic Complex, allowing conditions for coexistence of mountain glaciers, valley glaciers, rock glaciers and glaciarets. According to the public Chilean Glacier Inventory, there are more than 140 mapped cryoforms occupying an area of 57 km2 . Beside snow avalanches, there are multiple factor that provide ideal conditions for cryospheric hazards involving glaciers. Some of those factors are pointed out on the following: The presence of an active volcanic complex sets up the triggering agent for lahars and mixed snow/ice avalanche occurrence. There are three moraine-dammed glacial lakes with a cumulated area of up to 24 hectares in front of the El Morado glacier and two innominates. The lakes are still enlarging along with the glacier shrinkage, conforming three potential glofs in the region. Several debris-free glaciers have a very steep front, steeper than 30 degrees, favoring the occurrence of ice falls and ice avalanches. There is a reported surge event in the Nieves Negras glacier, located at the south face of the San Jose; volcano. The latter would have happened in the late 1940s according to literature. In addition, at least four glaciers showed abnormal advance rates in the early 1990s of up to 100 m/yr, along with the surge-like behavior of the Loma Larga glacier. Providing further knowledge of this complex region is key in order to enhance understanding and hazard management on a day to day basis.

This study deals with the riverbed of the Columbia river in the vicinity of the Hanford 300 Area study site in eastern Washington, where fluctuations in river stage take place both naturally (i.e. seasonally) and in conjunction with hydroelectric power dam operations. These fluctuations create conditions conducive to the influx and transport of fine-grained POM (a biological colloid originating from the river water and/or in situ periphyton production), within near-surface riverbed sediments. Although a great deal is known about dissolved organic matter (DOM) transport and metabolism in hyporheic zone sediments, there is a paucity of quantitative information on POM dynamics and its influence on hyporheic zone biogeochemistry (e.g. dissolved oxygen dynamics). We have developed a hydrobiogeochemical model capable of simulating the transport and metabolism of POM and its impact on dissolved oxygen (DO) distribution within the riverbed as influenced by periodic changes in river stage and fluid flow rate and direction. The model was employed as a tool to interpret the results of in situ measurements of POM intrusion into the riverbed made using “POM traps” emplaced within the upper 20 cm of the riverbed, as well as real-time in situ dissolved oxygen concentrations determined with a novel optical sensor buried directly in the riverbed at 20 cm depth. The simulations reproduced the accumulation of fresh POM within the upper few 5 cm of the riverbed observed in field POM trap deployments. Once sufficient surface POM accumulation takes place, an underlying zone of DO depletion develops as a consequence of variation in the rate of fluid exchange and POM/DOM degradation. The model predicted cyclic, hydrologically-driven variations in near-surface DO that are consistent with the results of the in situ DO probe deployments together with parallel measurements of fluid conductivity and hydrologic pressure. Our results suggest a complex interplay between fluid flow rate/direction and DO distribution that has important implication for riverbed biogeochemical dynamics at a variety of scales, as influenced by hydrological variability as well as the relative intensity of POM input and the availability of oxygen and other electron acceptors for microbial metabolism.

The impacts of global change especially the recent climate-related extremes such as floods and droughts reveal significant vulnerability and exposure of freshwater ecosystems and related human systems to current climate variability. However, the effects of the extreme drought in the Okavango Delta system are not well understood and documented. Therefore, the objective of this use case was to apply the products from Digital Earth Africa namely: the Water Observation from Space (WOfS) derived from Landsat, vegetation cover baseline derived from Sentinel 2 data; and data from the meteorological agencies such as rainfall and measured river discharge data to evaluate the effects of drought in the Okavango Delta wetland system in relation to its upstream areas in Angola. In particular, we used the 2019 drought as a case study to assess inundation extent and vegetation cover dynamics with an emphasis on floodplain and dryland vegetation. Our preliminary results reveal that the Okavango Delta permanent marshes are resilient to drought, whereas seasonal floodplains are susceptible to drought. Further, we discovered that the geospatial location of floodplains has a direct effect on the timing of desiccation, with the western tributaries that flow into Lake Ngami and Thamalakane River being the last to dry out due to drought. In addition, we found that the drought phenomenon in the Cubango-Okavango River Basin region started earlier than 2019 spanning over a period of 5 years; with 2018 as the year when the wetland system reached a minimum threshold for a tipping point triggered by the 2019 drought. In addition, the results contribute to the development of large-scale drought risk information and products for the Cubango- Okavango River Basin with a major focus in the Okavango Delta. Further, this use case provides recent baseline information on the effects of drought on vegetation cover and river flows in the Okavango Delta system at a landscape approach, which are essential elements for making informed science-based decisions on climate risks management and Sustainable Development Goals (SDGs) by relevant authorities in the Okavango Delta and the whole of Cubango-Okavango River Basin. In conclusion, this use case will be upscaled to other transboundary river basins in the Southern Africa Development Community.

Precision yield data is commonly recorded by modern combine harvesters and can be used to help growers optimize their operations. However, there have been very few attempts to predict variation in yield within a given field using multispectral satellite data. We used a precision yield dataset gathered in canola (Brassica napus L.) crops in central Alberta, Canada, and a time series of medium-resolution Sentinel-2 data collected over the growing season. Using two mapping methods, random forest regression and functional data analysis, we were able to predict crop yield to within 12-16% accuracy of actual yield, and to capture within-field variation. Our results demonstrate that time series of medium-resolution multispectral imagery is capable of mapping small-scale variation in crop yields, presenting new research and management applications for these techniques.