We use inverse modelling to infer the distribution and drivers of community-integrated zooplankton grazing dynamics based on the skill with which different grazing formulations recreate the satellite-observed seasonal cycle in phytoplankton biomass. We find that oligotrophic and eutrophic biomes require more and less efficient grazing dynamics, respectively. This is characteristic of micro- and mesozooplankton, respectively, and leads to a strong sigmoidal relationship between observed mean-annual phytoplankton biomass and the optimal grazing parameterization required to simulate its seasonal cycle. Globally, we find Type III rather than Type II functional response curves consistently exhibit higher skill. These new observationally-based distributions can help constrain, validate and develop next-generation biogeochemical models.

The ocean is a major carbon sink and takes up 25-30% of the anthropogenically emitted CO2. A state-of-the-art method to quantify this sink are global ocean biogeochemistry models (GOBMs) but their simulated CO2 uptake differs between models and is systematically lower than estimates based on statistical methods using surface ocean pCO2 and interior ocean measurements. Here, we provide an in-depth evaluation of ocean carbon sink estimates from 1980 to 2018 from a GOBM ensemble. As sources of inter-model differences and ensemble-mean biases our study identifies the (i) model set-up, such as the length of the spin-up, the starting date of the simulation, and carbon fluxes from rivers and into sediments, (ii) the ocean circulation, such as Atlantic Meridional Overturning Circulation and Southern Ocean mode and intermediate water formation, and (iii) the oceanic buffer capacity. Our analysis suggests that the late starting date and biases in the ocean circulation cause a too low anthropogenic CO2 uptake across the GOBM ensemble. Surface ocean biogeochemistry biases might also cause simulated anthropogenic fluxes to be too low but the current set-up prevents a robust assessment. For simulations of the ocean carbon sink, we recommend in the short-term to (1) start simulations in 1765, when atmospheric CO2 started to increase, (2) conduct a sufficiently long spin-up such that the GOBMs reach steady-state, and (3) provide key metrics for circulation, biogeochemistry, and the land-ocean interface. In the long-term, we recommend improving the representation of these metrics in the GOBMs.

Accelerometer-derived neutral mass density (NMD) is an important quantity describing the variability of the upper atmosphere. NMD is widely used to calibrate and validate some models used for satellite orbit determination and prediction. Quantifying the true NMD is nearly impossible due to, among others, the lack of simultaneous in-situ measurements for cross-validation and the incomplete characterization of the uncertainties of these NMD products. This study investigates the error distribution of three different accelerometer-derived NMD products from the CHAMP satellite mission during time periods of both high and low solar activity. Using a multimodel ensemble comprised of both physical and empirical models, the study characterizes the error variance of the NMD. The strategies employed here may be useful and applicable to other space missions spanning over longer time periods. The results show considerable differences among the three CHAMP data sets and also reveal a pronounced latitude dependence in their error distributions. The median error standard deviation of CHAMP NMD is smaller during time periods of high solar activity (11.0%) than during periods of low solar activity (13.1%). The results indicate that the method of processing the accelerometer data has a significant impact on the uncertainty estimates of the different CHAMP NMD products.

A document by Sverre Holm. Click on the document to view its contents.

The honey industry in Kenya is a valuable source of income for small-scale farmers, but they face various challenges such as pests and diseases, lack of access to markets, and inadequate skills. One of the primary challenges facing the honey market in Kenya is the issue of food safety, with poor-quality honey reaching the market, exposing consumers to risks. The use of information technology (IT)-integrated systems in the honey market has gained popularity as a tool for ensuring food safety, traceability, and market access for small-scale farmers. This literature review explores the effectiveness of IT-integrated systems in enhancing food safety in the honey market in Kitui, Baringo, West-Pokot, and Elgeyo Marakwet counties in Kenya. The review identifies the challenges hindering the adoption of IT-integrated systems, including the lack of awareness and knowledge among stakeholders, high implementation costs, inadequate technical skills, and infrastructure. The review highlights the potential benefits of IT-integrated systems, including improved traceability, reduced foodborne illnesses, and enhanced market access for small-scale honey farmers. The target audience for this review is professors and scientists interested in the use of IT in food supply chains.

An empirically based sediment budget model is developed for Cardiff State Beach CA to assess management strategies to maintain beach width subject to mean sea level rise (MSLR) and potentially more frequent El Niño storms. Two decades (2000-2019) of surveys support the hypothesis that the rocky reefs bounding this beach retain sand added to the nearshore zone, except during strong El Niño years with more severe storm waves. The subaerial beach has widened by ~60 m during the last 20 years owing to nourishment (~17K m3/yr) of imported sand, and sand bypassed annually by dredging a lagoon inlet at the beach’s updrift end. The observed widening yields 1 m/yr of mean beach width increase for each 6 m3/m-shoreline of added sand. A strong El Niño year is modeled with a permanent volume loss coupled with a shoreline retreat that recovers partially as the beach profile adjusts between El Niño years. Calibrated with observations from Cardiff and South Torrey Pines (a control beach), the model is used to project beach change through 2050. All modeled scenarios suggest that no bypassing or nourishment (no “management”) will result in tens of meters of beach width loss. However, continued bypassing would partially mitigate MSLR and El Niño beach width losses. An artificially built (living shoreline) dune that backs the beach, if completely undermined during strong El Niño storm waves, stores enough sand to balance one-third of the expected volume loss that year, and may make the beach more resilient and speed subsequent beach recovery.

Geoscience applications have been using sophisticated machine learning methods to model complex phenomena. These models are described as black boxes since it is unclear what relationships are learned. Models may exploit spurious associations that exist in the data. The lack of transparency may limit user’s trust, causing them to avoid high performance models since they cannot verify that it has learned realistic strategies. EXplainable Artificial Intelligence (XAI) is a developing research area for investigating how models make their decisions. However, XAI methods are sensitive to feature correlations. This makes XAI challenging for high-dimensional models whose input rasters may have extensive spatial-temporal autocorrelation. Since many geospatial applications rely on complex models for target performance, a recommendation is to combine raster elements into semantically meaningful feature groups. However, it is challenging to determine how best to combine raster elements. Here, we explore the explanation sensitivity to grouping scheme. Experiments are performed on FogNet, a complex deep learning model that uses 3D Convolutional Neural Networks (CNN) for coastal fog prediction. We demonstrate that explanations can be combined with domain knowledge to generate hypotheses about the model. Meteorological analysis of the XAI output reveal FogNet’s use of channels that capture relationships related to fog development, contributing to good overall model performance. However, analyses also reveal several deficiencies, including the reliance on channels and channel spatial patterns that correlate to the predominate fog type in the dataset, to make predictions of all fog types. Strategies to improve FogNet performance and trustworthiness are presented.

This study investigates the response of the semidiurnal tide (SDT) to the 2013 major sudden stratospheric warming (SSW) event using meteor radar wind observations and mechanistic tidal model simulations. In the model, the background atmosphere is constrained to meteorological fields from the Navy Global Environmental Model - High Altitude analysis system. The solar (thermal) and lunar (gravitational) SDT components are forced by incorporating hourly global temperature tendency fields from the ERA5 forecast model, and by specifying the M2 and N2 lunar gravitational potentials, respectively. The simulated SDT response is compared against meteor wind observations from the CMOR (43.3◦N, 80.8◦W), Collm (51.3◦N, 13.0◦E), and Kiruna (67.5◦N, 20.1◦E) radars, showing close agreement with the observed amplitude and phase variability. Numerical experiments investigate the individual roles of the solar and lunar SDT components in shaping the net SDT response. Further experiments isolate the impact of changing propagation conditions through the zonal mean background atmosphere, non-linear wave-wave interactions, and the SSW-induced stratospheric ozone redistribution. Results indicate that between 80-97 km altitude in the northern hemisphere mid-to-high latitudes the net SDT response is driven by the solar SDT component, which itself is shaped by changing propagation conditions through the zonal mean background atmosphere and by non-linear wave-wave interactions. In addition, it is demonstrated that as a result of the rapidly varying solar SDT during the SSW the contribution of the lunar SDT to the total measured tidal field can be significantly overestimated.

We propose that the observed enrichment of sulfur at the surface of Mercury (up to 4 wt.%) is the product of silicate sulfidation reactions with a S-rich reduced volcanogenic gas phase. Here, we present new experiments on the sulfidation behavior of olivine, diopside, and anorthite. We investigate these reaction products, and those of sulfidized glasses with Mercury compositions previously reported, by mid-IR reflectance spectroscopy. We investigate both the reacted bulk materials as powders, as well as cross-sections of the reaction products by in-situ micro-IR spectroscopy. The mid-IR spectra confirm the presence of predicted reaction products including quartz. The mid-IR reflectance of sulfide reaction products, such as CaS (oldhamite) or MgS (niningerite), is insufficient to be observed in the complex run products. However, the ESA/JAXA BepiColombo mission to Mercury will be able to test our hypothesis by investigating correlated abundances of sulfides with other reaction products such as quartz.

Extensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can leave large amounts of permafrost carbon vulnerable to post-thaw decomposition. We present three years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iskoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble-based Bayesian deep neural network framework. The three-year mean CO2-equivalent flux is estimated to be 106 gCO2 m-2 yr-1 for palsas, 1780 gCO2 m-2 yr-1 for ponds, and -31 gCO2 m-2 yr-1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing.

A document by Yihuan Ruan. Click on the document to view its contents.

Recent studies show that stoichiometric elemental ratios of marine ecosystems are not static at Redfield proportions but vary systematically between biomes. However, the wider Atlantic Ocean is under-sampled for particulate organic matter (POM) elemental composition, especially as it comes to phosphorus. Thus, it is uncertain how environmental variation in this region translates into shifts in C:N:P. To address this, we analyzed hydrography, genomics, and POM concentrations from 877 stations on the meridional transects AMT28 and C13.5, spanning the Atlantic Ocean. We observed nutrient-replete, high-latitude ecosystem C:N:P to be significantly lower than the oligotrophic gyres. Latitudinal and zonal differences in elemental stoichiometry were linked to overall nutrient supply as well as N vs. P limitation. C:P and N:P were generally higher in the P-stressed northern region compared to southern hemisphere regions. We also detected a zonal difference linked to a westward deepening nutricline and a shift from N to P limitation. We also evaluated possible seasonal changes in C:N:P across the basin and predicted these to be limited. Overall, this study confirms latitudinal shifts in surface ocean POM ratios but reveals previously unrecognized hemisphere and zonal gradients. This work demonstrates the importance of understanding how regional shifts in hydrography and type of nutrient stress shape the coupling between Atlantic Ocean nutrient and carbon cycles.

Offshore wind energy deployment in the US is expected to increase in the years to come, with proposed wind farm sites located in regions with high-risk for tropical cyclones. Yet, the wind turbine design criteria outlined by the International Electrotechnical Commission for extreme events may not account for the severe wind conditions in tropical cyclones, even the weaker storms that are likely to reach mid-Atlantic wind resource areas. To evaluate if current design standards capture the extreme conditions of these storms, we perform idealized large-eddy simulations of five tropical cyclones (two category-1, two category-2, and one category-3 storms) using the Weather Research and Forecasting model. Wind conditions near the eyewall of category-1, category-2 and category-3 storms can exceed current design standards for offshore wind turbines. Hub-height winds can exceed design criteria for Class I and Class T turbines for 50-year recurrence periods. Moreover, wind speed shear across the turbine rotor layer is larger than assumed in design specifications. Vertical variations in wind direction across the turbine rotor layer are also large for tropical cyclones of all intensity levels, suggesting design standards should include veer, which can amplify loads in wind turbines.

Shallow cloud decks residing in or near the boundary layer cover a large fraction of the Southern Ocean (SO) and play a major role in determining the amount of shortwave radiation reflected back to space from this region. In this article, we examine the macrophysical characteristics and thermodynamic phase of low clouds (tops < 3 km) and precipitation using ground-based ceilometer, depolarization lidar and vertically-pointing W-band radar measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE) from April 2016-March 2017. During MICRE, low clouds occurred ~65% of the time on average (slightly more often in austral winter than summer). About 2/3 of low clouds were cold-topped (temperatures < 0°C); these were thicker and had higher bases on average than warm-topped clouds. 83-88% of cold-topped low clouds were liquid phase at cloud base (depending on the season). The majority of low clouds had precipitation in the vertical range 150 to 250 meters below cloud base, a significant fraction of which did not reach the surface. Phase characterization is limited to the period between April 2016 and November 2016. Small-particle (low-radar-reflectivity) precipitation (which dominates precipitation occurrence) was mostly liquid below-cloud, while large-particle precipitation (which dominates total accumulation) was predominantly mixed/ambiguous or ice phase. Approximately 40% of cold-topped clouds had mixed/ambiguous or ice phase precipitation below (with predominantly liquid phase cloud droplets at cloud base). Below-cloud precipitation with radar reflectivity factors below about -10 dBZ were predominantly liquid, while reflectivity factors above about 0 dBZ were predominantly ice.

A document by Rebecca Ringuette. Click on the document to view its contents.

A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEB). As these retention basins are only being replenished by the intermittent precipitation, increasing droughts in the region and a growing demand for water have been presumed to jointly contributed to the land degradation. To understand the impact of climate change and human activities on dryland vegetation over the AEB, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53%) exhibited a greening trend over the past two decades. Partial correlation analyses indicated that climatic factors had varying effects on vegetation productivity as a function of vegetation types and aridity. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a significant expansion (17.3%) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the hyper-arid AEB, our results therefore raised the concerns on the ecological and societal sustainability in this region, where a mild increase in precipitation might not be able to catch up the rising evaporative demand and water consumption resulted from global warming and irrigation intensification.

Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analysed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models’ sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

Supervolcanic eruptions induced abrupt global cooling (roughly at a rate of ~1ºC/year lasting for years to decades), such as the prehistoric Yellowstone eruption released, by some estimates, SO2 about 100 times higher than the 1991 Mt. Pinatubo eruption. An abrupt global cooling of several ºC, even if only lasting a few years, would present immediate and drastic stress on biodiversity and food production - posing a global catastrophic risk to human society. Using a simple climate model, this paper discusses the possibility of counteracting supervolcanic cooling with the intentional release of greenhouse gases. Although well-known longer-lived compounds such as CO2 and CH₄ are found to be unsuitable for this purpose, select fluorinated gases (F-gases), either individually or in combinations, may be released at gigaton scale to offset most of the supervolcanic cooling. We identify candidate F-gases (viz. C4F6 and CH3F) and derive radiative and chemical properties of ‘ideal’ compounds matching specific cooling events. Geophysical constraints on manufacturing and stockpiling due to mineral availability are considered alongside technical and economic implications based on present-day market assumptions. The consequences of F-gas release in perturbing atmospheric chemistry are discussed in the context of those due to the supervolcanic eruption itself. The conceptual analysis here suggests the possibility of mitigating certain global catastrophic risks via intentional intervention.