Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters

Integration of simulated and observed states through data assimilation as well as model evaluation requires a realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using parameters from three different LSMs participating in the European Land Data Assimilation System (ELDAS) project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect soil moisture more strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the ELDAS LSMs differ strongly, most notably in the available moisture content between the wilting point and the critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated parameter range. The parsimonious framework presented here can be used to investigate first-order parameter sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent with many other studies using different LSMs under a more limited range of possible forcing condition

Toxicological effects of tire wear particles on mummichogs and fathead minnows

Recent studies on the distribution of microplastics in the Charleston Harbor, SC, revealed that a large part of the microplastic particles that are found in the intertidal sediments are tire wear particles. These particles originate from the wear of tire treads on roadways, and wash into the estuary during rain events. The abundance of these particles has raised questions about potential toxicity to aquatic organisms that may ingest these particles. The synthetic rubber in car tires consist of a large variety of chemicals, which can vary between brands, but usually contains styrene-butadiene rubber, carbon black and zinc. To investigate the potential toxicity of tire wear particles, both fathead minnow and Atlantic killifish were exposed to different concentrations of tire crumb particles (38 – 355 µm) in a 7-day exposure. Dissection of the fish revealed that particles were ingested and accumulated in the intestinal tract. At the highest concentration tested (6000 mg/l) we observed partial mortality in the fathead minnow, which is therefore close to the LC50. To investigate if polynuclear aromatic hydrocarbons were leaching from the particles, bile fluorescence was measured, together with potential induction of cytochrome P450-1A through the EROD assay. Elevated levels of 2-, 4-, and 5-, ring structures resembling polynuclear aromatic hydrocarbons were detected in the bile of exposed animals. Induction of CYP1A was also observed in exposed animals at environmentally relevant concentrations (\u3c1-2 g/l)

Modeled contrast in the response of the surface energy balance to heat waves for forest and grassland

Observations have shown that differences in surface energy fluxes over grasslands and forests are amplified during heat waves. The role of land-atmosphere feedbacks in this process is still uncertain. In this study, a single-column model (SCM) is used to investigate the difference between forest and grassland in their energy response to heat waves. Three simulations for the period 2005-11 were carried out: a control run using vegetation characteristics for Cabauw (the Netherlands), a run where the vegetation is changed to 100% forest, and a run with 100% short grass as vegetation. A surface evaporation tendency equation is used to analyze the impact of the land-atmosphere feedbacks on evapotranspiration and sensible heat release under normal summer and heat wave conditions with excessive shortwave radiation. Land-atmosphere feedbacks modify the contrast in surface energy fluxes between forest and grass, particularly during heat wave conditions. The surface resistance feedback has the largest positive impact, while boundary layer feedbacks generally tend to reduce the contrast. Overall, forests give higher air temperatures and drier atmospheres during heat waves. In offline land surface model simulations, the difference between forest and grassland during heat waves cannot be diagnosed adequately owing to the absence of boundary layer feedbacks

Future extreme precipitation intensities based on a historic event

In a warmer climate, it is expected that precipitation intensities will increase, and form a considerable risk of high-impact precipitation extremes. This study applies three methods to transform a historic extreme precipitation event in the Netherlands to a similar event in a future warmer climate, thus compiling a future weather scenario. The first method uses an observation-based non-linear relation between the hourly-observed summer precipitation and the antecedent dew-point temperature (the Pi–Td relation). The second method simulates the same event by using the convective-permitting numerical weather model (NWP) model HARMONIE, for both present-day and future warmer conditions. The third method is similar to the first method, but applies a simple linear delta transformation to the historic data by using indicators from The Royal Netherlands Meteorological Institute (KNMI)'14 climate scenarios. A comparison of the three methods shows comparable intensity changes, ranging from below the Clausius–Clapeyron (CC) scaling to a 3 times CC increase per degree of warming. In the NWP model, the position of the events is somewhat different; due to small wind and convection changes, the intensity changes somewhat differ with time, but the total spatial area covered by heavy precipitation does not change with the temperature increase. The Pi–Td method is simple and time efficient compared to numerical models. The outcome can be used directly for hydrological and climatological studies and for impact analysis, such as flood-risk assessments.</p

Экспериментальный анализ фонетических изменений в английской речи датско-английских билингвов

Одним из ключевых показателей, присущих двуязычному речевому поведению молодых датчан, является специфическая маркированность их английского произношения, что обращает на себя внимание уже с первых минут общения с ними. Эта маркированность произношения проявляется как следствие фонологической интерференции, которая возникает под влиянием артикуляции, интонации, ритмики, ударения первого (датского) языка на соответствующие параметры произношения второго (английского) языка.Одним з ключових показників, властивих двомовній мовній поведінці молодих данців, є специфічна маркированість їх англійської вимови, що звертає на себе увагу вже з перших хвилин спілкування з ними. Ця маркированість вимови виявляється як наслідок фонологічної інтерференції, яка виникає під впливом артикуляції, інтонації, ритміки, наголосу першої (данської) мови на відповідні параметри вимови другої (англійської) мови

Релігійний чинник у процесі вдосконалення Конституції України

Multiple states of woody cover under similar climate conditions are found in both conceptual models and observations. Due to the limitation of the observed woody cover data set, it is unclear whether the observed bimodality is caused by the presence of multiple stable states or is due to dynamic growth processes of vegetation. In this study, we combine a woody cover data set with an above ground biomass data set to investigate the simultaneous occurrences of savanna and forest states under different precipitation forcing. To interpret the results we use a recently developed vegetation dynamics model (the Balanced Optimality Structure Vegetation Model), in which the effect of fires is included. Our results show that bimodality also exists in above ground biomass and retrieved vegetation structure. In addition, the observed savanna distribution can be understood as derived from a stable state and a slightly drifting (transient) state, the latter having the potential to shift to the forest state. Finally, the results indicate that vegetation structure (horizontal vs. vertical leaf extent) is a crucial component for the existence of bimodality

Increase of Simultaneous Soybean Failures Due To Climate Change

While soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find long-term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas

Soil control on runoff response to climate change in regional climate model simulations

Simulations with seven regional climate models driven by a common control climate simulation of a GCM carried out for Europe in the context of the (European Union) EU-funded Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project were analyzed with respect to land surface hydrology in the Rhine basin. In particular, the annual cycle of the terrestrial water storage was compared to analyses based on the 40-yr ECMWF Re-Analysis (ERA-40) atmospheric convergence and observed Rhine discharge data. In addition, an analysis was made of the partitioning of convergence anomalies over anomalies in runoff and storage. This analysis revealed that most models underestimate the size of the water storage and consequently overestimated the response of runoff to anomalies in net convergence. The partitioning of these anomalies over runoff and storage was indicative for the response of the simulated runoff to a projected climate change consistent with the greenhouse gas A2 Synthesis Report on Emission Scenarios (SRES). In particular, the annual cycle of runoff is affected largely by the terrestrial storage reservoir. Larger storage capacity leads to smaller changes in both wintertime and summertime monthly mean runoff. The sustained summertime evaporation resulting from larger storage reservoirs may have a noticeable impact on the summertime surface temperature projections

Storylines of weather-induced crop failure events under climate change

Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the midwestern US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present-day, pre-industrial +2 and 3 ∘C warming, respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure and construct analogues of these failure conditions in future climate settings. We find that crop failures in the midwestern US are linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. Results suggest soybean failures are likely to increase with climate change. With more frequent warm years due to global warming, the joint hot–dry conditions leading to crop failures become mostly dependent on precipitation levels, reducing the importance of the relative compound contribution. While event analogues of the 2012 season are rare and not expected to increase, impact analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. This has implications for assessment of the drivers of extreme impact events