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Despite in‐situ observations of perennial firn aquifers (PFAs) at specific locations of the Antarctic ice sheet, a comprehensive continent‐wide mapping of PFA distribution is currently lacking. We present an estimate of their distribution across Antarctica in the form of a probability assessment using a Monte Carlo technique. Our approach involves a novel methodology that combines observations from Sentinel‐1 and Advanced SCATterometer (ASCAT) with output from a regional climate model. To evaluate our method, we conduct an extensive comparison with Operation Ice Bridge observations from the Greenland Ice Sheet. Application to Antarctica reveals high PFA probabilities in the Antarctic Peninsula (AP), particularly along its northern, northwestern, and western coastlines, as well as on the Wilkins, Müller, and George VI ice shelves. Outside the AP, PFA probability is low, except for some locations with marginally higher probabilities, such as on the Abbot, Totten, and Shackleton ice shelves. Plain Language Summary: We explore the presence of subsurface water storage within the firn layer in Antarctica, known as perennial firn aquifers (PFA), using a new method that combines satellite data and climate models. These PFAs, previously identified in Greenland, store and transmit meltwater, and could influence ice‐sheet behavior. Our study maps the likelihood of PFAs across Antarctica, finding high probabilities along the Antarctic Peninsula's northern and western coasts, as well as on specific ice shelves. Outside this region, PFAs are unlikely, except for a few spots with somewhat enhanced probability on Abbot, Totten, and Shackleton ice shelves. This inventory enhances our understanding of Antarctic hydrology and has broader implications for understanding ice‐sheet dynamics. Key Points: An innovative aquifer detection method, combining satellite and regional climate model data, is applied to AntarcticaThe evaluation of the methodology in Greenland, showing 91% correspondence, suggests good adaptability for AntarcticaThe Antarctic Peninsula stands out as the only region with high aquifer probability; in the rest of Antarctica, the likelihood is low [ABSTRACT FROM AUTHOR]

The representation of vegetative sub‐canopy wind is critical in numerical weather prediction (NWP) models for the determination of the air‐surface exchange processes of heat, momentum, and trace gases. Because of the relationship between wind speed and fire behaviors, the influence of the canopy on near‐surface wind speed is critical for prognostic fire spread models used in regional NWP models. In practice, the wind speed at the midflame point of fires (midflame wind speed) is used to determine the rate of fire spread. However, the wind speeds from most in situ measurements and NWP models are taken at some reference height above the canopy and fire flames. Hence, this study develops a modular and computationally‐efficient one‐dimensional model set composed of a canopy wind model and a wind adjustment factor (WAF) model for NWP applications across scales. The model set uses prescribed foliage shape functions to represent the vertical vegetation profile and its impacts on the three‐dimensional structure of horizontal wind speeds. Results from the canopy wind model well agree with ground‐based observations with average mean absolute bias, root mean square error and determination coefficients around 0.18 m s−1, 0.40 m s−1and 0.90, respectively. The WAF model provides midflame wind speeds by estimating the WAF based on canopy, fire and flame characteristics. Various user‐definable options provide flexibility to adapt to variations in canopy characteristics and additional complexities associated with wildfires. The model set is expected to improve NWP models by providing an improved representation of the sub‐grid wind flows at any spatial scale. Plain Language Summary: Compared to bare ground, wind speeds are reduced by the presence of vegetation at the surface by the leaves and woody parts of plants, especially within forest canopies. The altered wind speeds affect the atmosphere's stability and transport processes, as well as the spread of wildfires. Several fire prediction models are connected with weather models; however, most weather models do not consider such sub‐canopy effects on reducing wind speeds as the vertical structure of vegetation are not included in the models. Thus, this study presents a canopy model that can simulate the effects of vegetation, primarily forest canopies, on horizontal wind speeds used in weather models. The canopy model also provides the estimation of the wind speeds at the height of the midpoint of flame above ground level (midflame wind speed), which can better describe fire behaviors for general use in fire prediction models. The model results agree well with both above and below canopy top wind speed observations, and the midflame wind speeds are reliable when compared to previous studies. The canopy model is expected to improve weather modeling by providing a better representation of near‐surface wind flow, particularly for areas covered by dense forest canopies. Key Points: This study presents and evaluates a canopy wind model that simulates horizontal wind speeds above and within vegetation canopiesModel results agree well with observations and should provide a better representation of wind flow in numerical weather prediction modelsThe wind adjustment factor model provides midflame wind speed estimation, which can be used for the estimation of the rate of fire spread [ABSTRACT FROM AUTHOR]

De‐aliasing products are used in the estimation process of satellite‐based gravity field computation to reduce errors from high‐frequency mass variations that alias into monthly gravity fields. The latest official product is AOD1B RL07 and describes non‐tidal atmosphere and oceanic mass variations at 3‐hourly resolution. However, the model‐based de‐aliasing products are inevitably incomplete and prone to temporally and spatially correlated errors that substantially contribute to errors in the estimated gravity fields. Here, we investigate possible enhancement of current de‐aliasing products by nesting a regional high‐resolution atmospheric reanalysis over Europe into a global reanalysis. As further novelty we include almost mass consistent terrestrial water storage variability from a regional hydrological model nested into a global model as additional component of the de‐aliasing product. While we find in agreement with earlier studies only minor contributions from increasing the temporal resolution beyond 3‐hourly data, our investigations suggest that contributions from continental hydrology and from regional non‐hydrostatic atmospheric modeling to sub‐monthly mass variations could be relevant already for gravity fields estimated from current gravity missions. Moreover, in the context of extreme events, we find regionally contributions from additional moisture fields, such as cloud liquid water, in the order of a few mm over Europe. We suggest this needs to be taken into account when preparing data analysis schemes for future space gravimetric missions. Plain Language Summary: Observing temporal variations in the Earth's gravity field with satellite gravimetry plays an essential role for monitoring mass transports on and underneath the Earth's surface. This is crucial for understanding the evolution of floods and droughts, the role of groundwater pumping, and to quantify the melting of ice sheets and glaciers and the resulting sea level rise. In order to isolate the target variable (e.g., terrestrial water storage changes) unwanted signals (e.g., fast mass variations in the atmosphere) need to be removed in the gravity field estimation process using background models, so‐called de‐aliasing models. This paper focuses on improving background models by incorporating regional high‐resolution models, which more specifically resolve certain processes in the atmosphere. Our hypothesis is that this will lead to better gravity fields with increased spatial resolution and less noise. Moreover, we find that considering fast hydrological variations as additional background model could improve gravity fields from the current satellite mission GRACE‐FO. For the first time, we quantify contributions from so far neglected atmospheric moisture fields, such as cloud liquid water, to enhance background models in the context of extreme events—which, however, will likely be in particular relevant for more sensitive gravity missions in the future. Key Points: We provide an atmosphere‐hydrology de‐aliasing product with regional mass‐consistent refinement over EuropeUsing non‐hydrostatic as opposed to hydrostatic numerical weather prediction model output significantly impacts the de‐aliasing productWe found that for extreme events additional moisture fields unaccounted in present Atmosphere and Ocean De‐aliasing (AOD) models can reach magnitudes relevant for de‐aliasing [ABSTRACT FROM AUTHOR]

Greece is characterized by a significant warming trend in recent decades, accompanied by increasing frequency, intensity, and duration of heat waves (HWs). A particularly devastating HW that affected the country was the late July/early August 2021 event (JA2021HW), which lasted for 9 days (July 28–August 5). Focusing on the hottest day of the event (August 3), the main characteristics of JA2021HW are presented in the current study, using model reanalysis data and up to 11‐year observations derived from the dense network of ground‐based weather stations operated by the Meteo Unit at the National Observatory of Athens (NOA). This analysis highlights the severity of JA2021HW, especially in the central and southernmost regions of Greece. Most importantly, the impact of the direct effect of anthropogenic greenhouse gases to the examined extreme event, in terms of intensity and probability of occurrence, is examined by employing a regional 31‐member ensemble (ENS) modeling approach based on Weather Research and Forecasting (WRF) model, which is operationally used by NOA/Meteo. Firstly, WRF is validated under 7‐day lead‐time ENS simulations with current‐state greenhouse gas (GHG) concentrations (GHG_2021 ENS), showing a robust model performance in replicating the JA2021HW's magnitude on August 3rd. Then, 7‐day lead‐time ENS simulations with the GHG concentrations reduced to the pre‐industrial (1854) levels (GHG_1854 ENS) are performed and compared to the GHG_2021 ENS experiment. The results reveal a contribution of the immediate anthropogenic warming due to the increased GHG concentrations to the JA2021HW intensity in West and South continental and insular Greece, which can be important in the framework of the human health impacts of extreme temperatures. For the event's occurrence probability, no robust evidence of any change could be derived. These statements are partially constrained by the fact that only the direct GHG effect on the timescale of a few days was examined. [ABSTRACT FROM AUTHOR]

We describe and evaluate a system for regional modeling of atmospheric composition with the Met Office Unified Model (UM), suitable for climate, weather forecasting and air quality applications. In this system, named NUMAC ("Nested UM with Aerosols and Chemistry"), a global model provides boundary conditions for regional models nested within it, using the Met Office's Regional Nesting Suite for multi‐scale simulations. The regional models, which can run at convection‐permitting or cloud‐resolving scales, use the same code as the global model. The system includes double‐moment prognostic aerosol microphysics with interactive chemistry of sulfur species, ozone, NOx, and CO as in the UK Earth System Model. Double‐moment prognostic cloud microphysics is optional. To test NUMAC, we compare simulations to surface and aircraft measurements from NASA's Korea‐United States Air Quality campaign over South Korea. The performance of the regional model, which we run at 5 km resolution, is similar to the well‐evaluated global model when the regional and global models use the same emissions. Most species such as ozone, NOx, OH, or PM2.5 are simulated within a factor of 2 of observations most of the time, though they are biased low compared to monitors in polluted areas (observed surface dry PM2.5 averages 28 μgm−3 but we simulate 17 μgm−3). Meteorology and clouds are represented satisfactorily. With higher‐resolution emissions, many of the low model biases are reduced, but a tuning was required to keep NO concentrations realistic, indicating shortcomings in the chemistry scheme. We demonstrate the potential of NUMAC for studies of aerosol‐cloud interactions. Plain Language Summary: Unified atmospheric simulation systems, defined as those that are routinely used to represent air quality, weather and climate with the same code, are rare. The UK Met Office Unified Model (UM) is one such system, but until recently, aerosol‐cloud interactions were represented only in global climate simulations. Effects of aerosol‐cloud interactions and atmospheric chemistry on Earth's radiation balance are large and must be included in global and regional climate predictions. However, partly because clouds and emissions are spatially inhomogeneous, their effects on climate are currently poorly understood. Simulations with high grid resolution can help us understand the relevant processes better. We document here a system for representing air quality, weather and climate with the UM, including chemistry‐aerosol‐cloud interactions, at high spatial grid resolution on regional scales. We test our system against surface and aircraft measurements of atmospheric chemical species such as ozone and particulate matter made in May 2016 in Korea. The model represents most species well, but has some shortcomings we must address in future work. Overall, however, we judge that it is ready for studies of how atmospheric chemical species and particulate matter affect air quality, weather and climate, and can be further improved as it is used. Key Points: We introduce a system for simulations of regional air quality and aerosol‐climate interactions with the UK Met Office Unified ModelWe evaluate the model against data from NASA's Korea‐United States Air Quality campaign in 2016 over South KoreaDespite shortcomings in the representation of some chemical and aerosol species, the system is ready for application and further development [ABSTRACT FROM AUTHOR]

In the context of Arctic amplification many of the feedback mechanisms, decreasing or enhancing the warming, involve clouds and water vapor. Currently, there is a gap in understanding the role of clouds which leads to uncertainties in climate simulations. Modeling frameworks such as the ICOsahedral Non‐hydrostatic model (ICON) are used to understand the Arctic atmospheric processes as well as predict future changes. In this study, we challenge ICON in the large‐eddy setup (ICON‐LEM) by performing cloud‐resolving simulations over parts of Svalbard, including Ny‐Ålesund. We ran daily simulations over 5 months and analyzed the column above Ny‐Ålesund. The local supersite's observations enabled us to create a baseline for the model performance focusing on the representation of liquid water and water vapor. We narrow in on possibilities to improve the cloud microphysical representation based on statistical evaluations, not just single cases. We found an astonishing agreement between most of the analyzed variables. For instance, the model integrated water vapor showed only a low bias of 0.21 kg m−2. The number of cloudy days is slightly higher in the model (+4%). Further, we found that the model produces an unrealistically high number of pure ice clouds. Small to medium precipitation events are similar in amount and time but the number of strong precipitation events is underestimated. Further results are discussed and show that ICON‐LEM is a useful tool to study the Arctic. With this thorough analysis, we highlight the value of local cloud‐resolving simulations to understand changes in the Arctic atmosphere. Plain Language Summary: The Arctic is warming faster than the rest of the globe. Many atmospheric processes impact the rate of the warming. Explaining these processes is challenging. Especially processes involving clouds lack sufficient understanding. We can use computer models to answer some of our questions, but for that, the models must be good enough and we must know their limitations. In this study, we used several months of simulations for Svalbard which we created with the ICOsahedral Non‐hydrostatic (ICON) model. The simulations had a high resolution and we used a model specialized for that: The ICON Large‐Eddy model (ICON‐LEM). We analyzed how well the model can represent clouds and humidity. One result is that many clouds contained more ice than what was measured at that location. We also found that the model produced more clouds than were observed. On the other side, the water vapor amount and wind field were simulated relatively precisely. The results show that the ICON model is helpful to study the Arctic. The ability to analyze many cases made it possible to quantify some of the limitations in the creation and development of clouds and precipitation. Knowing these limitations will help us to improve the model in the future. Key Points: Cloud resolving simulations for several months using the ICOsahedral Non‐hydrostatic model Large‐Eddy model in the Arctic are evaluatedHigh agreement of model and observations on wind flow, integrated water vapor, and light to medium precipitationMore clouds are simulated than observed but at the same time the model clouds lack liquid water [ABSTRACT FROM AUTHOR]

Model estimates of the contribution of anthropogenic and natural fluxes of greenhouse gases from the territories of different countries to global climate change in the 21st century under different scenarios of anthropogenic forcing were obtained. Quantitative estimates were made for the effect of changes in regional climatic conditions on the intensity of the greenhouse gas exchange between the atmosphere and natural ecosystems over different time horizons in comparison with anthropogenic emissions. For Russia, China, Canada, and the United States, the CO2 uptake by natural ecosystems in the second half of the 21st century decreases under all scenarios of anthropogenic forcing, with a weakening of the corresponding climate-stabilizing effect. At the same time, the methane emission to the atmosphere by wetlands in the analyzed regions increases significantly in the 21st century according to the model estimates. As a consequence, the cumulative effect of natural fluxes of greenhouse gases into the atmosphere for some regions may accelerate the warming. [ABSTRACT FROM AUTHOR]

Regional‐scale estimations of sorghum biomass production allow identification of optimum genotype×environment×management (G×E×M) combinations for bioenergy generation. The objective of this study was to determine the degree of contributions of G, E, and M toward variability in sorghum biomass in the United States. Using the Agricultural Production Systems sIMulator in a grid computing platform, biomass was simulated for irrigated and rainfed conditions for 30 years across the United States for four sorghum varietal types (grain—GS, sudangrass—SS, photosensitive—PS, and photo‐insensitive—PI). Simulated biomass was assessed by environments clustered using the sum of intercepted solar radiation (ir), mean of temperature stress factor (tp) and water stress factor (sw). Simulated biomass ranged from 5.8 t ha−1 (GS‐rainfed) to 27.5 t ha−1 (PI‐irrigated). Under high‐temperature environments (mean annual temperature = 25°C), rainfed biomass between 40 and 80 days after planting (DAP) was strongly correlated with sw (r = 0.64–0.86) and irrigated biomass with ir (r = 0.68–0.81). Under low‐temperature environments (mean annual temperature = 18°C) after 40 DAP, tp and ir had greater effects than sw (r = 0.55–0.82). Biomass variance was mainly explained by varietal type (50%–76%) in all environments×irrigation combinations, except in the high‐ and mid‐temperature environments under rainfed conditions where rainfall had the major effect (25%–45%). However, when mean temperature during the growing season decreased from 25°C (high environments) to 18°C (low environments), the contribution of mean temperature to biomass variance increased from 7% to 34% (rainfed) and from 4% to 36% (irrigated). Varietal type had the larger interactions with other factors independently of the environment and irrigation. We demonstrated a need to quantify (i) the main G×E×M drivers of biomass variability based on environmental stress factors and (ii) the variance contribution of these drivers on sorghum biomass. Our regional‐scale estimations are key inputs for future robust biomass projections of energy sorghum genotypes integrating G×E×M under climate change scenarios. [ABSTRACT FROM AUTHOR]

The tropical Atlantic Ocean receives an important freshwater supply from river runoff and from precipitation in the intertropical convergence zone. It results in a strong salinity stratification that may influence vertical mixing, and thus sea surface temperature (SST) and air–sea fluxes. The aim of this study is to assess the impact of salinity stratification on the tropical Atlantic surface variables. This is achieved through comparison among regional 1/4 ∘ coupled ocean–atmosphere simulations for which the contribution of salinity stratification in the vertical mixing scheme is included or discarded. The analysis reveals that the strong salinity stratification in the northwestern tropical Atlantic induces a significant increase of SST (0.2 ∘ C–0.5 ∘ C) and rainfall (+ 19%) in summer, hereby intensifying the ocean–atmosphere water cycle, despite a negative atmospheric feedback. Indeed, the atmosphere dampens the oceanic response through an increase in latent heat loss and a reduction of shortwave radiation reaching the ocean surface. In winter, the impacts of salinity stratification are much weaker, most probably because of a deeper mixed layer at this time. In the equatorial region, we found that salinity stratification induces a year-round shoaling of the thermocline, reinforcing the cold tongue cool anomaly in summer. The concept of barrier layer has not been identified as relevant to explain the SST response to salinity stratification in our region of interest. [ABSTRACT FROM AUTHOR]

Background: Intensive winter wheat–summer maize (Triticum aestivum L.–Zea mays L.) double‐cropping systems in the North China Plain often show high nitrogen (N) losses and water use causing harmful threats to the environment. Methods: Continuous multiple‐year simulations (31 years) with the HERMES model were used to spatially quantify N and water losses at county scale in order to identify best‐practice management applications. Results: Results show simulated annual long‐term N losses for the investigated Quzhou County, Hebei Province of 297 kg N ha−1 under common farmers practice treatment (FP) and 102 kg N ha−1 under optimized treatment including model derived N fertilizer recommendation and automated irrigation (OPTai). Total losses by N leaching, volatilization and denitrification were 57% (FP) and 40% (OPTai) of the applied fertilizer N, respectively. Spatial differences in N losses were found due to survey‐specific differences of average N inputs among the townships. More than 260 kg N ha−1 y−1 of fertilizer input, N losses of 190 kg N ha−1 y−1, and around 116 mm y−1 of irrigation water could be saved on average by optimized treatments compared to farmers practice. Conclusion: On clay loam soil only OPTai could maintain crop yield without drought stress. The optimized treatments had the lowest N inputs and N losses but they did not seem to be able to sustain the organic N pools. [ABSTRACT FROM AUTHOR]

This study attempts dynamical downscaling to improve north Indian ocean (NIO) tropical cyclone prediction from a global multimodel ensemble prediction system using weather research and forecasting (WRF) model. A total of 16 ensembles are used in the WRF simulations, these ensembles are bias‐corrected prior to downscaling for model climatological errors. The ensemble mean constructed from the output of all downscaled ensembles is analyzed for added value to global predictions. This mean is also compared against observation as well as high‐resolution (12 km) deterministic forecast from global forecast system (GFS). Two devastating NIO tropical cyclone cases of year 2017 which were not reliably predicted by global systems have been selected for this study. The results show that downscaled predictions well simulate the intensity and spatial distribution of the rainfall and relative vorticity associated with these cyclonic storms. The wind and temperature vertical profiles during cyclone mature stage are also captured more accurately than raw prediction and high‐resolution global deterministic forecast. The study affirms the adequacy of dynamical downscaling in predicting the cyclonic storms over global real‐time weather forecasting system. [ABSTRACT FROM AUTHOR]

Research on empirical or physical models of ionospheric parameters is one of the important topics in the field of space weather and communication support services. To improve the accuracy of predicting the monthly median ionospheric propagating factor at 3000 km of the F2 layer (identified as M(3000)F2) for high frequency radio wave propagation, a model based on modified orthogonal temporal–spatial functions is proposed. The proposed model has three new characteristics: (1) The solar activity parameters of sunspot number and the 10.7-cm solar radio flux are together introduced into temporal reconstruction. (2) Both the geomagnetic dip and its modified value are chosen as features of the geographical spatial variation for spatial reconstruction. (3) A series of harmonic functions are used to represent the M(3000)F2, which reflects seasonal and solar cycle variations. The proposed model is established by combining nonlinear regression for three characteristics with harmonic analysis by using vertical sounding data over East Asia. Statistical results reveal that M(3000)F2 calculated by the proposed model is consistent with the trend of the monthly median observations. The proposed model is better than the International Reference Ionosphere (IRI) model by comparison between predictions and observations of six station, which illustrates that the proposed model outperforms the IRI model over East Asia. The proposed method can be further expanded for potentially providing more accurate predictions for other ionospheric parameters on the global scale. [ABSTRACT FROM AUTHOR]

The Brazilian Equatorial Shelf (BES) is one among the macrotidal regions worldwide. This study used a high-resolution numerical configuration of the ocean model ROMS (Regional Ocean Modeling System) forced with realistic surface and lateral forcing, as well as with tides and river discharges. Tidal heights of more than 2 m were found in three regions in BES due to the large tidal amplification across the estuarine channels inside each region: Amazon, Pará, and Maranhão, and for a considerable time fraction. Heights between 4 and 5 m occurred with a frequency greater than 20%–30% in some regions. All hypothetical barrages proposed in this study were capable of an annual power production, in two-way mode, higher than La Rance (533 GWh year−1, two-way operation, France) and Sihwa (553 GWh year−1, flood-only operation, South Korea), except one with the same production as Sihwa barrage. The installation effort was evaluated using the Gibrat ratio, the ratio between the length of the barrage and its annual energy production. Among the proposed barrages, the most efficient ones have an annual power generation greater than 1500 GWh year−1 and a Gibrat ratios between 1.17 and 3.26, much lower than the Gibrat ratio of Sihwa tidal barrage. • Tidal Heights between 4 and 5 m occurred with a frequency greater than 20–30%. • Most proposed barrages had an annual power production higher than La Rance. • The most efficient proposed barrages had a lower installation cost than Sihwa. • Brazilian Equatorial Shelf is a source of clean energy to be explored. [ABSTRACT FROM AUTHOR]

There is an emerging understanding toward the importance of land‐atmosphere interactions in the monsoon system, but the effects of specific land and water management practices remain unclear. Here, using regional process‐based experiments, we demonstrate that monsoon precipitation is sensitive to the choice of irrigation practices in South Asia. Experiments with realistic representation of unmanaged irrigation and paddy cultivation over north‐northwest India exhibit an increase in the late season terrestrial monsoon precipitation and intensification of widespread extreme events over Central India, consistent with changes in observations. Such precipitation changes exhibit substantially different spatial patterns in experiments with a well‐managed irrigation system, indicating that increase in unmanaged irrigation might be a factor driving the observed changes in the intraseasonal monsoon characteristics. Our findings stress the need for accurate representation of irrigation practices to improve the reliability of earth system modeling over South Asia. Plain Language Summary: South Asia is one of the most heavily irrigated regions of the world, and a large proportion of the water for irrigation is extracted through groundwater pumping. The major summer crop of the region is paddy, which is cultivated in fields flooded with water. Here we study the impact of this agricultural water use on the Indian Summer Monsoon using a climate model. We find that the excess irrigation over northern India shifts the September month monsoon rainfall toward the northwestern part of the subcontinent. This pattern of change is also visible in recorded rainfall over the region in the recent past. Another major impact we see is on the intensity of extreme rainfall events over the central part of the country. Central India has witnessed an increase in extreme rainfall in recent decades, and through this study, we find that irrigation increases the rainfall intensity over Central India during these events. These findings indicate that it is important to represent irrigation practices more accurately in climate models. Key Points: Representation of irrigation and paddy fields is added to a regional climate model to study that impacts on Indian Summer MonsoonExcess irrigation over northern India causes northwestward shift in September month monsoon rainfall over the land massIrrigation intensifies widespread extreme events over Central India [ABSTRACT FROM AUTHOR]

Abstract Ocean-estuary exchange variability was studied from 2008 to 2014 at a low latitude coastal plain estuary, Todos os Santos Bay, Brazil, near 13°S. Wind data from Climate Forecast System Reanalysis (CFSR) were used to complement the results of a high-resolution regional ocean model, ROMS. Volume exchange occurred under two distinct circulation patterns, namely Circ + and Circ − , that matched the down-estuary (spring/summer) and up-estuary (autumn/winter) wind regimes respectively. Circ + promoted water-column stratification, while Circ − lasted longer, induced more volume exchange, and reversed the estuarine circulation. In addition, a clockwise lateral circulation (looking into the estuary) occurred during Circ + , whereas a counterclockwise lateral circulation was observed during Circ −. Connectivity was observed between Jaguaripe River and Todos os Santos Bay entrance. Increases in Jaguaripe River discharge, in conjunction with up-estuary winds, promoted a northward advection of the river plume toward the main entrance of the estuary, creating a lateral density gradient over Todos os Santos Bay' western shoal. Our results demonstrate that findings about estuarine circulation at higher latitudes are applicable to low latitude estuaries when dynamical depth and/or basin's width indicate that Earth's rotation is relevant. Highlights • Bidirectional rather than unidirectional volume exchange tends to occur at Todos os Santos Bay entrance. • Exchange occurred under two patterns: intensification (Circ +) or attenuation/reversal (Circ -) of gravitational circulation. • Circ + promoted stratification, while Circ − lasted longer, induced more exchange, and reversed the estuarine circulation. • Some Circ − pulses represented a shift in the forcing of the total exchange flow from tidally driven to wind driven. • Connectivity between Jaguaripe River and the bay may enable supply of nutrients and migration of species to the estuary. • Even in a low latitude estuary (13°S), its depth and basin's width revealed that Earth's rotation effect is important. [ABSTRACT FROM AUTHOR]

[Display omitted] • Parallel computing framework is introduced to upscale agroecosystem model. • The new model well reproduced soil and crop variables in North China Plain. • The optimum irrigation water and fertilizer N demands were estimated. • The model shows a potential value to optimize regional water and N management. Process-based agroecosystem model is difficult to apply at the regional scale due to the complexity of model parameterization and execution efficiency. Thus, developing a regional agroecosystem model is challenging. In this study, a parallel computing framework is introduced to upscale an agroecosystem model (WHCNS model) from field to regional scale by integrating parallel computing, inversing modeling, and spatial calculation. The newly developed model was tested in the North China Plain (NCP) for modeling water consumption, N fate, and crop growth for winter wheat–summer maize rotation system under different water and N management practices. The optimum irrigation water and fertilizer N demands of winter wheat–summer maize system was estimated based on the auto-irrigation and auto-fertilization component in the model. Multisite testing showed that the model well reproduced soil water and N dynamics, crop biomass, LAI, yield, and crop N uptake, with R2 exceeding 0.886. The estimated annual average irrigation water and fertilizer N demands of winter wheat–summer maize rotation system was 395.8 mm and 529.5 kg N ha−1, respectively. Winter wheat was the major consumer of irrigation water and fertilizer N, accounting for 79.2 % and 54.4 % of annual irrigation water and chemical N fertilizer use, respectively. These results suggested that winter wheat was a crop with high water and fertilizer N demand in the NCP. The RegWHCNS model has potential value to assess and optimize regional water and N management for intensive agricultural-production systems. This study can provide as a scientific reference to upscale agroecosystem models from field to region for other agroecosystem models. [ABSTRACT FROM AUTHOR]

Abstract Upwelling and uplift events from 2008 to 2012 were analyzed at the South Equatorial Current bifurcation region and in the Salvador Canyon near 13°S. Wind data from Climate Forecast System Reanalysis (CFSR) were used to complement the results of a high-resolution regional ocean model. Upwelling and uplift events occurred predominantly during spring and summer. Previous studies have suggested upwelling implications for fisheries and fish diversity in the region. Uplift events were, on average, longer than upwelling events. Spatially, the strongest events were found over the adjacent shelf of Todos os Santos Bay and Salvador Canyon. This was also the region with the most frequent events. Analysis of forcing mechanisms revealed that wind-driven upwelling, mainly due to upward Ekman pumping, is dominant, with current-driven upwelling providing an additional contribution. Connectivity between Salvador Canyon and Todos os Santos Bay upwelling processes was observed in 20 events, preferentially during the austral summer. This connectivity was linked to a northward coastal undercurrent, which, in turn, advected upwelled/uplifted water from the canyon to the bay-adjacent shelf area. Connectivity occurred under two conditions: i) intense canyon upwelling and ii) low influence of the Brazil Current flow on the shelf. The results of this study demonstrate that wind-driven upwelling contributes directly to the connectivity phenomenon, whereas Brazil Current encroachment inhibits this process. Hence, the results shown herein are widely applicable to upwelling regions under the influence of Western Boundary Currents and/or submarine canyons where upwelling may occur. Highlights • Upwelling events were stronger over the bay's adjacent shelf and Salvador Canyon. • Wind-driven upwelling is mainly due to upward Ekman pumping. • Wind-driven upwelling prevails over current-driven upwelling. • Upwelling connectivity occurred preferentially during the austral summer. • Connectivity occurred under intense canyon upwelling and low influence of the WBC. [ABSTRACT FROM AUTHOR]

At the same temperature, below 0 °C, the saturation vapor pressure (SVP) over ice is slightly less than the SVP over liquid water. Numerical models use the Clausius-Clapeyron relation to calculate the SVP and relative humidity, but there is not a consistent method for the treatment of saturation above the freezing level where ice and mixed-phase clouds may be present. In the context of current challenges presented by cloud microphysics in climate models, we argue that a better understanding of the impact that this treatment has on saturation-related processes like cloud formation and precipitation, is needed. This study explores the importance of the SVP calculation through model simulations of the Indian summer monsoon (ISM) using the regional spectral model (RSM) at 15 km grid spacing. A combination of seasonal and multiyear simulations is conducted with two saturation parameterizations. In one, the SVP over liquid water is prescribed through the entire atmospheric column (woIce), and in another the SVP over ice is used above the freezing level (wIce). When SVP over ice is prescribed, a thermodynamic drying of the middle and upper troposphere above the freezing level occurs due to increased condensation. In the wIce runs, the model responds to the slight decrease in the saturation condition by increasing, relative to the SVP over liquid water only run, grid-scale condensation of water. Increased grid-scale mean seasonal precipitation is noted across the ISM region in the simulation with SVP over ice prescribed. Modification of the middle and upper troposphere moisture results in a decrease in mean seasonal mid-level cloud amount and an increase in high cloud amount when SVP over ice is prescribed. Multiyear simulations strongly corroborate the qualitative results found in the seasonal simulations regarding the impact of ice versus liquid water SVP on the ISM’s mean precipitation and moisture field. The mean seasonal rainfall difference over All India between wIce and woIce is around 10% of the observed interannual variability of seasonal All India rainfall. [ABSTRACT FROM AUTHOR]

This study investigates the future U.S. PM 2.5 pollution under multiple emissions scenarios, climate states, and long-range transport (LRT) effects using the regional Community Multi-scale Air Quality (CMAQ) model integrated with a regional climate model. CMAQ with fixed chemical lateral boundary conditions (LBCs) successfully reproduces the present-day PM 2.5 pollution and its major species in rural and suburban areas, but has some discrepancies in urban areas such as the Los Angeles Basin, where detailed emissions and meteorology conditions cannot be resolved by the 30 km grid. Its performance is slightly worsened when using dynamic chemical LBCs from global chemical transport model (CTM) simulations, which provide cleaner conditions into the CMAQ lateral boundaries. Under future Intergovernmental Panel on Climate Change (IPCC) emission scenarios, CMAQ projects large PM 2.5 reductions (∼40% for A1B and ∼20% for A1Fi scenario) in the eastern United States, but slight to moderate increases (∼5% for A1B and ∼10% for A1Fi) in the western United States. The projected increases are particularly large (up to 30%) near the Mexico-U.S. border, suggesting that Mexico is a major source for future U.S. PM 2.5 pollution. The effect from climate change alone is estimated to increase PM 2.5 levels ubiquitously (∼5% for both A1B and A1Fi) over the United States, except for a small decrease in the Houston, Texas area, where anthropogenic non-methane volatile organic compounds (NMVOCs) emissions dominate. This climate penalty, however, is substantially smaller than effects of emissions change, especially in the eastern United States. Future PM 2.5 pollution is affected substantially (up to −20%) by changes in SO 2 emissions and moderately (3–5%) by changes in NO x and NH 3 emissions. The long-range transport (LRT) effects, which are estimated by comparing CMAQ simulations with fixed and dynamic LBCs, are regional dependent, causing up to 10–20% decrease over the western United States in future summertime PM 2.5 pollution. Therefore, it is important to consider the relative contributions of emissions scenarios, climate conditions, and LRT to the major PM 2.5 components in future U.S. air quality regulation. [ABSTRACT FROM AUTHOR]

The African Sahel region is known to be highly vulnerable to climate variability and change. We analyze rainfall in the Sahelian Sudan in terms of distribution of rain-days and amounts, and examine whether regional climate models can capture these rainfall features. Three regional models namely, Regional Model (REMO), Rossby Center Atmospheric Model (RCA) and Regional Climate Model (RegCM4), are evaluated against gridded observations (Climate Research Unit, Tropical Rainfall Measuring Mission, and ERA-interim reanalysis) and rain-gauge data from six arid and semi-arid weather stations across Sahelian Sudan over the period 1989 to 2008. Most of the observed rain-days are characterized by weak (0.1–1.0 mm/day) to moderate (> 1.0–10.0 mm/day) rainfall, with average frequencies of 18.5% and 48.0% of the total annual rain-days, respectively. Although very strong rainfall events (> 30.0 mm/day) occur rarely, they account for a large fraction of the total annual rainfall (28–42% across the stations). The performance of the models varies both spatially and temporally. RegCM4 most closely reproduces the observed annual rainfall cycle, especially for the more arid locations, but all of the three models fail to capture the strong rainfall events and hence underestimate its contribution to the total annual number of rain-days and rainfall amount. However, excessive moderate rainfall compensates this underestimation in the models in an annual average sense. The present study uncovers some of the models' limitations in skillfully reproducing the observed climate over dry regions, will aid model users in recognizing the uncertainties in the model output and will help climate and hydrological modeling communities in improving models. [ABSTRACT FROM AUTHOR]

Abstract: Global climate models including the Climate Forecast System version 2, the operational model used for prediction of Indian summer monsoon rainfall by the India Meteorological Department, has dry precipitation bias, mostly over densely populated Ganga basin. This restricts the use of model output in hydrological simulations/forecasts. We use regional atmospheric Weather Research and Forecasting model coupled with land surface models, driven by the boundary conditions from Climate Forecast System version 2. We find significant reduction in the dry bias of Indian summer monsoon rainfall with regional land‐atmosphere model and this attributes to (a) improved moisture transport from Western and Upper Indian Ocean to Ganga Basin and (b) improved precipitation recycling over the Ganga basin. We find that the smoothened topography in the global model allows advection of cold dry subtropical air into the Indian monsoon region, contributing to the cold temperature and dry precipitation bias. These results have important implications for monsoon simulations in developing operational hydroclimatic prediction system in India. [ABSTRACT FROM AUTHOR]

Using high resolution convective permitting simulations, we have investigated the sensitivity of historical orographically enhanced extreme precipitation events to idealized temperature perturbations. Our simulations were typical autumn and winter synoptic scale extreme precipitation events on the west coast of Norway. The response in daily mean precipitation was around 5%/K for a 2 °C temperature perturbation with a clear topographical pattern. Low lying coastal regions experienced relative changes that were only about 1/3 of the changes at higher elevations. The largest changes were seen in the highest elevations of the near coastal mountain regions where the change was in order of +7.5%/K. With a response around 5%/K, our simulations had a precipitation response that was around 2%/K lower than Clausius-Clapeyron scaling and 3%/K lower than the water vapor change. The below Clausius-Clapeyron scaling in precipitation could not be explained by changes in vertical velocities, stability or relative humidity. We suggest that the lower response in precipitation is a result of a shift from the more efficient ice-phase precipitation growth to less effective rain production in a warmer atmosphere. A considerable change in precipitation phase was seen with a mean increase in rainfall of 16%/K which was partly compensated by a reduction in snowfall of around 23%/K. This change may have serious implications for flooding and geohazards. [ABSTRACT FROM AUTHOR]

In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model, to investigate the impact of canopy representation on regional evapotranspiration. The WRF-ACASA model uses a multilayer structure to represent the canopy, consequently allowing microenvironmental variables such as leaf area index (LAI), air and canopy temperature, wind speed and humidity to vary both horizontally and vertically. The improvement in canopy representation and canopy-atmosphere interaction allow for more realistic simulation of evapotranspiration on both regional and local scales. The coupled WRF-ACASA model is compared with the widely used intermediate complexity Noah land surface model in WRF (WRF-Noah) for both potential (ETo) and actual evapotranspiration (ETa). Two LAI datasets (USGS and MODIS) are used to study the model responses to surface conditions. Model evaluations over a diverse surface stations from the CIMIS and AmeriFlux networks show that an increase surface representations increase the model accuracy in ETa more so than ETo. Overall, while the high complexity of WRF-ACASA increases the realism of plant physiological processes, the model sensitivity to surface representation in input data such as LAI also increases. [ABSTRACT FROM AUTHOR]

Development and maintenance of the linearized and adjoint code for advanced circulation models is a challenging issue, requiring a significant proportion of total effort in operational data assimilation (DA). The ensemble-based DA techniques provide a derivative-free alternative, which appears to be competitive with variational methods in many practical applications. This article proposes a hybrid scheme for generating the search subspaces in the adjoint-free 4-dimensional DA method (a4dVar) that does not use a predefined ensemble. The method resembles 4dVar in that the optimal solution is strongly constrained by model dynamics and search directions are supplied iteratively using information from the current and previous model trajectories generated in the process of optimization. In contrast to 4dVar, which produces a single search direction from exact gradient information, a4dVar employs an ensemble of directions to form a subspace in order to proceed. In the earlier versions of a4dVar, search subspaces were built using the leading EOFs of either the model trajectory or the projections of the model-data misfits onto the range of the background error covariance (BEC) matrix at the current iteration. In the present study, we blend both approaches and explore a hybrid scheme of ensemble generation in order to improve the performance and flexibility of the algorithm. In addition, we introduce balance constraints into the BEC structure and periodically augment the search ensemble with BEC eigenvectors to avoid repeating minimization over already explored subspaces. Performance of the proposed hybrid a4dVar (ha4dVar) method is compared with that of standard 4dVar in a realistic regional configuration assimilating real data into the Navy Coastal Ocean Model (NCOM). It is shown that the ha4dVar converges faster than a4dVar and can be potentially competitive with 4dvar both in terms of the required computational time and the forecast skill. [ABSTRACT FROM AUTHOR]

Modeling studies and observations show that the thermohaline properties of the North West European Shelf Seas (NWESS) are sensitive to surface wind and heat flux forcing, as well as river outflows that transport fresh water from land to the ocean. In previous studies, it was assumed that the variability of the thermohaline properties in response to river outflow could be adequately sampled with a high-resolution, submesoscale permitting, long-term (i.e., 30-year) deterministic hindcast. In this study, we assume that the statistical distribution of the river forcing, rather than the time series of forcing itself, is adequately constrained by a 28-year history (1991 to 2018) of river forcing created specifically for our domain. In this way, we created an ensemble of 10 lower-resolution (≈ 7-km), short-term (i.e., 2.5 years) hindcast models that are forced with randomly perturbed river outflows and an ensemble of surface fluxes from the 10-member ECMWF ERA5 reanalysis (the 'Test' ensemble) as well with a companion ensemble that is forced with the ERA5 surface forcing fluxes but unperturbed river outflows (the 'Base' ensemble) for the June 2016 through December 2018 time period. In both ensembles, the modeled evolution of 25-hour averaged (to partially filter out tides) temperature and salinity is realistic with peaks in summer for sea surface temperature and in winter for salinity, and annual amplitudes that are comparable to those found in other studies of the NWESS. The increased mean and standard deviation of the sea surface and bottom salinity in the Test ensemble are partly an artifact of the assumption that the errors in river forcing have a log-normal distribution that mimics the episodic nature of river outflow with a positive mean and an asymmetrical shape with a long tail toward large values. For surface density, the standard deviation in the Test ensemble was below 0.5 kg/m3, covering an areal extent larger than that for the Base ensemble throughout the year. The annual cycle of the areal extent of density in that range had a peak in summer and minima in winter, in phase with that of the river outflow forcing. Overall, the effect of uncertain river forcing on the thermohaline properties in this study is small. In order to understand the true impact of river forcings, better temporal and spatial observations of river outflow are needed. • Modeled effect of perturbed rivers on hydrography is studied in European shelf seas. • The variability of SS density and its area are larger when rivers are randomly pulsed. • The area of SS density STD has a seasonal cycle with peaks/minima in summer/winter. • Relative to studies with larger river flows, the river effect on SS density is small. • The effect on hydrography depends on very uncertain river forcing; more obs needed. [ABSTRACT FROM AUTHOR]

Extreme rainfall events in the central Indian region are often related to the passage of synoptic scale monsoon low-pressure systems (LPS). This study uses the surrogate climate change method on ten monsoon LPS cases connected to observed extreme rainfall events, to investigate how sensitive the precipitation and runoff are to an idealized warmer and moister atmosphere. The ten cases are simulated with three different initial and lateral boundary conditions: the unperturbed control run, and two sets of perturbed runs where the atmospheric temperature is increased uniformly throughout the atmosphere, the specific humidity increased according to Clausius Clapeyron's relation, but the large-scale flow is unchanged. The difference between the control and perturbed simulations are mainly due to the imposed warming and feedback influencing the synoptic flow. The mean precipitation change with warming in the central Indian region is 18-20 %/K, with largest changes at the end of the LPS tracks. The LPS in the warmer runs are bringing more moisture further inland that is released as precipitation. In the perturbed runs the precipitation rate is increasing at all percentiles, and there is more frequent rainfall with very heavy intensities. This leads to a shift in which category that contributes most to the total precipitation: more of the precipitation is coming from the category with very heavy intensities. The runoff changes are similar to the precipitation changes, except the response in intensity of very heavy runoff, which is around twice the change in intensity of very heavy precipitation. [ABSTRACT FROM AUTHOR]

The estimates of Cs emissions from the accident happened in Elektrostal at the beginning of April 12, 2013 are presented. The transport of radionuclides and their dry and wet deposition on the surface are computed using the Lagrangian stochastic model of the NOSTRADAMUS software package worked out by Nuclear Safety Institute of Russian Academy of Sciences. Prognostic fields of wind (horizontal and vertical components) in the lower troposphere, precipitation, and vertical and horizontal turbulence diffusivity coefficients in the lower atmosphere (up to 4 km) were used as input data. Prognostic fields were obtained using the WRF-ARW numerical mesoscale model. [ABSTRACT FROM AUTHOR]

Performance of the adjoint and adjoint-free 4-dimensional variational (4dVar) data assimilation techniques is compared in application to the hydrographic surveys and velocity observations collected in the Adriatic Sea in 2006. Assimilating the data into the Navy Coastal Ocean Model (NCOM) has shown that both methods deliver similar reduction of the cost function and demonstrate comparable forecast skill at approximately the same computational expense. The obtained optimal states were, however, significantly different in terms of distance from the background state: application of the adjoint method resulted in a 30–40% larger departure, mostly due to the excessive level of ageostrophic motions in the southern basin of the Sea that was not covered by observations. [ABSTRACT FROM AUTHOR]

This study explores simulations using the numerical Weather Research and Forecasting (WRF) model, with respect to the representation of the nocturnal low-level jet (LLJ) over the Sahel. Three sets of experiments are designed to investigate the sensitivity with respect to (i) the boundary-layer and surface-layer schemes including local and non-local closures, (ii) the horizontal grid spacing and the number of vertical levels within the lowest kilometre and (iii) the role of initial and boundary data. In total, 27 simulations are performed on one host domain and two nested domains for a representative LLJ case study on 9 November 2006. The ability of the individual simulations to represent the life cycle of the nocturnal LLJ is validated against observations carried out in the framework of the African Monsoon Multidisciplinary Analysis (AMMA) special observation periods: surface wind observations from Agoufou, Bamba and Banizoumbou, atmospheric wind profiles derived from Atmospheric Radiation Measurement Mobile Facility, wind radar measurements at Niamey and profiles from radiosondes launched at Niamey. All runs reproduce the general characteristics of the observed LLJs satisfactorily. In contrast to earlier studies, results are more sensitive to the choice of initial and boundary data (here GFS and ECMWF) than to the boundary-layer and surface schemes used or to model grid resolution. The sensitivity to the model grid resolution is surprisingly minor. Considerable differences between the individual stations suggest that local surface conditions such as roughness length, albedo or soil moisture may play an important role in the observed mismatch between model simulations and observations. [ABSTRACT FROM AUTHOR]

There are various global and regional methods that have been proposed for the modeling of ionospheric vertical total electron content (VTEC). Global distribution of VTEC is usually modeled by spherical harmonic expansions, while tensor products of compactly supported univariate B-splines can be used for regional modeling. In these empirical parametric models, the coefficients of the basis functions as well as differential code biases (DCBs) of satellites and receivers can be treated as unknown parameters which can be estimated from geometry-free linear combinations of global positioning system observables. In this work we propose a new semi-parametric multivariate adaptive regression B-splines (SP-BMARS) method for the regional modeling of VTEC together with satellite and receiver DCBs, where the parametric part of the model is related to the DCBs as fixed parameters and the non-parametric part adaptively models the spatio-temporal distribution of VTEC. The latter is based on multivariate adaptive regression B-splines which is a non-parametric modeling technique making use of compactly supported B-spline basis functions that are generated from the observations automatically. This algorithm takes advantage of an adaptive scale-by-scale model building strategy that searches for best-fitting B-splines to the data at each scale. The VTEC maps generated from the proposed method are compared numerically and visually with the global ionosphere maps (GIMs) which are provided by the Center for Orbit Determination in Europe (CODE). The VTEC values from SP-BMARS and CODE GIMs are also compared with VTEC values obtained through calibration using local ionospheric model. The estimated satellite and receiver DCBs from the SP-BMARS model are compared with the CODE distributed DCBs. The results show that the SP-BMARS algorithm can be used to estimate satellite and receiver DCBs while adaptively and flexibly modeling the daily regional VTEC. [ABSTRACT FROM AUTHOR]

The Brazilian Equatorial Shelf (BES) is a very dynamic region due to the influence of various forcings: trade winds, macrotides, the Amazon River, and the North Brazil Current (NBC). To investigate each forcing's role in BES circulation, a control simulation and three sensitivity simulations (without rivers, without tides, and without winds) were performed using the Regional Ocean Modeling System (ROMS) with a horizontal resolution of 1/24° and realistic forcings. The results showed that the NBC advected low-salinity waters from the Amazon River northwestward to Caribbean Sea and occasionally eastward through the NBC retroflection. The NBC exhibited the strongest flow when all forcings were included in the simulation. On the other hand, southeasterly winds inhibited the NBC retroflection system and, thus reducing its contribution to North Equatorial Counter Current (NECC) and Equatorial Undercurrent (EUC). The simulation without tides showed a plume more advected by the NBC and constricted to the first 10 m of the water column, while in the simulation without wind the plume was less advected northwestward by the weakened NBC and spread more toward the shelf break. The correlation analysis highlighted the complexity of the system, the complementarity of the forcings and their time-variable interdependence. The three sensitivity simulations and correlation analysis demonstrated the wind to be the main mechanism of the circulation in the BES. The sensitivity simulations also gave insight of how circulation would respond in a future scenario if climate changes persist. Previous studies suggest that the reduced Amazon runoff may indirectly weaken Atlantic ITCZ and thus trade winds causing a weakened circulation in tropical Atlantic. Our results show that this weakened circulation would advect even less fresh water northward, worsening the disturbances in North Atlantic due to the reduced freshwater supply. • North Brazil Current exhibited the strongest flow in the simulation with all forcings. • Tides and river discharge worked against the NBC retroflection. • The wind is the main mechanism of the circulation in the Brazilian Equatorial Shelf. • Correlation analyses showed the complementarity and interdependence of the forcings. • Sensitivity simulations gave insight of the circulation if climate changes persist. [ABSTRACT FROM AUTHOR]

A regional model was used to simulate the water cycle over the Indian Ocean (IO) and Maritime Continent (MC). Sixteen 92 day simulations were performed using different combinations of eight cumulus parameterization schemes and three planetary boundary-layer (PBL) parameterization schemes. The strength of the water cycle in the IO and MC, measured by its domain mean precipitation and precipitable water, differs substantially among the simulations. The large spread of water cycle strength is mainly toward dry biases in comparison to global data assimilation products. The simulated water cycle, its spread, and biases differ between the IO and MC. Influences of PBL schemes can penetrate into the upper troposphere and those by cumulus schemes into the boundary layer. Dry biases in the simulations are produced mainly because of feedbacks among erroneously low diabatic heating peaks, shallow moisture convergence layers, dry lower troposphere, and weak surface evaporation. There is no single type of parameterization scheme that can be identified to be the main sources of the dry biases. It is the combination of errors from three types of parameterization schemes, namely, cumulus, PBL, and microphysics, that makes the simulated water cycle unrealistic. The lesson learned is that the tropical water cycle can be better simulated only by improving parameterization schemes of different processes all together as a package. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is the most prominent mode of tropical intraseasonal variability in the climate system and has worldwide influences on the occurrences and forecasts of heavy precipitation. This paper investigates the sensitivity of precipitation over the contiguous United States (CONUS) in a case study (boreal 2004-05 winter). Several major storms affected the western and eastern CONUS producing substantial economic and social impacts including loss of lives. The Weather Research and Forecasting (WRF) model is used to perform experiments to test the significance of the MJO amplitude. The control simulation uses the MJO amplitude observed by reanalysis, whereas the amplitude is modified in perturbation experiments. WRF realistically simulates the precipitation variability over the CONUS, although large biases occur over the Western and Midwest United States. Daily precipitation is aggregated in western, central and eastern sectors and the frequency distribution is analyzed. Increases in MJO amplitude produce moderate increases in the median and interquartile range and large and robust increases in extreme (90th and 95th percentiles) precipitation. The MJO amplitude clearly affects the transport of moisture from the tropical Pacific and Gulf of Mexico into North America providing moist rich air masses and the dynamical forcing that contributes to heavy precipitation. [ABSTRACT FROM AUTHOR]

Upwelling events observed from the years 2003 to 2011 were analyzed. Our focus was on the Abrolhos–Campos region (ACR, 15°S–23°S), which is located along the Brazilian Margin and influenced by a western boundary current, the Brazil Current (BC). Satellite sea surface temperature, National Centers for Environmental Prediction (NCEP) and Global Forecast System (GFS) wind data were used to complement the results of a high-resolution regional oceanic model to investigate the occurrence of and the mechanisms responsible for intense upwelling events in this region. These events were more frequent from 20°S to 23°S. Over 90% of the identified upwelling events were influenced by favorable wind stress. Surface Ekman transport was found to be more important for the region from Prado (17°S) to Marataízes (21°S), whereas upward Ekman pumping played a more important role from 22°S to 23°S. Current-driven upwelling processes associated with the location of the BC as well as its velocity and meso-scale activity were also analyzed. The results showed that these mechanisms are highly influenced by the local topography. Topographic effects exerted via the acceleration of the BC are more obvious in the southern ACR, while in the Prado region, BC cyclonic meanders tend to be more relevant. Moreover, eddy-driven upwelling increases in the southward direction after the passage of the Vitória-Trindade Ridge (20°S), an important submarine chain, which acts to constrain and modulate the southward flow of the BC. [ABSTRACT FROM AUTHOR]

Examples of the regional modeling on the basis of the seismotomography data are considered. The formulations of regional problems are analyzed and their ill-posedness and uncertainty of the results due to artificial conditions imposed on the region boundaries are discussed. To improve the modeling it is proposed to preassign additional regularization conditions and to bring the lower computation-domain boundary into coincidence with the interface between the upper and lower mantles. A new technique of regularizing solutions based on the use of geological data is proposed. The results of the calculations of the Mesozoic geodynamics of the Arctic region in which the positions of the subduction zones and the Arctic plume are preassigned are presented. [ABSTRACT FROM AUTHOR]

A regional ocean biogeochemical model for the Bering Sea is used to dynamically downscale three Earth System Models from the CMIP5 archive under the RCP 8.5 and RCP 4.5 scenarios. These continuous model runs, completed in conjunction with the Alaska Climate Integrated Modeling Project (ACLIM), span the 2006–2100 timeframe and project continued warming, freshening, and ocean acidification (OA) for the Bering Sea shelf region over the 21st Century, with larger magnitude changes in the RCP 8.5 scenario. The downscaled projections suggest that annual average surface seawater aragonite saturation state (Ω arag) for the Bering Sea shelf will decrease by 0.63–0.86 under RCP 8.5 and 0.18–0.43 under RCP 4.5 by 2100. Surface pH values decrease by 0.31–0.35 under RCP 8.5 and 0.07–0.13 under RCP 4.5. Seasonally, Ω arag < 1 conditions start to emerge for ∼2 months per year during winter between 2015 and 2030 under both climate change scenarios. Under RCP 8.5, the duration of these undersaturated conditions grows to ∼5 months per year by 2100, occurring from mid-October through mid-March. Under RCP 4.5, these conditions remain constrained to 2–3 months per year by 2100. In both scenarios, summer months maintain conditions of Ω arag > 1 due to primary productivity, though the maximum in Ω arag is greatly reduced under RCP 8.5. Spatially, the regions of greatest pH and Ω arag decline are the southeastern Bering Sea shelf and the outer shelf domain near the shelf break. Linear trends in carbonate variables between our downscaled simulations and the Earth System Model (ESM) output are comparable and indistinguishable compared to the model spread. However, bottom water trends differ somewhat between the ESM and our downscaled simulations, with the latter more consistently resolving the different shelf domains. The OA information provided by these downscaled simulations can help inform biological sensitivity experiments and longterm strategic planning for marine fisheries management. [ABSTRACT FROM AUTHOR]

This study examines the sensitivity of climate to changes in vegetation cover and land use in South Sudan. The focus lies on the effect of deforestation on precipitation and surface temperature especially during the rainy season. Sensitivity experiments are performed with the third version of the Abdus Salam International Centre for Theoretical Physics Regional Climate Model (RegCM3) where the present forest and vegetation cover south of 10°N in Sudan are replaced by either grass or, as an extreme case, desert. The model experiments were conducted for a time period of almost 21 years, from January 1989 to August 2009, and were preceded by a control experiment to ascertain the fidelity of the model simulations. The experiments indicate that the vegetation changes affect precipitation and surface temperature in both Southern and Central Sudan significantly although the land cover changes were imposed only in the south. The precipitation during the rainy season (June through September) was reduced in the perturbed region by about 0.1-2.1 mm d−1 for the desert scenario and by 0.1-0.9 mm d−1 for the grass scenario. The surface temperature increases by about 1.2 and 2.4 °C in the grass and desert scenario, respectively. The precipitation reduction is thus not only local but also extends to Central Sudan and neighbouring regions. The study demonstrates significant dependency for Southern and Central Sudan precipitation on the land use in Southern Sudan and indicates that the deforestation has both local and non-local regional climatic effects. [ABSTRACT FROM AUTHOR]

Abstract: Simulating the oceanic circulation in Eastern Boundary Upwelling Systems (EBUS) is a challenging issue due to the paucity of wind stress products of a sufficiently high spatial resolution to simulate the observed upwelling dynamics. In this study, we present the results of regional simulations of the Humboldt current system (Peru and Chile coasts) to assess the value of a statistical downscaling model of surface forcing. Twin experiments that differ only from the momentum flux forcing are carried out over the 1992–2000 period that encompasses the major 1997/98 El Niño/La Niña event. It is shown that the mean biases of the oceanic circulation can be drastically reduced simply substituting the mean wind field of NCEP reanalysis by a higher resolution mean product (QuikSCAT). The statistical downscaling model improves further the simulations allowing more realistic intraseasonal and interannual coastal undercurrent variability, which is notoriously strong off Central Peru and Central Chile. Despite some limitations, our results suggest that the statistical approach may be useful to regional oceanic studies of present and future climates. [Copyright &y& Elsevier]

Abstract: A new set of cross-over marine data has been used to generate a regional model for the secular variation of the total geomagnetic field, showing the potential of the suggested approach for gaining a better knowledge of the field over oceanic regions. The model, which is valid for the Northern Atlantic region during the temporal interval 1960–2000, was obtained using spherical cap harmonic analysis (SCHA) in space and penalized splines in time. The maximum spatial expansion is equivalent to degree 9 in ordinary spherical harmonic analysis. Annual mean intensity data from different geomagnetic observatories have been used to improve the spatial and temporal resolution of the original dataset. Results indicate that the regional model improves, in terms of the root mean square error, the prediction given by the 11th generation of IGRF and CM4 global models, especially for the geomagnetic observatories considered. We also provide the uncertainty of the model coefficients and the secular variation prediction given by a bootstrap algorithm. The model is available in the EarthRef. org Digital Archive at http://earthref.org/ERDA/1728/. [Copyright &y& Elsevier]

Wind erosion of soil is a major concern of the agricultural community, as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion degrade air quality, reduce visibility, and cause perturbations to regional radiation budgets. PM10 emitted from the soil surface can travel hundreds of kilometers downwind before being deposited back to the surface. Thus, it is necessary to address agricultural air pollutant sources within a regional air quality modeling system in order to forecast regional dust storms and to understand the impact of agricultural activities and land-management practices on air quality in a changing climate. The Wind Erosion Prediction System (WEPS) is a new tool in regional air quality modeling for simulating erosion from agricultural fields. WEPS represents a significant improvement, in comparison to existing empirical windblown dust modeling algorithms used for air quality simulations, by using a more process-based modeling approach. This is in contrast with the empirical approaches used in previous models, which could only be used reliably when soil, surface, and ambient conditions are similar to those from which the parameterizations were derived. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, we used the EROSION submodel from WEPS as a PM10 emission module for regional modeling by extending it to cover a large region divided into Euclidean grid cells. The new PM10 emission module was then employed within a regional weather and chemical transport modeling framework commonly used for comprehensive simulations of a wide range of pollutants to evaluate overall air quality conditions. This framework employs the Weather Research and Forecasting (WRF) weather model along with the Community Multi-scale Air Quality (CMAQ) model to treat ozone, particulate matter, and other air pollutants. To demonstrate the capabilities of the WRF/EROSION/CMAQ dust modeling framework, we present here results from simulations of dust storms that occurred in central and eastern Washington during 4 October 2009 and 26 August 2010. Comparison of model results with observations indicates that the modeling framework performs well in predicting the onset and timing of the dust storms and the spatial extent of their dust plumes. The regional dust modeling framework is able to predict elevated PM10 concentrations hundreds of kilometers downwind of erosion source regions associated with the windblown dust, although the magnitude of the PM10 concentrations are extremely sensitive to the assumption of surface soil moisture and model wind speeds. Future work will include incorporating the full WEPS model into the regional modeling framework and targeting field measurements to evaluate the modeling framework more extensively. [ABSTRACT FROM AUTHOR]

Abstract: A new method is introduced to find the relatively sudden temporal changes or jerks of the geomagnetic field over the polar regions. Geomagnetic jerk events during the 20th century have mostly been identified from direct measurements at mid-latitude geomagnetic observatories. Recently, however, global magnetic spherical harmonic models like CM4 and CHAOS have been found effective in identifying the Antarctic events of 1969, 1978, 1985, 1991 and 2003 that were followed by jerks in the Arctic region with time delays of one to three years. The present study extends these events into earlier decades from 1900 to 1985 using the global harmonic model gufm1 with localized spherical coefficients or Slepian functions for the polar regions. Power spectral minima for the localized coefficients correlate with and thus help identify the abrupt changes in geomagnetic secular acceleration models. The time delay between the Antarctic and Arctic geomagnetic jerks was also confirmed. [Copyright &y& Elsevier]

The Weather Research and Forecasting model (WRF) forced by ERA40 re-analyses, is used to examine, at regional scale, the role of key features of the local atmospheric circulation on the origin and development of Tropical Temperate Troughs (TTTs) representing a major contribution to South African rainfall during austral summer. A cluster analysis applied on 1971-2000 ERA40 and WRF simulated daily outgoing longwave radiation reveals for the November-February season three coherent regimes characteristic of TTTs over the region. Analyses of WRF simulated TTTs suggest that their occurrence is primarily linked with mid-latitude westerly waves and their phasing. Ensemble experiments designed for the case of austral summer 1996/1997 allow to examine the reproducibility of TTT events. The results obtained illustrate the importance of westerly waves phasing regarding the persistence of rain-producing continental TTT events. Moreover, oceanic surface conditions prevailing over the Agulhas current regions of the South West Indian Ocean (SWIO) are also found to influence TTT persistence for regional experiments with an oceanic mixed layer, warmer sea surface temperatures being associated with increased moisture advection from the SWIO where latent heat release is enhanced, favoring baroclinic instability and thus sustaining convection activity locally. [ABSTRACT FROM AUTHOR]

Primary socioeconomic activities in the System of Platja de Palma (SPdP), located in the southwestern coast of Mallorca, Spain, are beach-based holidays and, therefore, are very closely linked to climate. The social, economic and environmental adaptation that this region must make in the mid- and long-term period has to take into account the changes in the tourist potential when the climate changes. A second-generation climate index for tourism (CIT) has been adopted to measure tourism potential under the present and possible future climatic conditions. CIT is a theoretically based and empirically derived index that allows assessing the sun, sea and sand (3S) weather resource. Daily observed series of 2 m maximum temperature, precipitation, 2 m relative humidity, cloud cover and 10 m wind speed have been used to derive the present climate potential for tourism. For future projections, daily averaged meteorological variables have been obtained from a set of regional climate models (RCMs) within the European ENSEMBLES project. The adoption of a multimodel ensemble strategy allows quantifying the uncertainties arising from model errors and boundary conditions. To use CIT values based on RCM data properly at local scales, a quantile-quantile adjustment has been applied. Results show a significant increase in the annual frequency of days with acceptable conditions together with a slightly increment for the ideal climate perceptions at the expense of decreasing unacceptable categories. For the summery peak season, ideal conditions are projected to decrease from mid-century favouring acceptable categories. However, an almost general increase for both classes is anticipated in spring and autumn throughout the century. That is, higher frequencies of optimal climate perceptions for carrying out 3S outdoors activities are expected to shift from the peak to shoulder seasons. Therefore, climate change would result in a major impact for the current seasonally adjusted service sector in SPdP. With this information at hand, policy makers and the team of experts planning its socioeconomic future can respond more effectively to the demanding challenge of local adaptation to climate change by implementing adaptation and mitigation strategies to the tourist sector. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: In this work Multivariate Adaptive Regression B-Splines (BMARS) is applied to regional spatio-temporal mapping of the Vertical Total Electron Content (VTEC) using ground based Global Positioning System (GPS) observations. BMARS is a non-parametric regression technique that utilizes compactly supported tensor product B-splines as basis functions, which are automatically obtained from the observations. The algorithm uses a scale-by-scale model building strategy that searches for B-splines at each scale fitting adequately to the data and provides smoother approximations than the original Multivariate Adaptive Regression Splines (MARS). It is capable to process high dimensional problems with large amounts of data and can easily be parallelized. The real test data is collected from 32 ground based GPS stations located in North America. The results are compared numerically and visually with both the regional VTEC modeling generated via original MARS using piecewise-linear basis functions and another regional VTEC modeling based on B-splines. [Copyright &y& Elsevier]

Abstract: We examine the results from a regional-scale chemical-transport model with 4-km resolution to determine the spatial variability of trace gases on this scale. Model-derived variability statistics are generated using 1st-order structure functions and then compared with in situ trace gas measurements from a series of aircraft campaigns. The variability of the observations and the model-derived concentrations are found to be in reasonable agreement for O3 and CO, but the model underestimates the observed variability of NO2. Variability statistics are then calculated for model-derived tropospheric column integrals. These integrals are calculated for 0–10 km (representative of the entire tropospheric column), 0–2 km (representative of the planetary boundary layer, PBL) and 2–10 km (representative of the free troposphere, FT). For each of the species examined, the variability of the tropospheric column is generally controlled by the variability in the lowest 2 km. The degree of control for each trace gas, however, is different. Whereas NO2 is completely dominated by PBL processes, CO variability in the FT contributes appreciably to the variability of the entire tropospheric column, suggesting that two independent pieces of information for CO would be most helpful for describing the variability of the entire tropospheric column. Likewise, the variability of an independent free tropospheric measurement of O3 would provide additional insight into the observed variability of the entire column, but the amount of additional information provided by a separate FT measurement is not as beneficial to what was found for CO. We provide additional analyses to quantify relationships that can be used to better understand the model-derived structure functions and their dependence on grid size and time of day. Lastly we present a practical example of how this information may be used for guidance in the development of science requirements for future satellite instruments since measurements from these instruments must be able to resolve smaller scale gradients to be used successfully for air quality applications. [Copyright &y& Elsevier]

Abstract: Soil organic carbon (SOC) is an important carbon pool which can ameliorate the increasing concentration of atmospheric carbon dioxide as part of the carbon cycling process. Organic carbon in cropland soils is an active pool which is strongly influenced by anthropogenic activities. SOC in cropland soils accounted for 14.5% of the total organic carbon stock of 89.61Mt C in China. In this study, RothC model was used to simulate the change of SOC in upland soils at 626 original 50km×50km grids under B2 and A2 climate scenarios during the upcoming decades in China. Future climate data under B2 and A2 scenarios were predicted by Providing Regional Climates for Impacts Studies (PRECIS) regional climate model downscaled based on HadCM3. The simulation results showed that SOC will generally decrease during the next decades and the decrease rate of SOC will be higher over time if there is no addition of organic material (e.g. organic manure application or straw return) adopted in China. Simulations got the following results: (i) SOC will decrease in most areas of China, especially in northern China; the increase of SOC only occurred in a few scattered grids in Southwest China and mid-south China. The decrease rate of SOC in northern China was higher than in southern China under either B2 or A2 climate scenarios. (ii) The changing rate of SOC over time under B2 scenario was minor during the early 21st century and quite large during the late 21st century. In contrast, the changing rate of SOC over time under A2 scenario was somewhat constant over time. The percentage decrease (decrease rate divided by its basic value in 1980s) of SOC in northern China was around 5.5%, 12%, and 15% by the year 2020, 2050, and 2080 under B2 and A2 scenarios, while it was about 2.3%, 7.7%, and 10.9% under B2 scenario and 3.3%, 4.5%, and 5.5% under A2 scenario in southern China. (iii) Upland soils would lose organic carbon by 2.7tC/ha, 6.0tC/ha, and 7.8tC/ha at the 0–30cm depth by the year 2020, 2050, and 2080, respectively, under the typical conventional tillage without organic material amendment under B2 scenario in China, which accounted for about 4.2%, 9.3%, and 12.1%, respectively, of the basic SOC of 1980s in the upland soils. SOC would decrease by 2.9tC/ha, 6.8tC/ha, and 8.2tC/ha by the year 2020, 2050, and 2080, respectively, under A2 scenario in China, which accounted for about 4.5%, 10.5%, and 12.7%, respectively, of that in 1980s under the conventional tillage and management practices. (iv) Our simulations of future change in SOC suggest it is the influence of combination by temperature, precipitation, evaporation, and original soil properties together and that the predominant factors and the interaction among these factors will vary in different geographical regions. The limitations and uncertainties of the simulation are also discussed. [Copyright &y& Elsevier]

Recently the Southwest has experienced a spate of dryness, which presents a challenge to the sustainability of current water use by human and natural systems in the region. In the Colorado River Basin, the early 21st century drought has been the most extreme in over a century of Colorado River flows, and might occur in any given century with probability of only 60%. However, hydrological model runs from downscaled Intergovernmental Panel on Climate Change Fourth Assessment climate change simulations suggest that the region is likely to become drier and experience more severe droughts than this. In the latter half of the 21st century the models produced considerably greater drought activity, particularly in the Colorado River Basin, as judged from soil moisture anomalies and other hydrological measures. As in the historical record, most of the simulated extreme droughts build up and persist over many years. Durations of depleted soil moisture over the historical record ranged from 4 to 10 years, but in the 21st century simulations, some of the dry events persisted for 12 years or more. Summers during the observed early 21st century drought were remarkably warm, a feature also evident in many simulated droughts of the 21st century. These severe future droughts are aggravated by enhanced, globally warmed temperatures that reduce spring snowpack and late spring and summer soil moisture. As the climate continues to warm and soil moisture deficits accumulate beyond historical levels, the model simulations suggest that sustaining water supplies in parts of the Southwest will be a challenge. [ABSTRACT FROM AUTHOR]

Mountain ranges are known to have a first-order control on mid-latitude climate, but previous studies have shown that the Andes have little effect on the large-scale circulation over South America. We use a limited-domain general circulation model (RegCM3) to evaluate the effect of the Andes on regional-scale atmospheric dynamics and precipitation. We present experiments in which Andean heights are specified at 250 m, and 25, 50, 75, and 100% of their modern values. Our experiments indicate that the Andes have a significant influence on moisture transport between the Amazon Basin and the central Andes, deep convective processes, and precipitation over much of South America through mechanical forcing of the South American low-level jet (LLJ) and topographic blocking of westerly flow from the Pacific Ocean. When the Andes are absent, the LLJ is absent and moisture transport over the central Andes is mainly northeastward. As a result, deep convection is suppressed and precipitation is low along the Andes. Above 50% of the modern elevation, a southward flowing LLJ develops along the eastern Andean flanks and transports moisture from the tropics to the subtropics. Moisture drawn from the Amazon Basin provides the latent energy required to drive convection and precipitation along the Andean front. Large northerly moisture flux and reduced low-level convergence over the Amazon Basin leads to a reduction in precipitation over much of the basin. Our model results are largely consistent with proxy evidence of Andean climate change, and have implications for the timing and rate of Andean surface uplift. [ABSTRACT FROM AUTHOR]

Abstract: Different algorithms have been proposed for the modeling of the ionosphere. The most frequently used method is based on the spherical harmonic functions achieving successful results for global modeling but not for the local and regional applications due to the bounded spherical harmonic representation. Irregular data distribution and data gaps cause also difficulties in the global modeling of the ionosphere. In this paper we propose an efficient algorithm with Multivariate Adaptive Regression Splines (MARS) to represent a new non-parametric approach for regional spatio-temporal mapping of the ionospheric electron density using ground-based GPS observations. MARS can handle very large data sets of observations and is an adaptive and flexible method, which can be applied to both linear and non-linear problems. The basis functions are directly obtained from the observations and have space partitioning property resulting in an adaptive model. This property helps to avoid numerical problems and computational inefficiency caused by the number of coefficients, which has to be increased to detect the local variations of the ionosphere. Since the fitting procedure is additive it does not require gridding and is able to process large amounts of data with large gaps. Additionally the model complexity can be controlled by the user via limiting the maximal number of coefficients and the order of products of the basis functions. In this study the MARS algorithm is applied to real data sets over Europe for regional ionosphere modeling. The results are compared with the results of Bernese GPS Software over the same region. [Copyright &y& Elsevier]

Abstract: Accurate determination of rooting depths in terrestrial biosphere models is important for simulating terrestrial water and carbon cycles. In this study, we developed a method for optimizing rooting depth using satellite-based evapotranspiration (ET) seasonality and an ecosystem model by minimizing the differences between satellite-based and simulated ET. We then analyzed the impacts of rooting depth optimization on the simulated ET and gross primary production (GPP) seasonality in California, USA. First, we conducted a point-based evaluation of the methods against flux observations in California and tested the sensitivities of the simulated ET seasonality to the rooting depth settings. We then extended it spatially by estimating spatial patterns of rooting depth and analyzing the sensitivities of the simulated ET and GPP seasonalities to the rooting depth settings. We found large differences in the optimized and soil survey (STATSGO)-based rooting depths over the northern forest regions. In these regions, the deep rooting depths (>3m) estimated in the study successfully reproduced the satellite-based ET seasonality, which peaks in summer, whereas the STATSGO-based rooting depth (<1.5m) failed to sustain a high ET in summer. The rooting depth refinement also has large effects on simulated GPP; the annual GPP in these regions is increased by 50–100% due to sufficient soil water during the summer. In the grassy and shrubby regions of central and southern California, the estimated rooting depths are similar to those of STATSGO, probably due to the shallow rooting depth in these ecosystems. Our analysis suggests that setting a rooting depth is important for terrestrial ecosystem modeling and that satellite-based data could help both to estimate the spatial variability of rooting depths and to improve water and carbon cycle modeling. [Copyright &y& Elsevier]