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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]

Forecasts of tropical cyclone (TC) tornadoes are less skillful than their non‐TC counterparts at all lead times. The development of a convection‐allowing regional ensemble, known as the Warn‐on‐Forecast System (WoFS), may help improve short‐fused TC tornado forecasts. As a first step, this study investigates the fidelity of convective‐scale kinematic and thermodynamic environments to a preliminary set of soundings from WoFS forecasts for comparison with radiosondes for selected 2020 landfalling TCs. Our study shows reasonable agreement between TC convective‐scale kinematic environments in WoFS versus observed soundings at all forecast lead times. Nonetheless, WoFS is biased toward weaker than observed TC‐relative radial winds, and stronger than observed near‐surface tangential winds with weaker winds aloft, during the forecast. Analysis of storm‐relative helicity (SRH) shows that WoFS underestimates extreme observed values. Convective‐scale thermodynamic environments are well simulated for both temperature and dewpoint at all lead times. However, WoFS is biased moister with steeper lapse rates compared to observations during the forecast. Both CAPE and, to a lesser extent, 0–3‐km CAPE distributions are narrower in WoFS than in radiosondes, with an underestimation of higher CAPE values. Together, these results suggest that WoFS may have utility for forecasting convective‐scale environments in landfalling TCs with lead times of several hours. Plain Language Summary: Landfalling tropical cyclones (known as tropical depressions, tropical storms, or hurricanes) often spawn tornadoes, which are more challenging to forecast than Great Plains tornadoes. The development of a weather forecast ensemble model, known as the Warn‐on‐Forecast system, may help improve tornado forecasts. This study compares vertical profiles of winds, temperature, moisture, and associated severe weather metrics from the Warn‐on‐Forecast with observed radiosonde data (i.e., balloon‐borne instruments) in landfalling tropical cyclones. Our analysis shows agreement between the observed and forecasted vertical structure of TC winds at all lead times including strong changes in near‐surface wind speed and direction. Nonetheless, forecasted tropical cyclone winds tend to be biased weaker than observed, which is associated with an underestimation of environmental favorability for tornadoes. Similarly, forecasted temperature and moisture are also reasonably represented compared to observations. However, Warn‐on‐Forecast tends to overestimate moisture and near‐surface temperature, with smaller‐than‐observed variability in thermodynamic environmental favorability. Together, these results suggest that the weather forecast model used here may be useful for improving forecasts of supercell environments in landfalling TCs. Key Points: Warn‐on‐Forecast tropical cyclone forecasts show reasonable agreement between observed and forecast convective‐scale kinematic environmentsForecast convective‐scale thermodynamic environments also show reasonable fidelity compared to observationsA case study shows stronger discrepancies than the full sample including weaker than observed winds, temperature inversions, and dry layers [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]

N2 fixation is a central process of the marine nitrogen cycle, yet little is known about how this process varies from year‐to‐year. Here, we investigate this variability in the Western Tropical Atlantic (WTA), a region where N2 fixation is prevalent, fueled, in part, by the nutrient input from the Amazon River. We use hindcast simulations from 1983 through 2019 with the Regional Oceanic Modeling System (ROMS) coupled to the Biogeochemical Elemental Cycling (BEC) model expanded to include Diatom‐Diazotroph Assemblages (DDAs). Throughout the WTA, we find a substantial level of interannual variability of N2 fixation, altering it by up to 33%, and locally by up to nearly 60%. Part of this interannual variability is driven by variations in the Amazon River discharge, which during high discharge events leads to reduced rates in the upper parts of the plume and strongly enhanced rates in the downstream part. This dipole pattern is a consequence of the riverine inputs of phosphorus and the competition with non‐diazotrophs for this limiting resource. Another part of the N2 fixation variability is driven by the Atlantic Meridional Mode (AMM), and the El Niño‐Southern Oscillation (ENSO). These processes alter N2 fixation primarily through the supply of the limiting nutrients phosphorus and iron by vertical mixing, while the role of top‐down control through grazing is relatively limited in our model. The high, and so far not well recognized interannual N2 fixation variability can lead to erroneous extrapolation of field measurements and inaccuracies in the marine nitrogen budget, if unaccounted for. Plain Language Summary: Nitrogen fixation is a vital biological process in which dinitrogen is transformed into bioavailable nitrogen, which supports phytoplankton growth and ultimately maintains oceanic food webs. The amount of nitrogen fixed in this way can vary substantially due to a complex interplay of changes in environmental conditions, but this variability remains poorly constrained. Here, we focus on the year‐to‐year variability of nitrogen fixation that occurs in the Western Tropical Atlantic, where nitrogen fixation rates are particularly high due to the input of nutrients from the Amazon River. We use a regional ocean model that includes two types of nitrogen‐fixing organisms and analyze the variability from 1983 through 2019. We find that nitrogen fixation in the Western Tropical Atlantic deviates up to a third from the mean. These deviations are related to Amazon River discharge fluctuations and climate variability, in particular the El Niño—Southern Oscillation and the Atlantic Meridional Mode. Discharge and climate variability impact the availability of nutrients, which controls the growth and activity of nitrogen‐fixing organisms. In contrast, zooplankton predation does not drive variations in nitrogen fixation. These findings have implications for the marine nitrogen cycle and underscore the importance of better constraining interannual variability in nitrogen fixation. Key Points: Marine N2 fixation in the Western Tropical Atlantic varies substantially from year‐to‐year, up to a third of the mean rateIn the Amazon River plume, variability is primarily caused by variations in Amazon River discharge, altering the supply of phosphorusIn most of the Western Tropical Atlantic, the Atlantic Meridional Mode and ENSO drive variability by altering the upward nutrient mixing [ABSTRACT FROM AUTHOR]

Changes in tropical cyclones due to greenhouse‐gas forcing in the Shanghai area have been studied in a double‐nesting regional model experiment using the Met Office convection‐permitting model HadREM3‐RA1T at 4 km resolution and the regional model HadREM3‐GA7.05 at 12 km for the intermediate nest. Boundary conditions for the experiment have been constructed from HadGEM2‐ES, a General Circulation Model (GCM) from the 5th Coupled Model Intercomparison Project (CMIP5), directly using high‐frequency data for the atmosphere (6‐hourly) and the ocean (daily), for the historical period (1981–2000) and under the Representative Concentration Pathway 8.5 (2080–2099). These choices identify one of the warmest climate scenarios available from CMIP5. Given the direct use of GCM data for the baseline, large scale conditions relevant for tropical cyclones have been analyzed, demonstrating a realistic representation of environmental conditions off the coast of eastern China. GCM large scale changes show a reduction in wind shear in addition to the expected strong increase in sea‐surface temperature. Tropical cyclones from the 4 km historical simulation have a negative bias in intensity, not exceeding Category 4, and a wet bias in the rainfall associated with these cyclones. However, there is a clear improvement in cyclone intensity and rainfall at 4 km in comparison with the 12 km simulation. Climate change responses in the 4 km simulation include an extension of the tropical cyclone season, and strong increases in frequency of the most intense cyclones (approximately by a factor of 10) and associated rainfall. These are consistent with the results from the 12 km simulation. Plain Language Summary: Since global climate models do not have the spatial resolution to simulate tropical cyclones, higher resolution limited area models are commonly used to improve the representation of these phenomena. In this study, where changes in the Shanghai area have been estimated, this was done in two steps, using a regional model at 12 km resolution as the intermediate step to finally reach a 4 km resolution for the region around Shanghai with a model which can explicitly represent convection processes. This was done by using one of the warmest climate scenarios available from the climate projections run under realistic increases of greenhouse‐gas concentrations, to study climate change at the end of this century. The global climate model reproduces large scale conditions relevant for tropical cyclones which compare well with observations in the historical period and projects changes in the atmosphere and the ocean which are relevant for tropical cyclones. The downscaled model at 4 km reproduces the observation with some biases in intensity, not sufficient to reach Category 4, and in the cyclone precipitation, through marked improvements with respect to the 12 km model. The climate change response shows a strong increase in intense tropical cyclone frequency and rainfall, common to both models. Key Points: Double‐nesting experiment with a 4 km convective scale model driven by a high end Coupled Model Intercomparison Project general circulation model to study changes in tropical storms near ShanghaiImprovement at 4 km over the intermediate 12 km nest on rainfall and intensity of tropical storms, but not sufficient to reach category 4Future changes at 4 km show a marked increase in frequency of intense tropical cyclones and in rainfall, well matched by the 12 km model [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]

Marine heatwaves (MHWs) have been recognized as a serious threat to marine life, yet, most studies so far have focused on the surface only. Here, we investigate the vertical dimension and propagation of surface MHWs in the Eastern Pacific (EP) using results from a high‐resolution hindcast simulation (1979–2019), performed with the Regional Ocean Modeling System. We detect MHWs using a seasonally varying percentile threshold on a fixed baseline and track their vertical propagation across the upper 500 m. We find that nearly a third (∼29%) of the MHWs extend beyond the surface mixed layer depth (MLD). On average, these deep‐reaching MHWs (dMHWs) extend to 110 m below the MLD and last five times longer than MHWs that are confined to the mixed layer (184 vs. 36 days). The dMHWs can cause stronger temperature anomalies at depth than at the surface (maximum intensity of 5.0 vs. 1.9°C). This general subsurface MHW intensification even holds when scaling the temperatures with the respective local variability. A clustering of dMHWs reveals that 41% of them are block‐like, that is, continually remain in contact with the sea surface, 24% propagate downward, 20% propagate upward, while 15% appear at the surface multiple times. Although the water column MHW duration, intensity and severity are only moderately correlated with their corresponding surface‐based MHW characteristics, dMHWs have the potential to be detected from the surface. Our study can help to augment the remote sensing‐based monitoring of upper ocean exposure to MHWs. Plain Language Summary: Periods of extremely warm water temperatures, referred to as marine heatwaves (MHWs), pose a serious threat to marine life. While many studies have analyzed MHW properties at the sea surface, little is known about their subsurface nature. We here use a new approach to define MHWs throughout the water column and study the vertical structure and propagation behavior of MHWs in the Eastern Pacific based on a high‐resolution numerical hindcast simulation (1979–2019). We find that nearly a third of MHWs that are discernible at the sea surface reach below the layer of well‐mixed waters near the sea surface, the so‐called mixed layer (ML). These deep‐reaching MHWs (dMHWs) last on average longer than ML confined MHWs (184 vs. 36 days) and are marked by larger temperature threshold exceedances (maximum intensity of 5.0 vs. 1.9°C). We furthermore identify four distinct vertical propagation patterns associated with the dMHWs: 41% continually affect the sea surface, 24% propagate downwards, 20% propagate upward, while 15% appear at the surface multiple times. Overall, these analyses show that MHWs can affect the water column to a larger degree than diagnosed based on the sea surface only. Key Points: About a third of marine heatwaves (MHW) in the Eastern Pacific extend below the mixed layer. Ten percent of MHWs extend on average deeper than 150 m59% of deep‐reaching MHWs (dMHWs) are partly invisible at the surface, as they either deepen, shoal or exhibit multi‐surfacing behaviorAs initial tests indicate that the surface‐based detection of dMHWs seems feasible, dMHWs might be detectable using remote sensing data [ABSTRACT FROM AUTHOR]

A month‐long data assimilation experiment is carried out to assess the impact of CrIS and IASI Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3‐hr Weather Research and Forecasting cycling system implemented over the central North Pacific Ocean. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System and the European Centre for Medium‐Range Weather Forecasts analyses are used in the evaluation process. The results show that the assimilation of TRs alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800–300 hPa), and particularly in the lower portion (800–600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade‐wind inversion. Plain Language Summary: A month‐long data assimilation experiment is carried out to assess the impact of hyper‐spectral sensor Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3‐hr Weather Research and Forecasting cycling system implemented over the central North Pacific Ocean. TRs are the result of a mathematical inversion process that compresses the informational content of the hyper‐spectral radiances into a limited number of uncorrelated parameters, and provides an ad hoc observation operator, for their assimilation within Numerical Weather Prediction models. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System and the European Centre for Medium‐Range Weather Forecasts analyses are used in the evaluation process. The results show that the assimilation of TRs, both alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800–300 hPa), and particularly in the lower portion (800–600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade‐wind inversion. Key Points: Hyperspectral radiances are efficiently compressed into a limited number of uncorrelated parameters: the Transformed Retrievals (TRs)Assimilation of TRs is akin to assimilation of physical profilesThe assimilation of TRs results in a higher forecast accuracy in predicting the distribution of the water vapor [ABSTRACT FROM AUTHOR]

The energetics in the Bay of Bengal (BoB) region is influenced by primary and secondary mesoscale ocean processes, such as boundary currents, gyres, and eddies. Using the turbulent kinetic energy budget equation, the fluxes and their role in BoB dynamics are studied. A regional domain with a horizontal resolution of 0.1 ∘ is considered in the Indian Ocean, which includes the BoB region. The analysis of flow energetics reveals differences in eddy kinetic energy (K), shear production flux (P), buoyancy flux (B), and viscous dissipation (ε) terms. Our results reveal the presence of an inverse energy-cascade mechanism in the southern Bay of Bengal, wherein energy flows back into mean flow structures from the turbulent eddies. Further analysis shows that buoyancy flux, B, is the major source of turbulence production in the north and central BoB regions. These results define the mechanisms by which energy is produced and transferred in different regions in the Bay of Bengal. [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]

Gas phase hydrogen chloride (HCl) was measured at Pasadena and San Joaquin Valley (SJV) ground sites in California during May and June 2010 as part of the CalNex study. Observed mixing ratios were on average 0.83 ppbv at Pasadena, ranging from below detection limit (0.055 ppbv) to 5.95 ppbv, and were on average 0.084 ppbv at SJV with a maximum value of 0.776 ppbv. At both sites, HCl levels were highest during midday and shared similar diurnal variations with HNO3. Coupled phase partitioning behavior was found between HCl/Cl− and HNO3/NO3− using thermodynamic modeling and observations. Regional modeling of Cl− and HCl using Community Multiscale Air Quality captures some of the observed relationships but underestimates measurements by a factor of 5 or more. Chloride in the 2.5–10 μm size range in Pasadena was sometimes higher than sea salt abundances, based on co‐measured Na+, implying that sources other than sea salt are important. The acid‐displacement of HCl/Cl− by HNO3/NO3− (phase partitioning of semi‐volatile acids) observed at the SJV site can only be explained by aqueous phase reaction despite low RH conditions and suggests the temperature dependence of HCl phase partitioning behavior was strongly impacted by the activity coefficient changes under relevant aerosol conditions (e.g., high ionic strength). Despite the influence from activity coefficients, the gas‐particle system was found to be well constrained by other stronger buffers and charge balance so that HCl and Cl− concentrations were reproduced well by thermodynamic models. Plain Language Summary: Despite the known importance of gas‐particle partitioning in chlorine activation chemistry, the sources and atmospheric fate of hydrogen chloride (HCl) and particulate Cl− require further study. HCl concentrations and the corresponding phase partitioning mechanisms with Cl− in PM2.5 were studied through field measurements and modeling as part of the CalNex field campaign. The measurements confirmed that gas phase HCl was the dominant component of atmospheric chlorine and suggested that even though the current modeling can explain the diurnal variation of HCl, the mixing ratios were severely underestimated. Temperature dependence of Cl− replacement reaction by HNO3 was observed at one site. This study suggests further model development and field observations of HCl, as well as more reliable understanding of HCl phase partitioning behavior are needed to better predict the atmospheric fate of HCl and Cl−. Key Points: Hydrogen chloride (HCl) was measured at two CalNex sites. At both, the diurnal variation of HCl was similar and HCl was correlated with HNO3 on a given dayThe Community Multiscale Air Quality model captures HCl diurnal variation but underestimates the atmospheric chlorine concentrations by at least a factor of 5The aqueous phase exchange reaction drove coupled partitioning behavior between HCl/Cl− and HNO3/NO3− at one site despite low humidity [ABSTRACT FROM AUTHOR]

Hydrological modeling is nowadays critical for evaluating the status, past trends, and future perspectives of water availability at the global, regional, and local scales. The Iberian Peninsula is registering more frequent and severe droughts and water scarcity caused not only by extreme meteorological events, but also by increased demand for water for urban, industrial, and agricultural supplies. Better simulation models are thus needed for accurately quantifying the availability of local water resources. In this study, the natural flow regime in different watersheds of the Iberian Peninsula was simulated using the process-based, fully distributed, MOHID-Land model from 1979 to 2013. Streamflow results were compared with measurements at 73 hydrometric stations not influenced by reservoirs, and with the data available in the management plans of each hydrographic region. The results showed a high dispersion of the goodness-of-fit indicators, with the coefficient of determination (R2) ranging between 0 and 0.91, and the modeling efficiency (NSE) being higher than 0.35 at only 22 (calibration) and 28 (validation) hydrometric stations. Considering the scale of application, results were acceptable but evidenced the difficulties in simulating streamflow in watersheds using a coarse resolution. As such, this paper further deals with the difficulties and challenges of the adopted modeling approach. Nevertheless, this study constitutes a further step towards the more accurate assessment of water resources availability at the Iberian Peninsula scale using process-based modeling. [ABSTRACT FROM AUTHOR]

The Long Island Sound (LIS) Tropospheric Ozone Study was a multi‐agency collaborative field campaign conducted during the summer of 2018 to improve the understanding of ozone chemistry and transport from New York City to areas downstream, especially the LIS and adjacent Connecticut coastline. Measurements made during this campaign were leveraged to test and evaluate the coupled WRF‐CMAQ model at 12 km, 4 and 1.33 km horizontal grid spacing. Special attention was placed on the model's representation of sea breeze circulations, low level jets, and boundary layer evolution. The evaluation suggests using higher resolutions resulted in improved surface meteorology statistics throughout the whole summer, with temperature biases seeing the biggest statistical improvements when using 1.33‐km grid spacing, going from −0.12 to 0.08 K. Additionally, 4‐km grid spacing provided the biggest advantage when simulating ozone over the region of interest, with biases being reduced from 2.40 to 0.57 to 0.37 ppbV with increased resolution. Case studies of two high ozone concentration events (July 10 and August 6) revealed that sound breezes and low‐level jets had a critical role in transporting pollutant‐rich, shallow marine air masses from the LIS inland over the Connecticut coast. Modifications were made to the representation of sea surface temperatures, which subsequently improved the simulation of surface ozone predictions. Plain Language Summary: Surface‐level ozone pollution across the country is closely monitored by the U.S. Environmental Protection Agency due to the threat it can pose to public health and safety. Ozone is not emitted naturally; instead it forms when other pollutants are emitted and exposed to sunlight. Ozone pollution can be especially harmful near large urban areas like New York City where high emissions of pollutants and local meteorology interact to produce ozone within the city and transport it downwind across the Long Island Sound into Connecticut. In this study, we used a weather and chemistry model to explore how surface winds over the LIS impact high ozone along the Connecticut and Long Island coastlines. Our work highlighted the importance of accurately modeling meteorological conditions so that we can adequately represent high ozone events in models. We also found that ozone over Connecticut is especially sensitive to where and when local wind features like sound breezes and low‐level jets form and dissipate. Continued work in these areas of study can help policy makers develop strategies that control all the factors that cause ozone pollution in this area. Key Points: High‐res WRF‐CMAQ model simulations showed improvements to surface meteorology and ozone over the NYC‐Connecticut‐Long Island Sound regionSound breezes along the CT and Long Island coasts were found to be instrumental to the modeled transport of ozone and its precursorsIncorporating diurnal variations to sea surface temperatures improved the representation of transport and surface ozone mixing ratios [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]

Dynamical downscaling remains a powerful tool for studying regional climate processes, and the genesis of high‐resolution historical and future climate data. This technique is particularly important over areas of complex terrain, such as the western United States (WUS), where global models are especially limited in representing regional climate. After identifying a suite of WRF options that best simulate snow and precipitation for an average water year (2010) over the WUS, we evaluate the performance of the dynamically downscaled European Centre for Medium‐range Weather Forecasting's fifth Reanalysis (ERA5) from 1980 to 2020 on 45‐km, 9‐km, and two 3‐km grids. We find that by decreasing the horizontal grid spacing within WRF, improvements to Sierra Nevada and Northern Rocky Mountain snow, Santa Ana and Diablo winds, and coastal meteorology occur. For landfalling atmospheric rivers (ARs), the downscaled reanalysis simulates greater upstream integrated vapor transport (IVT) than ERA5. However, WRF skillfully simulates the positioning of the IVT and the timing and magnitude of AR precipitation. This potential IVT bias, in conjunction with increasing resolution, leads to a wet precipitation bias across the Sierra Nevada in the 3‐km experiment. This conclusion is supported by streamflow analysis, although we note that the bias in the 3‐km experiment can also be explained by in situ undercatch issues. Meanwhile, the 9‐km experiment is more biased than the 3‐km experiment across the Northern Rocky Mountains compared to in situ measured SWE and precipitation, indicating a geographic sensitivity to biases. Plain Language Summary: Atmospheric reanalyses are often used to evaluate the performance of global climate models (GCMs). Reanalyses have a model component, but they also incorporate observations in post‐processing to orient the final product about reality. The horizontal resolution of reanalyses is too low to resolve small landscape features that control localized meteorology and climate. Thus, scientists are in need of an even higher resolution modeling framework. It is therefore necessary to embed a regional climate model (RCM), a tool that uses physics to simulate weather in higher resolution, within the reanalysis to generate kilometer‐scale weather and climate information. This process is called dynamical downscaling. Using this process, we show that, in spite of increased resolution, selecting the wrong RCM configurations can lead to poor RCM performance compared to observations, and we identify a superior set of RCM options for simulating weather and climate across the western United States. Following our tests, we find that the RCM adequately reproduces the historical weather and climate (1980–2020), and RCM accuracy improves with increasing model resolution. In creating this product, we now have a benchmark by which to evaluate the fidelity of dynamically downscaled GCMs, as we seek to extend this modeling framework to future climate. Key Points: A tractable method for dynamically downscaling the historical record across the western United States is presentedThorough testing of regional climate model (RCM) options is imperative before dynamically downscaling over climate time scalesHigher‐resolutions in RCMs should generally add value when considering a diverse array of simulated atmospheric phenomena [ABSTRACT FROM AUTHOR]

Forest restoration positively affects rural economies by facilitating employment and income generation with logging, wood utilization, and other restoration activities. To investigate economic effects and modeling of forest restoration, a regional contribution analysis of the Four Forest Restoration Initiative (4FRI) in Arizona was conducted. With over 12,000 acres mechanically thinned in 2017, 4FRI treatments led to the processing of 400,000 green tons of sawlogs and biomass. Restoration activities spurred more than 900 full-time equivalent jobs in the region, $50 million in regional labor income, and affected over 140 different industry sectors in the region. When compared to the US Forest Service Treatments for Restoration Economic Analysis Tool model estimates for 4FRI economic contributions, we found that using primary data from 4FRI contractors provided more conservative results. Primary considerations for modeling forest restoration contributions include contractor surveys, appropriate investigation of the regional context, methodological transparency in bridging restoration expenditures to input-output models, and consideration of how to enhance restoration contributions. [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]

Circulation and water mass transformation within the Filchner‐Ronne Ice Shelf (FRIS) cavity create precursors to Antarctic bottom water, which closes the global overturning circulation. This water mass transformation is contingent upon a relative low rate of FRIS basal melt, currently around 100–200 Gt/yr. Previous studies have indicated that Antarctic climate changes may induce intrusions of warm modified Warm Deep Water (mWDW) and an order‐of‐magnitude increase in basal melt, and signatures of mWDW have recently been observed along the face of the FRIS. However, it remains unclear how changes in near‐Antarctic climate translate mechanistically to changes in mWDW access to the FRIS cavity. In this study a regional model is developed to investigate FRIS circulation dependence on local atmospheric state. Experiments with modified initial cavity conditions but identical atmospheric states yield bistable "warm" and "cold" FRIS cavity states, with an order‐of‐magnitude difference in basal melt rates. Idealized atmospheric perturbation experiments reveal that relatively modest perturbations to the katabatic winds shift the FRIS cavity between "warm" and "cold" states, which occur when the FRIS cavity is filled by mWDW or High Salinity Shelf Water (HSSW), respectively. The authors present a conceptual model in which the FRIS cavity state is determined by whether mWDW or HSSW is denser and thus floods the cavity; these states are bistable because the basal melt rate feeds back on the salinity of HSSW. These findings highlight a key role for the katabatic winds in mediating the melt of the FRIS and other Antarctic ice shelves. Plain Language Summary: The Filchner‐Ronne Ice Shelf (FRIS) is Antarctica's largest floating glacier and is the source of the ocean's densest waters. Previous studies have predicted that warmer waters from offshore may flow beneath the FRIS in the future, elevating its melt rate and suppressing formation of dense waters. In this study, the controls on this warm water inflow are investigated using simulations of the southern Weddell Sea (WS), imposing various perturbations to the atmospheric winds and temperature. The ocean beneath the FRIS is found to be "bistable": Depending on its initial conditions, the ocean beneath the FRIS adjusts either to a "cold" state in which it is filled by waters originating from the WS continental shelf or to a "warm" state in which it is filled by waters originating from offshore. A switch between warm and cold states can be forced by changing the winds that blow northward over the FRIS; together with FRIS meltwater, these winds influence the density of cold waters formed on the WS continental shelf. These findings highlight a key role for offshore winds in mediating the melt of the FRIS and indicate that existing dense waters beneath ice shelves may buffer against future intrusions of warm water. Key Points: The Filchner‐Ronne Ice Shelf cavity supports bistable warm and cold states under identical atmospheric forcing in a regional modelThe Filchner‐Ronne Ice Shelf cavity is flooded by the denser of HSSW (cold) or WDW (warm), which in turn sets the melt rateModest changes in offshore winds can switch between cold and warm states by modifying the density of High Salinity Shelf Water [ABSTRACT FROM AUTHOR]

According to IPCC reports, global climate change is likely to be accompanied by a greater frequency, intensity, and duration of heat waves in urban areas. This is related to predicted and ongoing variation of atmospheric temperature and its association with the dynamical evolution of cities. Changes in the roughness pattern of the surface, wind intensity, soil available humidity and radiative properties compared to the natural surfaces characterize the formation of the Urban Heat Island (UHI). A dynamical downscaling of A2 and B1 SRES's future scenarios from Intergovernmental Panel on Climate Change were performed for Londrina, a medium-size city of Southern Brazil, using the Weather Research and Forecasting model. The main objective of this study is to investigate the impact of these scenarios on the UHI formation and intensity based on different input data, and its role and influence in the rural area. For this, an evaluation of the model and a comparison with the scenarios were done to mitigate the current trends. The results show a tendency in the current situation in following the pessimistic A2 scenario. Also, a drier rural area for the sustainable projection (B1) is found which implicates in a higher temperature and wind patterns modification for both sites, urban and rural region. Both future projections have a direct influence on the UHI intensity and formation, yielding effects in the agriculture and affecting conditions on human comfort over the region. [ABSTRACT FROM AUTHOR]

Mountain snow has a fundamental role in regional water budgets through its seasonal accumulation, storage, and melt. However, characterizing snow accumulation over large regions remains difficult because of limited observational networks and the inability of available satellite instruments to remotely sense snow depth or water equivalent in mountains. Models offer some ability to estimate snow water storage (SWS) on mountain range to continental scales. Here we compare four commonly used global data sets to understand whether there is a consensus regarding mountain SWS estimates among them. The data sets—European Centre for Medium‐Range Weather Forecasts Reanalysis‐Interim, Global Land Data Assimilation System, Modern‐Era Retrospective Analysis for Research and Applications version 2, and Variable Infiltration Capacity—agree to within ±36% of the four–data set average for total global SWS. When mountain areas are extracted using a new seasonal mountain snow classification data set, the four data products have more agreement, where all are within ±21% of the seasonal SWS for mountain regions. However, when compared to high‐resolution (9 km) simulations of SWS from the Weather Research and Forecasting (WRF) regional model, the four global products differ from WRF‐estimated North American mountain snow accumulation by 40–66%, with a negative bias up to 651 km3, comparable to the annual streamflow of the Mississippi River. If we extend the North America SWS bias to global mountains, the global data sets may miss as much as 1,500 km3 of SWS, equivalent to 4% of the flow in all the world's rivers. The potential difference of SWS suggests more work must be done to characterize water resources in snow‐dominated regions, particularly in mountains. Key Points: Based on a novel seasonal mountain snow mask, mountains with seasonal snow account for 31% of global snow cover and 9% of land areaGlobal data sets estimate more than 1,100 km3 of snow storage in global mountainsCompared to regional model estimates, global data sets may underestimate mountain snow storage by 1,500 km3, a bias exceeding 50% [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]

Providing skilful predictions of high‐impact weather up to 2 weeks ahead is on the agenda of international weather centers. Evaluating the capabilities of current numerical systems in predicting past events can bring extremely valuable contributions to the assessment of the information content available today with operational models. In the framework of the activities for the fiftieth anniversary of the extreme precipitation event that occurred in Italy in November 1966, this paper investigates its predictability using state‐of‐the‐art global and regional ensemble simulations. The first goal is to assess if and how many days in advance, this event can be predicted by current European Centre for Medium‐Range Weather Forecasts (ECMWF) global ensembles. A second goal is to evaluate the potential added value of running nested higher‐resolution and convection‐permitting ensembles. It is shown that ECMWF ensembles are able to provide valuable information up to 3 days before the event. Within this forecast range, convection‐permitting simulations can provide more accurate estimations of precipitation maxima. However, the results indicate also a strong underestimation of rainfall amounts with both global and regional models even at short forecast range. To partially explain this shortcoming, we discuss how the scarcity of observations available in 1966 for the analysis process limits the quality of the ensemble initial conditions and we adopt a method to obtain more reliable ensemble forecasts. The paper concludes with a comparison with previous similar works; results indicate a gain in predictability of up to 12 hr with respect to numerical revisitations performed to mark the fortieth anniversary of the event. Key Points: Current state‐of‐the‐art global ensembles are able to provide valuable information up to 3 days before the Arno River flooding event (Italy, November 1966)Convection‐permitting ensembles provide more accurate estimations of precipitation maximaAdvances in numerical modeling occurred in the last 10 years, determine a gain in predictability of up to 12 hr with respect to similar works [ABSTRACT FROM AUTHOR]

Characterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle. Key Points: Comparisons to regional climate model simulations suggest that global data products underestimate cool‐season precipitation at the watershed scalePrecipitation underestimation is largely driven by biases within mountainous portions of watershedsGlobal data sets additionally underestimate snow accumulation in the mountains [ABSTRACT FROM AUTHOR]

This paper outlines the development of inputs to an integrated land use and transportation model based on a series of environmentally extended social accounting matrices (SAMs) for the Canadian province of Alberta. A novel form of industry disaggregation is employed, based on aggregate iterative proportion and a unique formulation of location quotients. Social accounts are extended via the inclusion of detailed household consumption broken down by income quintiles. The SAMs are developed from supply-use matrices. Physical flow accounts are framed as derived demands, acting as necessary inputs to the production of downstream goods and services. Applications to regional economic modeling are considered, as planning authorities increasingly seek to model the environmental impacts of policy. The SAMs are then applied to the assessment of two technology change scenarios: a shift in the provincial electricity generation mix and a transition to a fully electric private automobile fleet. [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]

Abstract: Tropical cyclone (TC) 05A (TC05A, November 2011) over the Indian Ocean originated near the equator and brought huge damages to Sri Lanka and India. The genesis and propagation of TC05A involves interactions with the Madden–Julian Oscillation (MJO), interannual (IA) variations, and other synoptic (SY) systems. To investigate the impacts of the SY, MJO, and IA forcings across the lateral boundaries on TC05A, regional simulation with the Weather Research and Forecast model through the partial lateral forcing approach is conducted. The control experiment reproduces realistic genesis and movement of TC05A and associated heavy rainfall, as well as the mean states and MJO. Both the SY and IA forcings make a moderate contribution to the initiation of cyclonic vorticity and rainfall enhancement in the central Indian Ocean and moderate modification of the TC05A track. However, it is the MJO forcing that fundamentally reproduces a coherent moist, low‐level convergence, and a cyclonic environment that is favorable for the development of TC05A. The greatest impact of northern boundary intraseasonal (IS) forcing on the genesis location and path of TC05A is further identified, suggesting the importance of the interaction between the MJO and northeast winter monsoon. In contrast, the southern boundary IS forcing is unimportant and the western and eastern boundary IS forcings play a moderate role in slowing down TC05A and modifying its track, intensity, and rainfall. This study demonstrates that the partial lateral forcing method is an effective tool to identify the key factors controlling the extreme events through regional climate modeling. [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]

Every 2-7 years, El Niño events trigger a strong decrease in phytoplankton productivity off Peru, which profoundly alters the environmental landscape and trophic chain of the marine ecosystem. Here we use a regional coupled physical-biogeochemical model to study the dynamical processes involved in the productivity changes during El Nino, with a focus on the strongest events of the 1958-2008 period. Model evaluation using satellite and in situ observations shows that the model reproduces the surface and subsurface interannual physical and biogeochemical variability. During El Niño, the thermocline and nutricline deepen significantly during the passage of coastal-trapped waves. While the upwelling-favorable wind increases, the coastal upwelling is compensated by a shoreward geostrophic near-surface current. The depth of upwelling source waters remains unchanged during El Niño but their nutrient content decreases dramatically, which, along with a mixed layer depth increase, impacts the phytoplankton growth. Offshore of the coastal zone, enhanced eddy-induced subduction during El Niño plays a potentially important role in nutrient loss. [ABSTRACT FROM AUTHOR]

Thermal breakthrough is the main factor that affects the long-term efficiency of groundwater heat pump systems that predominantly for heating, particularly in northern China. A large water-source heat pump engineering project in Tangshan, China, is examined in this study. The project involves 18 pumping wells with a yield of 80 m3/h and 40 injection wells, covering a total planning area of 445,000 m2. In the third year, a groundwater monitoring system is installed in 23 wells to monitor the groundwater temperature. A hydrothermal coupling numerical model is established with the non-isothermal multiphase migration software TOUGH2. The model is analyzed by applying it to simulations. Simulation results are validated through a comparison with monitoring data. Results of the 10-year operation simulation show that the A3 and A4 well groups experience severe cold accumulation. Three optimization scenarios are thus presented to improve the groundwater temperature. Simulation results show that using the aquifer thermal energy storage method could fully meet the future requirements of heating during winter; however, the cost of this method is high. The optimization effect of alternating the pumping and the injection wells is insignificant. Adopting different pumping and injection layers can effectively control the water temperature decrease in the pumping wells; however, the breakthrough of cold plume should be prevented. The results of this study could provide a reference for the numerical prediction of large-scale water-source heat pump engineering projects in northern China. [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]

ABSTRACT Regional precipitation recycling is the measure of the contribution of local evaporation E to local precipitation. This study provides a set of two methods developed in the Weather Research and Forecasting WRF model system for investigating regional precipitation recycling mechanisms: (1) tracking of tagged atmospheric water species originating from evaporation in a source region, ie E-tagging, and (2) three-dimensional budgets of total and tagged atmospheric water species. These methods are used to quantify the effect of return flow and nonwell vertical mixing neglected in the computation of the bulk precipitation recycling ratio. The developed algorithms are applied to a WRF simulation of the West African Monsoon 2003. The simulated region is characterized by vertical wind shear condition, i.e., southwesterlies in the low levels and easterlies in the mid-levels, which favors return flow and nonwell vertical mixing. Regional precipitation recycling is investigated in 100 × 100 and 1000 × 1000 km2 areas. A prerequisite condition for evaporated water to contribute to the precipitation process in both areas is that it is lifted to the mid-levels where hydrometeors are produced. In the 100 × 100 (1000 × 1000) km2 area the bulk precipitation recycling ratio is 0.9 (7.3) %. Our budget analysis reveals that return flow and nonwell vertically mixed outflow increase this value by about +0.2 (2.9) and +0.2 (1.6) %, respectively, thus strengthening the well-known scale-dependency of regional precipitation recycling. [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]

Groundwater withdrawals can reduce aquifer-to-stream flow and induce stream-to-aquifer flow. These effects involve potential threats over surface water and groundwater quantity and quality. As a result, the description of stream-aquifer flow in space and time is of high interest for water managers. In this study, the EauDyssée platform, an integrated groundwater/surface water model is extended to provide the distribution of stream-aquifer flow at the regional scale. The methodology is implemented over long periods (17 years) in the Seine river basin (76 375 km, France) with a 6 481 km long simulated river network. The study scale is compatible with the scale of interest of water authorities, which is often larger than study scales of research projects. Net and gross stream-aquifer exchange flow are computed at the daily time step over the whole river network at a resolution of 1 km. Simulation results highlight that a major proportion of the main stream network (82 %) is supplied by groundwater. Groundwater withdrawals induce a reduction of net aquifer-to-stream flow (−19 %) at the basin scale and flow reversals in the vicinity of pumping locations. Such an integrated model provided at the appropriate regional scale is an essential tool provided to water managers for the implementation of the EU Water Framework Directive. [ABSTRACT FROM AUTHOR]

The middle basin of Heihe River has witnessed rapid urban growth and excessive agricultural activities during the last two decades, mainly because of its economic development and increasing population pressure. In this study, we aimed to understand the growth dynamics of the region, to forecast its future expansion, and to provide a basis for regional management. We calibrated and validated a SLEUTH model with historical data derived from different sources, which comprised remotely sensed and strategic planning data records from 1995, 2000, 2005, and 2009. Three scenarios based on local regional ecological planning were designed to simulate the spatial pattern of urban growth in different conditions. The first scenario allowed urban expansion without any additional managed growth limitations and the continuation of the actual historical trend. The second scenario was limited based on environmental considerations and managed growth was assumed with moderate protection. The third scenario simulated managed growth with strict protection on wetland reserves and productive agricultural areas in the study area. We consider that the results of these models of growth in the study area obtained under different scenarios are of great potential use to city managers and stakeholders. We also suggest that scale sensitivity and spatial accuracy are among the factors that must be considered in practical applications. We urge future researchers to build on the present study to produce models for similar regions in northwest China. [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]

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]

Large thermal extractions and extensive implementation of groundwater heat pumps (GWHP) necessitate a validation of the sustainability of their use and possible detrimental effects on groundwater. The goal of this work is to develop a regional heat transport model (of ~13 km × 5 km) for real site conditions. This model should consider all relevant transport processes, despite the large area under investigation. The model is based on a two-dimensional, transient-calibrated groundwater flow model for the “Leibnitzer Feld” (Styria, Austria). The two-dimensional horizontal model is linked via the FEFLOW interface manager with a newly developed “Multi-Layer-Model”-tool, which reproduces thermal aquifer–atmosphere interaction. Based on the regional heat transport model, scenarios are delineated for heating and cooling purposes for large GWHPs, which are appropriate for a small manufacturing business, an administrative building and 10 family homes. First of all, these have large spacing and thereafter, effects of area-covering usage of geothermal systems are evaluated for five administrative buildings located in close proximity to one another (200–350 m) and also for a large number of smaller heat extractions (each representing a one family house system). Modeled spatial and temporal temperature effects on the shallow aquifer are discussed. It was possible to present a simulation of realistic heating and cooling scenarios. This simulation may be introduced into practice once some further simplifications to the system are made. Locally limited heat plumes (max. length: 625 m) were observed for the manufacturing business. Any thermal effects coming from the geothermal systems were shown to be temporally stable. As such, no distinct trend of reduced annual temperatures could be observed. [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]

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]

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]

One of the main concerns associated with the development and use of regional CGE models is the determination of key parameter values, particularly substitution and other price elasticities. A common problem is the lack of appropriate regional data for econometric estimation. Consequently, it is important to identify key parameters that are likely to be important in determining quantitative results and then to prioritize these for estimation where appropriate data are available. In this paper, the focus is on the estimation of the regional trade (import) substitution parameters, which tend to be important in analysis for regional economies (given their openness to trade). Here, commodity import elasticities for the Illinois economy are estimated and tested in a single region CGE model of the Illinois economy. In our econometric estimation, we apply a model that takes account of market size and distance in estimating the substitutability between commodities produced in Illinois and other US states. [ABSTRACT FROM AUTHOR]