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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]

Abstract 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.

Abstract 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.

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]

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]

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]

Abstract 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.

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]

Abstract 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.

Marine biofouling is considered one of the major biophysical processes influencing the vertical dynamics of plastic debris in seawater. We numerically implement, for the first time, this mechanism within a fine-resolution, regional model of the Tyrrhenian Sea, in order to simulate the dispersion of microplastics (MPs) released at the mouth of a highly polluting river. Four polymers and three particle sizes are used to quantify algal concentration influence on the trajectories, fates, and accumulation spots of the tracked MPs, by comparing 2002 winter and summer runs encompassing or not biofouling. Besides a marked seasonality for most of the MP types and radii tested, biofouling effects are prominently observed for only 2 polymers and particles bigger than 1 μ m. Thus, further realistic applications of the biofouling mechanism in oceanic circulation models are required to achieve a thorough assessment of its impact on plastic density within distinctive basins of the world seas. • Marine biofouling is explicitly resolved in a realistic regional model (ROMS). • Microplastic (MP) particles are released at the outlet of a highly polluted river. • Biofouling strongly affects the horizontal and vertical dispersion of MPs. • Biofouling effects are significant for a limited range of MPs' sizes and polymers. • A marked seasonality is observed in the biofilm growth over the MP particles. [Display omitted] [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]

Study region: Nile basin. Study focus: Several studies have shown a relationship between climate change and changes in sediment yield. However, there are limited modeling applications that study this relationship at regional scales mainly due to data availability and computational cost. This study proposes a methodological framework using the SWAT+ model to predict and project sediment yield at a regional scale in data-scarce regions using global datasets. We implement a framework that (a) incorporates topographic factors from high/medium resolution DEMs (b) incorporates crop phenology data (c) introduces an areal threshold to linearize sediment yield in large model units and (d) apply a hydrological mass balance calibration. We test this methodology in the Nile Basin using a model application with (revised) and without (default) the framework under historical and future climate projections. New hydrological insights for the region: Results show improved sediment yield estimates in the revised model, both in absolute values and spatial distribution when compared to measured and reported estimates. The contemporary long term (1989 – 2019) annual mean sediment yield in the revised model was 1.79 t ha−1 yr−1 and projected to increase by 61 % (44 % more than the default estimates) in the future period (2071 – 2100), with the greatest sediment yield increase in the eastern part of the basin. Thus, the proposed framework improves and influences modeled and predicted sediment yield respectively.

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]

The Gulf of Mexico is a very productive and economically important system where riverine runoff acts as a linkage between the continental shelf and the open ocean, providing nutrients in addition to freshwater. This work investigates the three-dimensional transport and pathway structure of this river runoff offshore the continental shelf using ensembles of numerical simulations with different configurations regarding grid resolution (mesoscale resolving and submesoscale permitting) and river setup using suites of 5-months long integrations covering nearly 3 years. The riverine forcing is applied only at the surface over an area around the river mouth, a strategy often adopted in numerical studies, or as a meridional flux with a vertical extension. The simulated flow captures the southward offshore transport of river runoff driven by its interaction with the largest mesoscale circulations in the basin, the Loop Current and Loop Current eddies. This pathway is strong and well-document during summer but also active and relevant in winter, despite a less obvious surface signature. The most intense transport occurs primarily at the peripheries of the Loop Current and the detached eddies, and the freshwater is subducted as deep as 600 m around the mesoscale anticyclonic eddies. Submesoscale motions strengthen slightly the spread of freshwater plumes in summer but their contribution is negligible, if not negative, in winter. Differences in the freshwater distribution and transport volume among runs are small and generally less than 10% among ensembles, with overall slightly higher volume of freshwater transported off-shore and at depth in submesoscale permitting runs that include a velocity flux in their riverine input representation.

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]

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

Abstract 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.

Long-term sustainable water resources management requires comprehensive assessment of all water cycle components. The combined calibration of surface water (SW) and groundwater (GW) parameters is one of the biggest challenges in simulating SW-GW interactions. SWAT-MODFLOW has improved our ability to simulate SW and GW interactions, however there remain challenges associated with (i) understanding what parameters most influence model outputs and (ii) properly calibrating the GW related components of the water cycle (as represented within SWAT-MODFLOW). In this paper, we applied SWAT-MODFLOW to the 1452 km2 Shiraz catchment, located in southwestern Iran, for the period 2003–2019. A comprehensive sensitivity analysis (SA) of both SW and GW parameters was conducted to determine what controls runoff and GW level in the study catchment. The SA reveals that the Curve Number (CN2) and GW hydraulic conductivity (K) parameters have the most significant impact on runoff and GW level simulation. This work also presents a “GW Calibrator” (GWCal) tool along with a demonstration of how GWCal improves the performance of SWAT-MODFLOW in the Shiraz catchment as a result of the insights gained from the SA.

Interannual variability, seasonal evolution, and intraseasonal variability of the South Pacific Convergence Zone (SPCZ) are quantified using a new data set of 3-hourly SPCZ labels, available from 1980 to 2012 Nov–Apr. The SPCZ label is a binary field indicating presence (1) or absence (0) of the SPCZ at each grid point (½° longitude by ½° latitude) as a function of time and is the output of a Bayesian spatiotemporal statistical model that takes in instantaneous data from geostationary satellites. The statistical model is designed to emulate the way human observers identify the SPCZ. Results show two distinct parts to the SPCZ: the western tropical part and the eastern subtropical part. At times, the two parts do not connect. When they do connect, they are oriented quite differently, such that the subtropical part has a steeper meridional slope. The SPCZ is present 50–70% of the time in the tropics from Jan to Mar and is usually anchored to the warm sea surface temperature (SST) distribution of the equatorial west Pacific. The subtropical part does not have the same sensitivity to the underlying SST distribution and is present more often in Nov–Dec and Apr than in Jan–Mar when the SST is highest. Interannual variability in SPCZ location is strongly associated with El Niño–Southern Oscillation (ENSO); however, no change in overall SPCZ area is detected in association with ENSO. On the intraseasonal time scale, composite analysis shows the distinct spatial patterns in SPCZ presence associated with each phase of the Madden Julian Oscillation.

This paper presents the ability of the Rossby Center Regional Climate Model (RCA4) driven by nine global circulation models of the Coupled Models Intercomparison Project Phase 5 (CMIP5) to reproduce 10 hydro-climate extremes indices (HEIs) over Casamance river basin in southern Senegal, West Sahel for the present period of 1950 to 2000 using the data of four rain gauges stations. The temporal and spatial analysis highlights different behavior between the value and the variability of HEIs, but also between the western and the eastern part of the basin based on RCA4 outputs. Duration indices seem to be consistently overestimated by the RCA4 model with large uncertainty and variability (e.g. consecutive wet days), whereas the absolute indices are underestimated with uncertainties more pronounced in the west of the river basin. The heavy rain days are overestimated and very heavy rain days are underestimated as known in the literature. The RCA4 fails to well represent the daily precipitation intensity in the Casamance river basin except in one station (Bignona). However, the RCA4 model frames relatively well the indices based on the percentile threshold (R95PTOT, R99PTOT) but with some fluctuations. Overall, the annual total wet days seems to be better simulated by the RCA4 model in the western part. As conclusion, the RCA4 model exhibits relatively good or poor performance in representing extreme indices depending on the locations within the river basin but also on the range periods. These results confirm the difficulties for models in extreme climate representation.

Abstract 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.

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

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

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

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]

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

This study examined the surface-wetness effects in calculating dust generation in source regions, using Asian dust aerosol model version 3 (ADAM3; the control run; CNTL). Model sensitivity experiment was conducted in such a way that the dust generation in CNTL is compared against three ADAM3 versions with various surface-wetness effect schemes. The dust-generation algorithm in ADAM_RAIN utilizes precipitation, while the scheme in ADAM3_SM1 and ADAM3_SM2 employs soil water content to account for the surface-wetness effects on dust generation. Each run was evaluated for the spring (March–May) of 2020. ADAM3_SM1 shows the best performance for the dust source region in East Asia based on the root-mean-square error and the skill score, followed by ADAM3_SM2 and ADAM3_RAIN. Particularly, incorporation of the surface-wetness effects improves dust generation mostly in wet cases rather than dry cases. The three surface-wetness-effect runs reduce dust generation in the source regions compared to CNTL; hence, the inclusion of surface-wetness effects improves dust generation in the regions where CNTL overestimates dust generation.

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]