Representation Of Fluxes Research Articles

Comparison of 2-m surface temperature data between reanalysis and observations over the Arabian Peninsula

The 2-m temperature data is a significant indicator for studying the weather extremes and the exchange of water and energy fluxes between the surface and atmosphere. This study compared three reanalysis datasets, i.e., ERA5, ERA5-Land, and MERRA-2, with observations from in-situ sources from 1990 to 2022 using various statistical error metrics and extreme temperature indices over the Arabian Peninsula (AP) region. We selected these reanalysis datasets due to the continuous improvements and higher spatiotemporal output better capturing the temperature variability. The spatiotemporal climatology shows lower temperatures in winter (<15 °C) and maximum in summer (>35 °C); however, the reanalysis data show more deviations in temperature during the cold season than in the warm season. The reanalysis data underestimated the frequency of the cold (<10 °C) and hot (>30 °C) days across the four regions, except ERA5-Land, which closely followed observed data. The strength of the correlation shows better performance (>0.90) in the cold extremes (cold nights and cold days) frequency than the hot extremes. On an interannual scale, reanalysis products exhibit strong correlations (>0.90) with in-situ data across most regions, particularly in winter and autumn, moderate in spring, and weaker in summer. The reanalysis data shows negative biases in the inland regions and positive biases in the coastal areas with consistent root mean square differences (RMSD) spatiotemporally. The differences in performance are due to the topography and poor representation of the energy fluxes, especially in MERRA-2 as well as missing data in observations. This study recommends ERA5-Land as the first choice for extreme weather simulations in the region, followed by MERRA-2 and ERA5 on the same scale, but proper attention is needed when using reanalysis data for cold and hot extremes.

Assessing the Performance of Flux Imbalance Prediction Models Using Large Eddy Simulations Over Heterogeneous Land Surfaces

Accurate representation of heat fluxes is crucial for understanding land–atmosphere interactions and improving atmospheric simulations. However, a common issue arises with flux imbalance, where the measured turbulent heat flux tends to be underestimated due to the nonlocal effects of atmospheric secondary circulations. This study evaluated four flux imbalance prediction models by analyzing data from large eddy simulations performed over heterogeneous land surfaces. For that, a checkerboard pattern of soil moisture was used to define the lower boundary conditions for the atmosphere, across heterogeneity scales ranging from 50 m to 2.4 km. The results show that the selected models can effectively predict flux imbalance when provided with proper semi-empirical factors. The presence of two distinct secondary circulations, thermally-induced mesoscale circulation and turbulent organized structures, account for the nonlinear effect of the heterogeneity scale on the flux imbalance, but it does not affect the performance of the selected models. This study suggests that the flux imbalance prediction models are useful for improving e.g. eddy-covariance measurements. Additionally, a quadrant analysis showed an increasing difference between ejections and sweeps with height, which explains the decrease and increase of the turbulent heat flux and flux imbalance, respectively, and underscores the importance of accounting for vertical variations in turbulent fluxes to represent atmospheric processes accurately.

Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique

The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving the Evaporative (EF) and the Non-Evaporative (NEF) Fractions as representations of the daytime average fluxes. The applicability of the investigated technique, is demonstrated for sixteen calendar days of year 2011 using in-situ data acquired from nine CarboEurope sites representing a variety of climatic, topographic and environmental conditions. Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM predicted maps showed a small bias of 0.08 vol vol−1, a scatter of 0.18 vol vol−1 and a RMSD of 0.19 vol vol−1. The predicted LE fluxes showed a relatively low overall agreement (RMSD = 65.10 Wm-2), whereas for H flux reported RMSD was 85.02 Wm-2. The results also confirmed the ability of the investigated technique to provide meaningful estimates of the NEF and EF. All in all, the present study findings were at least comparable, or of higher accuracy, to those reported in other similar verification studies of the “triangle” using both high resolution (airborne) and low resolution (satellite) data. To our knowledge, this study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting combining the SimSphere SVAT model and ASTER EO datasets.

Within-canopy variation in chlorophyll fluorescence parameters can be well captured by vertical patterns of leaf biophysical and chemical traits.

The strong correlation between chlorophyll fluorescence (ChlF) parameters and photosynthesis highlights the need for a comprehensive spatial representation of ChlF parameters within the canopy. Such an approach is essential to advance our understanding and to improve the representation and modeling of water and carbon fluxes at scales ranging from the leaf to the canopy level. However, the challenge remains in determining how to effectively describe and track the variability of ChlF parameters within the canopy. In this study, we determined the variation in leaf biophysical and chemical traits and ChlF parameters along the vertical height of the canopy for several species in a temperate deciduous forest. We observed general associations of height with leaf biophysical and chemical traits and ChlF parameters, although these relationships were species-dependent. In addition, leaf biophysical and chemical traits, particularly light-harvesting pigments, showed significant effects on ChlF parameters. To effectively track variation in ChlF parameters within the canopy, we used gradient-boosted regression (GBR) models driven by leaf traits and species, which explained more than 80% of the variation in all ChlF parameters. Our study demonstrates the feasibility of utilizing leaf biophysical and chemical traits to predict vertical variation in ChlF parameters and provide supportive data for modeling canopy photosynthesis.

The Effect of Coupling Between CLUBB Turbulence Scheme and Surface Momentum Flux on Global Wind Simulations

AbstractThe higher‐order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near‐surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB's stability‐driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB‐computed zonal and meridional wind speed tendencies. Our 30‐year present‐day climate simulations (1980–2010) show that static coupling overestimates 10‐m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10‐m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind‐speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near‐surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

The importance of the inertial coupling in the two-fluid model of two-phase flow

The new flux representation of the two-fluid model of two-phase flow, where the mixture is described in terms of the volumetric and drift fluxes, is currently the most consistent formulation to treat the inertial coupling between phases. In this representation, the dynamics of the relative motion between phases is revealed as a non-linear wave propagation equation. It is shown that the character and stability of this equation is determined by the balance between the inertial coupling and the interfacial drag. A novel stability criterion is derived that can be used to assess the interfacial closure laws and as a tool to determine the conditions under which a drift-flux correlation is stable. A family of inertial coupling functions for vertical two-phase flow, based on topologies of bubble's vortical wakes, is derived and the corresponding coupling parameters are assessed using available experimental data. The resulting stability maps reveal the occurrence of an unstable region at intermediate void fractions bound by a bistable condition at low and high void fractions, which can be associated with the slug flow-pattern regime.

Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign

Abstract. We present a comprehensive observational approach that aims to establish relationships between the surface–atmosphere exchange of ammonia (NH3) and CO2 uptake and transpiration by vegetation. In doing so, we study relationships useful for the improvement and development of NH3 flux representations in models. The NH3 concentration and flux are measured using a novel open-path miniDOAS (differential optical absorption spectroscopy) measurement setup, taken during the 5-week Ruisdael Land–Atmosphere Interactions Intensive Trace-gas and Aerosol measurement (RITA-2021) campaign (25 August until 12 October 2021) at the Ruisdael Observatory in Cabauw, the Netherlands. After filtering for unobstructed flow, sufficient turbulent mixing and CO2 uptake, we find the diurnal variability in the NH3 flux to be characterized by daytime emissions (0.05 µgm-2s-1 on average) and deposition at sunrise and sunset (−0.05 µgm-2s-1 on average). We first compare the NH3 flux to the observed gross primary production (GPP), representing CO2 uptake, and latent heat flux (LvE), representing net evaporation. Next, we study the observations following the main drivers of the dynamic vegetation response, which are photosynthetically active radiation (PAR), temperature (T) and the water vapor pressure deficit (VPD). Our findings indicate the dominance of the stomatal emission of NH3, with a high correlation between the observed emissions and both LvE (0.70) and PAR (0.72), as well as close similarities in the diurnal variability in the NH3 flux and GPP. However, efforts to establish relationships are hampered by the high diversity in the NH3 sources of the active agricultural region and the low data availability after filtering. Our findings show the need to collocate meteorological, carbon and nitrogen studies to advance our understanding of NH3 surface exchange and its representation.

The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1

Abstract. A widely applicable parameterisation of turbulent heat and momentum fluxes at sea has been developed for the SURFEX v8.1 surface model. This wave-age-dependent stress parameterisation (WASP) combines a close fit to available in situ observations at sea up to wind speed of 60 m s−1 with the possibility of activating the impact of wave growth on the wind stress. It aims in particular at representing the effect of surface processes that depend on the surface wind according to the state of the art. It can be used with the different atmospheric models coupled with the surface model SURFEX, including the CNRM-CM climate model, the operational (numerical weather prediction) systems in use at Météo-France, and the research model Meso-NH. Designed to be used in coupled or forced mode with a wave model, it can also be used in an atmosphere-only configuration. It has been validated and tested in several case studies covering different surface conditions known to be sensitive to the representation of surface turbulent fluxes: (i) the impact of a sea surface temperature (SST) front on low-level flow by weak wind, (ii) the simulation of a Mediterranean heavy precipitating event where waves are known to influence the low-level wind and displace precipitation, (iii) several tropical cyclones, and (iv) a climate run over 35 years. It shows skills comparable to or better than the different parameterisations in use in SURFEX v8.1 so far.

Including land management in a European carbon model with lateral transfer to the oceans

The use of cover crops (CCs) is a promising cropland management practice with multiple benefits, notably in reducing soil erosion and increasing soil organic carbon (SOC) storage. However, the current ability to represent these factors in land surface models remains limited to small scales or simplified and lumped approaches due to the lack of a sediment-carbon erosion displacement scheme. This precludes a thorough understanding of the consequences of introducing a CC into agricultural systems. In this work, this problem was addressed in two steps with the spatially distributed CE-DYNAM model. First, the historical effect of soil erosion, transport, and deposition on the soil carbon budget at a continental scale in Europe was characterized since the early industrial era, using reconstructed climate and land use forcings. Then, the impact of two distinct policy-oriented scenarios for the introduction of CCs were evaluated, covering the European cropping systems where surface erosion rates or nitrate susceptibility are critical. The evaluation focused on the increase in SOC storage and the export of particulate organic carbon (POC) to the oceans, compiling a continental-scale carbon budget. The results indicated that Europe exported 1.95 TgC/year of POC to the oceans in the last decade, and that CCs can contribute to reducing this amount while increasing SOC storage. Compared to the simulation without CCs, the additional rate of SOC storage induced by CCs peaked after 10 years of their adoption, followed by a decrease, and the cumulative POC export reduction stabilized after around 13 years. The findings indicate that the impacts of CCs on SOC and reduced POC export are persistent regardless of their spatial allocation adopted in the scenarios. Together, the results highlight the importance of taking the temporal aspect of CC adoption into account and indicate that CCs alone are not sufficient to meet the targets of the 4‰ initiative. Despite some known model limitations, which include the lack of feedback of erosion on the net primary productivity and the representation of carbon fluxes with an emulator, the current work constitutes the first approach to successfully couple a distributed routing scheme of eroded carbon to a land carbon model emulator at a reasonably high resolution and continental scale. Short abstractA spatially distributed model coupling erosion, transport, and deposition to the carbon cycle was developed. Then, it was used to simulate the impact of cover crops on both erosion and carbon, to show that cover crops can simultaneously increase organic carbon storage and reduce particulate organic carbon export to the oceans. The results seemed persistent regardless of the spatial distribution of cover crops.

Improving process-consistency of an ecohydrological model through inclusion of spatial patterns of satellite-derived land surface temperature

Distributed hydrological models can correctly simulate spatial patterns if the model parameters adequately represent the spatial heterogeneity of the basin. However, determining realistic values of these parameters is often difficult. Considering remote sensing-derived land-surface states and fluxes in combination with streamflow observations is a suitable strategy to better constrain parameters and improve process-consistency. For ecohydrological modelling, an accurate representation of vegetation characteristics is important to capture the vegetation response to varying moisture availability. Land surface temperature (Ts) may serve as a valuable diagnostic because it is pivotal to the surface energy and water balance, and conveys information about ecosystem stress and water use. This study aims at assessing the benefits of integrating spatial patterns of Landsat-derived Ts into calibration of a process-based ecohydrological model to improve process representation of catchment-scale energy fluxes and vegetation response to moisture deficits. We explicitly analyze the trade-off between streamflow and Ts performance, and explore the value of adding an increasing number of Ts images in the calibration process. The study is performed in a mixed land cover catchment in Germany using the ecohydrological model EcH2O. Our results demonstrate the value of satellite-derived Ts data for reducing uncertainties of energy-balance related vegetation parameters, which are hardly constrained in streamflow-only calibration. Including satellite-derived Ts for model calibration reduced the mean absolute error of spatial anomalies in simulated Ts patterns by 15 % with negligible deterioration of streamflow performance. Inclusion of spatial Ts patterns improved capturing the differences in Ts between vegetation types, and affected simulated evapotranspiration fluxes. Improvements in simulated Ts could already be achieved by including only few (4–5) images of satellite-derived Ts in calibration. Based on our results, we advocate a wider use of satellite-based Ts data for multivariate calibration to improve model parameter identifiability and process consistency of highly parameterised, process-based distributed ecohydrological models, especially when capturing hydrology-vegetation interactions is crucial.

Evaluation of summer mean radiant temperature simulation in ENVI-met in a hot Mediterranean climate

ENVI-met is currently one of the most popular simulation tools of urban microclimates, used by researchers and designers alike. Yet, in recent years some concerns have been raised regarding the software's capability to accurately simulate the effects of solar irradiance on increased heat stress, especially in hot weather conditions. These concerns have eventually led to the implementation of a new radiation scheme in version 5 of the software. This study set out to evaluate whether this new scheme increased the software's accuracy in evaluating incoming shortwave and longwave radiation fluxes, expressed as Mean Radiant Temperature (MRT), by comparing monitored and simulated summertime values at 60 locations.Our results show that while ENVI-met's new version improved MRT simulation accuracy, the software systematically underestimated the magnitude of MRT in both shaded and unshaded summer conditions. Our MRT evaluation resulted in an RMSE of 6.08 °C in the shade and 13.32 °C at locations fully exposed to solar radiation, which may result in inaccurate and less severe appreciation of outdoor heat stress, especially when outdoor shade is missing. Based on our analysis, we suggest that the origin of error may stem from inaccurate representation of longwave radiation fluxes emitted by ground surfaces.

Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models

AbstractThe proper numerical representation of physical processes in mechanistic hydrological models is essential to produce robust predictions. A common problem with numerical schemes in hydrological models is that multiple concurrent fluxes are calculated sequentially. Although the importance of errors introduced by inappropriate numerical schemes is well recognized in the literature, many hydrological models calculate concurrent fluxes sequentially. Here, two versions of the HYPE model are used to investigate the limitations of sequential calculations. A fourth order Gear‐Nordsieck solution of the continuous state‐space formulation of HYPE (I‐HYPE) is developed to provide a robust solution, and a fixed‐step implicit Euler scheme (IE‐HYPE) is implemented to provide a computationally efficient and robust approximation of the I‐HYPE simulations. In contrast to I‐HYPE, results show that the original HYPE and the sequential calculation implemented in the continuous state‐space formulation of HYPE (SQ‐HYPE) typically simulate no interflow when soil moisture levels exceed the field capacity. The discrepancy between SQ‐HYPE and I‐HYPE grows with the size of the computation time step, and this implies a compromised representation of flow paths by sequential schemes. IE‐HYPE provides responses comparable with I‐HYPE for both daily and hourly time steps. IE‐HYPE and SQ‐HYPE are compared in terms of their groundwater representation, parameter identifiability, and predictive skills for two catchments. The sequential models have larger groundwater contributions to flow than IE‐HYPE because the splitting errors in SQ‐HYPE limit the interflow flux. IE‐HYPE estimates of the groundwater flux are more consistent with literature values of groundwater contributions to flow for the basins studied.

Carbon and Water Fluxes of the Boreal Evergreen Needleleaf Forest Biome Constrained by Assimilating Ecosystem Carbonyl Sulfide Flux Observations

AbstractGross primary production (GPP) by boreal forests is highly sensitive to environmental changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain due to the lack of direct photosynthesis observations at large scales. Carbonyl sulfide (COS) has emerged as a promising proxy to improve the representation of GPP in LSMs. Because COS is absorbed by vegetation following the same diffusion pathway as CO2 during photosynthesis and not emitted back to the atmosphere, incorporating a mechanistic representation of vegetation COS uptake in LSMs allows using COS observations to refine GPP representation. Here, we perform ecosystem COS flux and GPP data assimilations to constrain the COS‐ and GPP‐related parameters in the ORCHIDEE LSM for boreal evergreen needleleaf forests (BorENF). Assimilating ecosystem COS fluxes at Hyytiälä forest increases the simulated net ecosystem COS uptake by 14%. This increase largely results from changes in the internal conductance to COS, highlighting the need to improve the representation of COS internal diffusion and consumption. Moreover, joint assimilation of ecosystem COS flux and GPP at Hyytiälä improves the simulated latent heat flux, contrary to the GPP‐only data assimilation, which fails to do so. Finally, we scaled this assimilation framework up to the boreal region and find that the joint assimilation of COS at Hyytiälä and GPP fluxes at 10 BorENF sites increases the modeled vegetation COS uptake up to 18%, but not GPP. Therefore, this study encourages the use of COS flux observations to inform GPP and latent heat flux representations in LSMs.

Modelling the biogeochemical footprint of rivers in the Hauraki Gulf, New Zealand

Building accurate physical-biogeochemical models of processes driving climate and eutrophication-related stressors in coastal waters is an essential step in managing the impacts of these stressors. Here we develop a coupled physical-biogeochemical model to investigate present day processes for a key marine ecosystem in Aotearoa, New Zealand: The Hauraki Gulf/Firth of Thames system. Simulation results compared well with an accompanying long-term (decadal) observational dataset, indicating that the model captured most of the physical and biological dynamics of the Hauraki Gulf/Firth of Thames system. This model was used to investigate the riverine and cross shelf exchanges of nutrients in the region and showed that only a small number of large rivers within the Firth of Thames dominated the freshwater inputs, with phytoplankton concentrations driven by nutrient inputs from these rivers. However, while riverine inputs dominated the biological response in the Firth of Thames, cross-shelf fluxes dominated the biological response in the outer Hauraki Gulf region. Nutrients from both sources were balanced by a sediment denitrification flux. Analyses were conducted to examine agreement of observations with subsampled and climatological model outputs. These revealed that modelling effort needs to focus on the representation of sediment fluxes and parameterizations during the autumn, and the observational effort needs to focus on increased temporal data collection during summer to better understand biases in seasonal climatologies derived from model and observations. These results are valuable for demonstrating effects of land-derived and oceanic drivers of the biogeochemical dynamics of the Hauraki Gulf/Firth of Thames system.

The impact of seasonality on the annual air-sea carbon flux and its interannual variability

Interannual variability of the ocean carbon sink is often assessed using annual air–sea carbon fluxes, but the drivers of the variability may instead arise from seasonal processes that are neglected in the annual average. The seasonal cycle largely modulates air–sea carbon exchange, hence understanding seasonal mechanisms and their link to interannual variability is necessary to determine long-term changes in the ocean carbon sink. We contrast carbon fluxes from an Earth System Model large ensemble and an observation-based ensemble to assess the representation of annual and seasonal carbon fluxes in two distinct ocean regions—the North Atlantic basin and the Southern Ocean and investigate if seasonal variability can help diagnose interannual variability. Both ensembles show strong agreement in their annual mean fluxes. However, discrepancies between the two ensembles are one to two times greater for the seasonal fluxes than the annual fluxes in the North Atlantic basin and three to four times greater in the Southern Ocean. These seasonal discrepancies compensate in the annual mean, obscuring significant seasonal mismatches between the ensembles, particularly in the Southern Ocean. A solid understanding of seasonal variability can be leveraged to diagnose interannual variability of carbon fluxes where necessary observational constraints have been built, for example, in the North Atlantic basin, where boreal winter and spring drive the interannual variability. However, in a data-sparse region like the Southern Ocean, both ensembles disagree substantially in their representations of seasonal carbon fluxes and variability, and currently, seasonal variability is of limited use in diagnosing the interannual variability of carbon fluxes in the Southern Ocean.

A Pareto‐Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data

AbstractEvaporation is gaining increasing attention as a calibration and evaluation variable in hydrologic studies that seek to improve the physical realism of hydrologic models and go beyond the long‐established streamflow‐only calibration. However, this trend is not yet reflected in sensitivity analyses aimed at determining the relevant parameters to calibrate, where streamflow has traditionally played a leading role. On the basis of a Pareto optimization approach, we propose a framework to integrate the temporal dynamics of streamflow and evaporation into the sensitivity analysis and calibration stages of the hydrologic modeling exercise, here referred to as “Pareto‐based sensitivity analysis” and “multiobjective calibration.” The framework is successfully applied to a case study using the Variable Infiltration Capacity (VIC) model in three catchments located in Spain as representative of the different hydroclimatic conditions within the Iberian Peninsula. Several VIC vegetation parameters were identified as important to the performance estimates for evaporation during sensitivity analysis, and therefore were suitable candidates to improve the model representation of evaporative fluxes. Sensitivities for the streamflow performance, in turn, were mostly driven by the soil and routing parameters, with little contribution from the vegetation parameters. The multiobjective calibration experiments were carried out for the most parsimonious parameterization after a comparative analysis of the performance gains and losses for streamflow and evaporation, and yielded optimal adjustments for both hydrologic variables simultaneously. Results from this study will help to develop a better understanding of the trade‐offs resulting from the joint integration of streamflow and evaporation data into modeling frameworks.

Impact of Soil Moisture in the Monsoon Region of South America during Transition Season

The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content.

Bay of Bengal upper-ocean stratification and the sub-seasonal variability in convection: Role of rivers in a coupled ocean-atmosphere model

The Bay of Bengal (BoB) receives a large amount of freshwater from rains and rivers, resulting in large upper-ocean stratification due to the freshening effect. This salinity stratification has been theorized to impact sea-surface temperature (SST) and convection on intra-seasonal time scales by affecting the ocean mixed layer and the barrier layer. This article aims to quantify the impact of salinity stratification on the sub-seasonal variability in SST and convection by using in-situ ocean observations and coupled model experiments. It is shown that monsoon intra-seasonal oscillations (MISOs) exhibit varied levels of intra-seasonal variability in SST and rainfall based on the underlying ocean conditions. The largest intra-seasonal variability in SST does not cause the largest convection variability in the north-western BoB. Instead, moderate variability in SST and rainfall associated with MISOs co-occur with deep mixed layer and thick barrier layer conditions. Realistic representation of river freshwater fluxes in a coupled ocean-atmosphere model leads to improved intra-seasonal SST and rainfall variability. Thick barrier layers in the north-western Bay attenuates the entrainment cooling of the mixed layer, and the high mixed layer heat content provides conducive oceanic conditions for the genesis of monsoon low-pressure systems (LPS), thereby affecting rainfall over India. This study has important implications for operation forecasting using coupled models.

New optical recursional spherical ferromagnetic flux for optical sonic microscale

In this paper, we construct optical spherical recursion flux for optical spherical [Formula: see text]-magnetic curve with optical normalized microscale by sonic magnetic crystal. Also, we design optical spherical magnetic normalized density of optical spherical [Formula: see text]-magnetic curve. Then, we illustrate new version of optical spherical recursional flux of optical spherical [Formula: see text]-magnetic curve. Moreover, we characterize total magnetical normalized density with optical ferromagnetic model for optical spherical [Formula: see text]-magnetic curve. The obtained spherical recursion flux and optical graphical representations visualize geometry of sonic magnetic crystal media.