scholarly journals Interaction between cloud-radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a Cloud Resolving Model

2022 ◽  
Author(s):  
Layrson J. M. Gonçalves ◽  
Simone M. S. C. Coelho ◽  
Paulo Y. Kubota ◽  
Dayana C. Souza
Keyword(s):  
Large Scale ◽  
Meteorological Data ◽  
Cloud Fraction ◽  
Atmospheric Modeling ◽  
Amazon Region ◽  
Atmospheric Dynamics ◽  
Precipitation Rate ◽  
Temporal Series ◽  
Cloud Resolving Model ◽  
Scale Variable

Abstract. Observational meteorological data from the field experiment GoAmazon 2014/15 and data from numerical simulations with the Cloud-Resolving Model (CRM) called System for Atmospheric Modeling (SAM) are used to study the interaction between the cloudiness-radiation and the atmospheric dynamics and thermodynamics variables for a site located in the central Amazon region (−3.2° S, −60.6° W) during the wet and dry periods. The main aims are to (a) analyze the temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux; and (b) to determine the relationship between the integrated cloud fraction, radiative fluxes, and large-scale variable anomalies as a function of the previous day's average. The temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux from SAMS simulations showed physical consistency with the observations from GoAmazon 2014/15. Shallow and deep convection clouds show to have meaningful impact on radiation fluxes in the Amazon region during wet and dry periods. Anomalies of large-scale variables (relative to the previous day's average) are physically associated with cloud formation, evolution and dissipation. SAM consistently simulated these results, where the cloud fraction vertical profile shows a pattern very close to the observed data (cloud type). Additionally, the integrated cloud fraction and large-scale variable anomalies, as a function of the previous day's average, have a good correlation. These results suggest that the memory of the large-scale dynamics from previous day can be used to estimate the clouds fraction. As well as the water content, which is a variable of the cloud itself. In general, the SAM satisfactorily simulated the interaction between cloud-radiation and dynamic and thermodynamic variables of the atmosphere during the periods of this study, being indicated to obtain atmospheric variables that are impossible to obtain in an observational way.

Self-Organized Criticality and Homeostasis in Atmospheric Convective Organization

2012 ◽  
Vol 69 (12) ◽  
pp. 3449-3462 ◽  
Author(s):  
Jun-Ichi Yano ◽  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Self Regulation ◽  
Point Of View ◽  
Precipitation Rate ◽  
Regulation Mechanism ◽  
Scale Invariant ◽  
Self Organized ◽  
Cloud Resolving Model ◽  
Self Organized Criticality ◽  
The One

Abstract Atmospheric convection has a tendency to organize on a hierarchy of scales ranging from the mesoscale to the planetary scales, with the latter especially manifested by the Madden–Julian oscillation. The present paper examines two major competing mechanisms of self-organization in a cloud-resolving model (CRM) simulation from a phenomenological thermodynamic point of view. The first mechanism is self-organized criticality. A saturation tendency of precipitation rate with increasing column-integrated water, reminiscent of critical phenomena, indicates self-organized criticality. The second is a self-regulation mechanism that is known as homeostasis in biology. A thermodynamic argument suggests that such self-regulation maintains the column-integrated water below a threshold by increasing the precipitation rate. Previous analyses of both observational data as well as CRM experiments give mixed results. In this study, a CRM experiment over a large-scale domain with a constant sea surface temperature is analyzed. This analysis shows that the relation between the column-integrated total water and precipitation suggests self-organized criticality, whereas the one between the column-integrated water vapor and precipitation suggests homeostasis. The concurrent presence of these two mechanisms is further elaborated by detailed statistical and budget analyses. These statistics are scale invariant, reflecting a spatial scaling of precipitation processes.

scholarly journals Correction to: Methane and Electricity Production from Poultry Litter Digestion in the Amazon Region of Brazil: A Large-Scale Study

2021 ◽  
Author(s):  
Marcelo Mendes Pedroza ◽  
Wanderson Gomes da Silva ◽  
Luciene Santos de Carvalho ◽  
Alice Rocha de Souza ◽  
Girlene Figueiredo Maciel
Keyword(s):  
Large Scale ◽  
Poultry Litter ◽  
Amazon Region ◽  
Electricity Production ◽  
Large Scale Study

scholarly journals The definition of an atmospheric database for Aeolus

2011 ◽  
Vol 4 (1) ◽  
pp. 67-88 ◽  
Author(s):  
G. J. Marseille ◽  
K. Houchi ◽  
J. de Kloe ◽  
A. Stoffelen
Keyword(s):  
High Resolution ◽  
Meteorological Data ◽  
Sampling Strategy ◽  
Laser Wavelength ◽  
Geographic Region ◽  
Atmospheric Dynamics ◽  
Specific Humidity ◽  
Small Scale ◽  
Horizontal Line ◽  
Definition Of

Abstract. The definition of an atmospheric database is an important component of simulation studies in preparation of future earth observing remote sensing satellites. The Aeolus mission, formerly denoted Atmospheric Dynamics Mission (ADM) or ADM-Aeolus, is scheduled for launch end of 2013 and aims at measuring profiles of single horizontal line-of-sight (HLOS) wind components from the surface up to about 32 km with a global coverage. The vertical profile resolution is limited but may be changed during in-orbit operation. This provides the opportunity of a targeted sampling strategy, e.g., as a function of geographic region. Optimization of the vertical (and horizontal) sampling strategy requires a characterization of the atmosphere optical and dynamical properties, more in particular the distribution of atmospheric particles and their correlation with the atmospheric dynamics. The Aeolus atmospheric database combines meteorological data from the ECMWF model with atmosphere optical properties data from CALIPSO. An inverse algorithm to retrieve high-resolution particle backscatter from the CALIPSO level-1 attenuated backscatter product is presented. Global weather models tend to underestimate atmospheric wind variability. A procedure is described to ensure compatibility of the characteristics of the database winds with those from high-resolution radiosondes. The result is a high-resolution database of zonal, meridional and vertical wind, temperature, specific humidity and particle and molecular backscatter and extinction at 355 nm laser wavelength. This allows the simulation of small-scale atmospheric processes within the Aeolus observation sampling volume and their impact on the quality of the retrieved HLOS wind profiles. The database extends over four months covering all seasons. This allows a statistical evaluation of the mission components under investigation. The database is currently used for the development of the Aeolus wind processing, the definition of wind calibration strategies and the optimization of the Aeolus sampling strategy.

scholarly journals Coupling between Large-Scale Atmospheric Processes and Mesoscale Land–Atmosphere Interactions in the U.S. Southern Great Plains during Summer. Part II: Mean Impacts of the Mesoscale

2004 ◽  
Vol 5 (6) ◽  
pp. 1247-1258 ◽  
Author(s):  
Christopher P. Weaver
Keyword(s):  
Case Studies ◽  
Time Scales ◽  
Great Plains ◽  
Large Scale ◽  
Spatial Scales ◽  
Surface Heterogeneity ◽  
Atmospheric Modeling ◽  
Mesoscale Circulations ◽  
Southern Great Plains ◽  
Atmospheric Processes

Abstract This is Part II of a two-part study of mesoscale land–atmosphere interactions in the summertime U.S. Southern Great Plains. Part I focused on case studies drawn from monthlong (July 1995–97), high-resolution Regional Atmospheric Modeling System (RAMS) simulations carried out to investigate these interactions. These case studies were chosen to highlight key features of the lower-tropospheric mesoscale circulations that frequently arise in this region and season due to mesoscale heterogeneity in the surface fluxes. In this paper, Part II, the RAMS-simulated mesoscale dynamical processes described in the Part I case studies are examined from a domain-averaged perspective to assess their importance in the overall regional hydrometeorology. The spatial statistics of key simulated mesoscale variables—for example, vertical velocity and the vertical flux of water vapor—are quantified here. Composite averages of the mesoscale and large-scale-mean variables over different meteorological or dynamical regimes are also calculated. The main finding is that, during dry periods, or similarly, during periods characterized by large-scale-mean subsidence, the characteristic signature of surface-heterogeneity-forced mesoscale circulations, including enhanced vertical motion variability and enhanced mesoscale fluxes in the lowest few kilometers of the atmosphere, consistently emerges. Furthermore, the impact of these mesoscale circulations is nonnegligible compared to the large-scale dynamics at domain-averaged (200 km × 200 km) spatial scales and weekly to monthly time scales. These findings support the hypothesis that the land– atmosphere interactions associated with mesoscale surface heterogeneity can provide pathways whereby diurnal, mesoscale atmospheric processes can scale up to have more general impacts at larger spatial scales and over longer time scales.

scholarly journals Changes in Temperature and Precipitation Extremes in Superparameterized CAM in Response to Warmer SSTs

2017 ◽  
Vol 30 (24) ◽  
pp. 9827-9845 ◽  
Author(s):  
Xin Zhou ◽  
Marat F. Khairoutdinov
Keyword(s):  
Large Scale ◽  
Rain Rate ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Global Scale ◽  
Precipitation Extremes ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
Cloud Resolving Model ◽  
Cold Extremes

Subdaily temperature and precipitation extremes in response to warmer SSTs are investigated on a global scale using the superparameterized (SP) Community Atmosphere Model (CAM), in which a cloud-resolving model is embedded in each CAM grid column to simulate convection explicitly. Two 10-yr simulations have been performed using present climatological sea surface temperature (SST) and perturbed SST climatology derived from the representative concentration pathway 8.5 (RCP8.5) scenario. Compared with the conventional CAM, SP-CAM simulates colder temperatures and more realistic intensity distribution of precipitation, especially for heavy precipitation. The temperature and precipitation extremes have been defined by the 99th percentile of the 3-hourly data. For temperature, the changes in the warm and cold extremes are generally consistent between CAM and SP-CAM, with larger changes in warm extremes at low latitudes and larger changes in cold extremes at mid-to-high latitudes. For precipitation, CAM predicts a uniform increase of frequency of precipitation extremes regardless of the rain rate, while SP-CAM predicts a monotonic increase of frequency with increasing rain rate and larger change of intensity for heavier precipitation. The changes in 3-hourly and daily temperature extremes are found to be similar; however, the 3-hourly precipitation extremes have a significantly larger change than daily extremes. The Clausius–Clapeyron scaling is found to be a relatively good predictor of zonally averaged changes in precipitation extremes over midlatitudes but not as good over the tropics and subtropics. The changes in precipitable water and large-scale vertical velocity are equally important to explain the changes in precipitation extremes.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

scholarly journals Using Stochastically Generated Subcolumns to Represent Cloud Structure in a Large-Scale Model

2006 ◽  
Vol 134 (12) ◽  
pp. 3644-3656 ◽  
Author(s):  
Robert Pincus ◽  
Richard Hemler ◽  
Stephen A. Klein
Keyword(s):  
Large Scale ◽  
Statistical Tests ◽  
Internal Variability ◽  
Cloud Fraction ◽  
Shortwave Radiation ◽  
Scale Model ◽  
Cloud Structure ◽  
Model Configuration ◽  
The Impact ◽  
Radiation Scheme

Abstract A new method for representing subgrid-scale cloud structure in which each model column is decomposed into a set of subcolumns has been introduced into the Geophysical Fluid Dynamics Laboratory’s global atmospheric model AM2. Each subcolumn in the decomposition is homogeneous, but the ensemble reproduces the initial profiles of cloud properties including cloud fraction, internal variability (if any) in cloud condensate, and arbitrary overlap assumptions that describe vertical correlations. These subcolumns are used in radiation and diagnostic calculations and have allowed the introduction of more realistic overlap assumptions. This paper describes the impact of these new methods for representing cloud structure in instantaneous calculations and long-term integrations. Shortwave radiation computed using subcolumns and the random overlap assumption differs in the global annual average by more than 4 W m−2 from the operational radiation scheme in instantaneous calculations; much of this difference is counteracted by a change in the overlap assumption to one in which overlap varies continuously with the separation distance between layers. Internal variability in cloud condensate, diagnosed from the mean condensate amount and cloud fraction, has about the same effect on radiative fluxes as does the ad hoc tuning accounting for this effect in the operational radiation scheme. Long simulations with the new model configuration show little difference from the operational model configuration, while statistical tests indicate that the model does not respond systematically to the sampling noise introduced by the approximate radiative transfer techniques introduced to work with the subcolumns.

scholarly journals Analysis of evapotranspiration and biomass in pastures with degradation indicatives in the Upper Tocantins River Basin, in Brazilian Savanna

Revista CERES ◽  
2016 ◽  
Vol 63 (6) ◽  
pp. 754-760 ◽  
Author(s):  
Ricardo Guimarães Andrade ◽  
Antônio Heriberto de Castro Teixeira ◽  
Janice Freitas Leivas ◽  
Sandra Furlan Nogueira
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Meteorological Data ◽  
Simple Algorithm ◽  
Production Potential ◽  
Pasture Degradation ◽  
Ndvi Time Series ◽  
Tocantins River ◽  
Remote Sensing Techniques ◽  
Degraded Pastures

ABSTRACT The objective of this study was to apply the Simple Algorithm For Evapotranspiration Retrieving (SAFER) with MODIS images together with meteorological data to analyze evapotranspiration (ET) and biomass production (BIO) according to indicative classes of pasture degradation in Upper Tocantins River Basin. Indicative classes of degraded pastures were obtained from the NDVI time-series (2002-2012). To estimate ET and BIO in each class, MODIS images and data from meteorological stations of the year 2012 were used. The results show that compared to not-degraded pastures, ET and BIO were different in pastures with moderate to strong degradation, mainly during water stress period. Therefore, changes in energy balance partition may occur according to the degradation levels, considering that those indicatives of degradation processes were identified in 24% of the planted pasture areas. In this context, ET and BIO estimates using remote sensing techniques can be a reliable indicator of forage availability, and large-scale aspects related to the degradation of pastures. It is expected that this knowledge may contribute to initiatives of public policies aimed at controlling the loss of production potential of pasture areas in the Upper Tocantins River Basin in the state of Goiás, Brazil.

scholarly journals Cluster analysis of midlatitude oceanic cloud regimes: mean properties and temperature sensitivity

2010 ◽  
Vol 10 (13) ◽  
pp. 6435-6459 ◽  
Author(s):  
N. D. Gordon ◽  
J. R. Norris
Keyword(s):  
Large Scale ◽  
Longwave Radiation ◽  
Cloud Fraction ◽  
Climate System ◽  
Radiative Properties ◽  
Upper Troposphere ◽  
Low Level ◽  
Increasing Temperature ◽  
Cloud Regimes ◽  
Cloud Response

Abstract. Clouds play an important role in the climate system by reducing the amount of shortwave radiation reaching the surface and the amount of longwave radiation escaping to space. Accurate simulation of clouds in computer models remains elusive, however, pointing to a lack of understanding of the connection between large-scale dynamics and cloud properties. This study uses a k-means clustering algorithm to group 21 years of satellite cloud data over midlatitude oceans into seven clusters, and demonstrates that the cloud clusters are associated with distinct large-scale dynamical conditions. Three clusters correspond to low-level cloud regimes with different cloud fraction and cumuliform or stratiform characteristics, but all occur under large-scale descent and a relatively dry free troposphere. Three clusters correspond to vertically extensive cloud regimes with tops in the middle or upper troposphere, and they differ according to the strength of large-scale ascent and enhancement of tropospheric temperature and humidity. The final cluster is associated with a lower troposphere that is dry and an upper troposphere that is moist and experiencing weak ascent and horizontal moist advection. Since the present balance of reflection of shortwave and absorption of longwave radiation by clouds could change as the atmosphere warms from increasing anthropogenic greenhouse gases, we must also better understand how increasing temperature modifies cloud and radiative properties. We therefore undertake an observational analysis of how midlatitude oceanic clouds change with temperature when dynamical processes are held constant (i.e., partial derivative with respect to temperature). For each of the seven cloud regimes, we examine the difference in cloud and radiative properties between warm and cold subsets. To avoid misinterpreting a cloud response to large-scale dynamical forcing as a cloud response to temperature, we require horizontal and vertical temperature advection in the warm and cold subsets to have near-median values in three layers of the troposphere. Across all of the seven clusters, we find that cloud fraction is smaller and cloud optical thickness is mostly larger for the warm subset. Cloud-top pressure is higher for the three low-level cloud regimes and lower for the cirrus regime. The net upwelling radiation flux at the top of the atmosphere is larger for the warm subset in every cluster except cirrus, and larger when averaged over all clusters. This implies that the direct response of midlatitude oceanic clouds to increasing temperature acts as a negative feedback on the climate system. Note that the cloud response to atmospheric dynamical changes produced by global warming, which we do not consider in this study, may differ, and the total cloud feedback may be positive.

scholarly journals Sunlight-absorbing aerosol amplifies the seasonal cycle in low cloud fraction over the southeast Atlantic

2021 ◽  
Author(s):  
Jianhao Zhang ◽  
Paquita Zuidema
Keyword(s):  
Boundary Layer ◽  
Seasonal Cycle ◽  
Large Scale ◽  
Cloud Fraction ◽  
Small Scale ◽  
Aerosol Cloud ◽  
Ascension Island ◽  
Tropospheric Aerosol ◽  
Low Cloud ◽  
Aerosol Cloud Interactions

Abstract. Many studies examining shortwave-absorbing aerosol-cloud interactions over the southeast Atlantic apply a seasonal averaging. This disregards a meteorology that raises the mean altitude of the smoke layer from July to October. This study details the month-by-month changes in cloud properties and the large-scale environment as a function of the biomass-burning aerosol loading at Ascension Island from July to October, based on measurements from Ascension Island (8º S, 14.5º W), satellite retrievals and reanalysis. In July and August, variability in the smoke loading predominantly occurs in the boundary layer. During both months, the low-cloud fraction is less and is increasingly cumuliform when more smoke is present, with the exception of a late morning boundary layer deepening that encourages a short-lived cloud development. The meteorology varies little, suggesting aerosol-cloud interactions consistent with a boundary-layer semi-direct effect can explain the cloudiness changes. September marks a transition month during which mid-latitude disturbances can intrude into the Atlantic subtropics, constraining the land-based anticyclonic circulation transporting free-tropospheric aerosol to closer to the coast. Stronger boundary layer winds help deepen, dry, and cool the boundary layer near the main stratocumulus deck compared to that on days with high smoke loadings, with stratocumulus reducing everywhere but at the northern deck edge. Longwave cooling rates generated by a sharp water vapor gradient at the aerosol layer top facilitates small-scale vertical mixing, and could help to maintain a better-mixed September free troposphere. The October meteorology is more singularly dependent on the strength of the free-tropospheric winds advecting aerosol offshore. Free-tropospheric aerosol is less, and moisture variability more, compared to September. Low-level clouds increase and are more stratiform, when the smoke loadings are higher. The increased free-tropospheric moisture can help sustain the clouds through reducing evaporative drying during cloud-top entrainment. Enhanced subsidence above the coastal upwelling region increasing cloud droplet number concentrations may further prolong cloud lifetime through microphysical interactions. Reduced subsidence underneath stronger free-tropospheric winds at Ascension supports slightly higher cloud tops during smokier conditions. Overall the monthly changes in the large-scale aerosol and moisture vertical structure act to amplify the seasonal cycle in low-cloud amount and morphology, raising a climate importance as cloudiness changes dominate changes in the top-of-atmosphere radiation budget.

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