On the Climate Impact of Surface Roughness Anomalies

2008 ◽  
Vol 65 (7) ◽  
pp. 2215-2234 ◽  
Author(s):  
Daniel B. Kirk-Davidoff ◽  
David W. Keith
Keyword(s):  
Surface Roughness ◽  
Large Scale ◽  
Climate Impact ◽  
Water Model ◽  
Dynamic Mechanisms ◽  
Community Atmosphere Model ◽  
Horizontal Length ◽  
Rossby Wave Response ◽  
The Mean ◽  
The Impact

Abstract Large-scale deployment of wind power may alter climate through alteration of surface roughness. Previous research using GCMs has shown large-scale impacts of surface roughness perturbations but failed to elucidate the dynamic mechanisms that drove the observed responses in surface temperature. Using the NCAR Community Atmosphere Model in both its standard and aquaplanet forms, the authors have explored the impact of isolated surface roughness anomalies on the model climate. A consistent Rossby wave response in the mean winds to roughness anomalies across a range of model implementations is found. This response generates appreciable wind, temperature, and cloudiness anomalies. The interrelationship of these responses is discussed, and it is shown that the magnitude of the responses scales with the horizontal length scale of the roughened region, as well as with the magnitude of the roughness anomaly. These results are further elucidated through comparison with results of a series of shallow-water model experiments.

scholarly journals What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

2019 ◽  
Vol 147 (7) ◽  
pp. 2433-2449
Author(s):  
Laura C. Slivinski ◽  
Gilbert P. Compo ◽  
Jeffrey S. Whitaker ◽  
Prashant D. Sardeshmukh ◽  
Jih-Wang A. Wang ◽  
...  
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Mean Square ◽  
Observational Network ◽  
In Situ Observations ◽  
The Mean ◽  
The Impact ◽  
Assimilation System

Abstract Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

Large-scale fuel deposition patterns on northern Spanish shores following the ‘Prestige’ oil spill

2008 ◽  
Vol 88 (3) ◽  
pp. 463-468 ◽  
Author(s):  
José Luis Acuña ◽  
Araceli Puente ◽  
Ricardo Anadón ◽  
Consolación Fernández ◽  
María Luisa Vera ◽  
...  
Keyword(s):  
Large Scale ◽  
Northern Spain ◽  
The West ◽  
Deposition Patterns ◽  
Prestige Oil Spill ◽  
Cantabrian Coast ◽  
The Mean ◽  
The Impact ◽  
Oil Tanker ◽  
Fuel Deposition

Following the accident of the oil tanker ‘Prestige’, we surveyed the large scale fuel deposition patterns on the Cantabrian shore (northern Spain) covering three regions (from west to east): (i) Asturias, west of Cape Peñas (24 segments surveyed); (ii) Asturias, east of Cape Peñas (33 segments surveyed); and (iii) Cantabria (also east of Cape Peñas, 256 segments surveyed). Fuel arrived to the Cantabrian Coast as a single oil wave which was more intense to the east than to the west of Cape Peñas. The mean percentage of coast length affected was 25, 41 and 15% in western Asturias, eastern Asturias and Cantabria, respectively. However, less than 10% of the substrate was covered by fuel in oiled patches, thus the impact was moderate. We conclude that these patterns are consistent with fuel transport by the Iberian Poleward Current, a hydrographic feature typical of this region during winter.

Drivers of biases in the extratropical storm tracks in CMIP6

2020 ◽  
Author(s):  
Matthew Priestley ◽  
Duncan Ackerley ◽  
Jennifer Catto ◽  
Kevin Hodges ◽  
Ruth McDonald ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Storm Track ◽  
Storm Tracks ◽  
Extratropical Cyclones ◽  
Ocean Coupling ◽  
Coupled Climate Models ◽  
The Mean ◽  
The Impact

<p>Extratropical cyclones are the leading driver of the day-to-day weather variability and wintertime losses for Europe. In the latest generation of coupled climate models, CMIP6, it is hoped that with improved modelling capabilities come improvements in the structure of the storm track and the associated cyclones. Using an objective cyclone identification and tracking algorithm the mean state of the storm tracks in the CMIP6 models is assessed as well as the representation of explosively deepening cyclones. Any developments and improvements since the previous generation of models in CMIP5 are discussed, with focus on the impact of model resolution on storm track representation. Furthermore, large-scale drivers of any biases are investigated, with particular focus on the role of atmosphere-ocean coupling via associated AMIP simulations and also the influence of large-scale dynamical and thermodynamical features.</p>

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

2020 ◽  
Author(s):  
Alex Sun ◽  
Bridget Scanlon ◽  
Himanshu Save ◽  
Ashraf Rateb
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Large Scale ◽  
Water Storage ◽  
Total Water ◽  
New Paradigm ◽  
Satellite Mission ◽  
Automated Machine Learning ◽  
The Mean ◽  
The Impact

<p>The GRACE satellite mission and its follow-on, GRACE-FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The approximately one-year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. There is strong interest in using machine learning (ML) algorithms to reconstruct GRACE-like data to fill this gap. So far, most studies attempted to train and select a single ML algorithm to work for global basins. However, hydrometeorological predictors may exhibit strong spatial variability which, in turn, may affect the performance of ML models. Existing studies have already shown that no single algorithm consistently outperformed others over all global basins. In this study, we applied an automated machine learning (AutoML) workflow to perform GRACE data reconstruction. AutoML represents a new paradigm for optimal model structure selection, hyperparameter tuning, and model ensemble stacking, addressing some of the most challenging issues related to ML applications. We demonstrated the AutoML workflow over the conterminous U.S. (CONUS) using six types of ML algorithms and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML-assisted gap filling achieved satisfactory performance over the CONUS. For the testing period (2014/06–2017/06), the mean gridwise Nash-Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root-mean square error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE-FO periods (after 2017/06). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end-to-end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large-scale hydrological prediction systems and when predictor importance exhibits strong spatial variability.</p>

scholarly journals An Explanation for the Sensitivity of the Mean State of the Community Atmosphere Model to Horizontal Resolution on Aquaplanets

2017 ◽  
Vol 30 (13) ◽  
pp. 4781-4797 ◽  
Author(s):  
Adam R. Herrington ◽  
Kevin A. Reed
Keyword(s):  
Moisture Transport ◽  
General Circulation ◽  
Large Scale ◽  
General Circulation Models ◽  
Horizontal Resolution ◽  
Hadley Cell ◽  
Community Atmosphere Model ◽  
The Mean ◽  
Atmosphere Model ◽  
Mean State

The sensitivity of the mean state of the Community Atmosphere Model to horizontal resolutions typical of present-day general circulation models is investigated in an aquaplanet configuration. Nonconvergence of the mean state is characterized by a progressive drying of the atmosphere and large reductions in cloud coverage with increasing resolution. Analyses of energy and moisture budgets indicate that these trends are balanced by variations in moisture transport by the resolved circulation, and a reduction in activity of the convection scheme. In contrast, the large-scale precipitation rate increases with resolution, which is approximately balanced by greater advection of dry static energy associated with more active resolved vertical motion in the ascent region of the Hadley cell. An explanation for the sensitivity of the mean state to horizontal resolution is proposed, based on linear Boussinesq theory. The authors hypothesize that an increase in horizontal resolution in the model leads to a reduction in horizontal scale of the diabatic forcing arising from the column physics, facilitating finescale flow and faster resolved convective updrafts within the dynamical core, and steering the coupled system toward a new mean state. This hypothesis attempts to explain the underlying mechanism driving the variations in moisture transport observed in the simulations.

Effect of Lean method implementation in the histopathology section of an anatomical pathology laboratory

2007 ◽  
Vol 61 (11) ◽  
pp. 1193-1199 ◽  
Author(s):  
S S Raab ◽  
D M Grzybicki ◽  
J L Condel ◽  
W R Stewart ◽  
B D Turcsanyi ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Large Scale ◽  
Turnaround Time ◽  
Full Time ◽  
Pathology Laboratory ◽  
Quality Improvement Process ◽  
The Mean ◽  
Work Units ◽  
The Impact ◽  
Productivity Ratio

Background:In the USA, the lack of processes standardisation in histopathology laboratories leads to less than optimal quality, errors, inefficiency and increased costs. The effectiveness of large-scale quality improvement initiatives has been evaluated rarely.Aim:To measure the effect of implementation of a Lean quality improvement process on the efficiency and quality of a histopathology laboratory section.Methods:A non-concurrent interventional cohort study from 1 January 2003 to 31 December 2006 was performed, and the Lean process was implemented on 1 January 2004. Also compared was the productivity of the Lean histopathology section to a sister histopathology section that did not implement Lean processes. Pre- and post-Lean specimen turnaround time and productivity ratios (work units/full time equivalents) were measured. For 200 Lean interventions, a 5-part Likert scale was used to assess the impact on error, success and complexity.Results:In the Lean laboratory, the mean monthly productivity ratio increased from 3439 to 4074 work units/full time equivalents (p<0.001) as the mean daily histopathology section specimen turnaround time decreased from 9.7 to 9.0 h (p = 0.01). The Lean histopathology section had a higher productivity ratio compared with a sister histopathology section (1598 work units/full time equivalents, p<0.001) that did not implement Lean processes. The mean impact, success and complexity of interventions were 2.4, 2.7 and 2.5, respectively. The mean number of specific error causes affected by individual interventions was 2.6.Conclusion:It is concluded that Lean process implementation improved efficiency and quality in the histopathology section.

scholarly journals Investigation of sheet-flow processes based on novel acoustic high-resolution velocity and concentration measurements

2015 ◽  
Vol 767 ◽  
pp. 1-30 ◽  
Author(s):  
Thibaud Revil-Baudard ◽  
Julien Chauchat ◽  
David Hurther ◽  
Pierre-Alain Barraud
Keyword(s):  
Large Scale ◽  
Reynolds Shear Stress ◽  
Sediment Flux ◽  
Ensemble Averaging ◽  
Sheet Flow ◽  
Two Phase ◽  
Interface Position ◽  
Suspension Layer ◽  
The Mean ◽  
The Impact

AbstractA new dataset of uniform and steady sheet-flow experiments is presented in this paper. An acoustic concentration and velocity profiler (ACVP) is used to measure time-resolved profiles of collocated 2C velocity ($u,w$) and sediment concentration and to measure the time evolution of the bed interface position. Ensemble averaging over 11 similar experiment realisations is done to evaluate the mean profiles of streamwise velocity, concentration, sediment flux and Reynolds shear stress. The repeatability, stationarity and uniformity of the flow are carefully checked for a Shields number ${\it\theta}\approx 0.5$ and a suspension number of $S=1.1$. The mean profile analysis allows to separate the flow into two distinct layers: a suspension layer dominated by turbulence and a bed layer dominated by granular interactions. The bed layer can be further subdivided into a frictional layer capped by a collisional layer. In the suspension layer, the mixing length profile is linear with a strongly reduced von Karman parameter equal to 0.225. The Schmidt number is found to be constant in this region with a mean value of ${\it\sigma}_{s}=0.44$. The present results are then interpreted in terms of existing modelling approaches and the underlying assumptions are discussed. In particular, the well-known Rouse profile is shown to predict the concentration profile adequately in the suspension layer provided that all the required parameters can be evaluated separately. However, the strong intermittency of the flow observed in the bed layer under the impact of turbulent large-scale coherent flow structures suggests the limitations of averaged steady two-phase flow models.

scholarly journals The effects of ionization feedback on star formation: a case study of the M 16 H II region

2019 ◽  
Vol 627 ◽  
pp. A27 ◽  
Author(s):  
Jin-Long Xu ◽  
Annie Zavagno ◽  
Naiping Yu ◽  
Xiao-Lan Liu ◽  
Ye Xu ◽  
...  
Keyword(s):  
Star Formation ◽  
Large Scale ◽  
Young Stellar Objects ◽  
Ionized Gas ◽  
Stellar Objects ◽  
Multi Wavelength ◽  
The Mean ◽  
The Impact ◽  
The Relationship

Aims. We aim to investigate the impact of the ionized radiation from the M 16 H II region on the surrounding molecular cloud and on its hosted star formation. Methods. To present comprehensive multi-wavelength observations towards the M 16 H II region, we used new CO data and existing infrared, optical, and submillimeter data. The 12CO J = 1−0, 13CO J = 1−0, and C18O J = 1−0 data were obtained with the Purple Mountain Observatory (PMO) 13.7 m radio telescope. To trace massive clumps and extract young stellar objects (YSOs) associated with the M 16 H II region, we used the ATLASGAL and GLIMPSE I catalogs, respectively. Results. From CO data, we discern a large-scale filament with three velocity components. Because these three components overlap with each other in both velocity and space, the filament may be made of three layers. The M 16 ionized gas interacts with the large-scale filament and has reshaped its structure. In the large-scale filament, we find 51 compact cores from the ATLASGAL catalog, 20 of them being quiescent. The mean excitation temperature of these cores is 22.5 K, while this is 22.2 K for the quiescent cores. This high temperature observed for the quiescent cores suggests that the cores may be heated by M 16 and do not experience internal heating from sources in the cores. Through the relationship between the mass and radius of these cores, we obtain that 45% of all the cores are massive enough to potentially form massive stars. Compared with the thermal motion, the turbulence created by the nonthermal motion is responsible for the core formation. For the pillars observed towards M 16, the H II region may give rise to the strong turbulence.

scholarly journals Process-oriented analysis of aircraft soot-cirrus interactions constrains the climate impact of aviation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Bernd Kärcher ◽  
Fabian Mahrt ◽  
Claudia Marcolli
Keyword(s):  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Climate Impact ◽  
Global Climate Models ◽  
Soot Particles ◽  
Cloud Ice ◽  
The Impact

AbstractFully accounting for the climate impact of aviation requires a process-level understanding of the impact of aircraft soot particle emissions on the formation of ice clouds. Assessing this impact with the help of global climate models remains elusive and direct observations are lacking. Here we use a high-resolution cirrus column model to investigate how aircraft-emitted soot particles, released after ice crystals sublimate at the end of the lifetime of contrails and contrail cirrus, perturb the formation of cirrus. By allying cloud simulations with a measurement-based description of soot-induced ice formation, we find that only a small fraction (<1%) of the soot particles succeeds in forming cloud ice alongside homogeneous freezing of liquid aerosol droplets. Thus, soot-perturbed and homogeneously-formed cirrus fundamentally do not differ in optical depth. Our results imply that climate model estimates of global radiative forcing from interactions between aircraft soot and large-scale cirrus may be overestimates. The improved scientific understanding reported here provides a process-based underpinning for improved climate model parametrizations and targeted field observations.

scholarly journals Generalizing Cloud Overlap Treatment to Include Solar Zenith Angle Effects on Cloud Geometry

2007 ◽  
Vol 64 (6) ◽  
pp. 2116-2125 ◽  
Author(s):  
Adrian M. Tompkins ◽  
Francesca Di Giuseppe
Keyword(s):  
Zenith Angle ◽  
Large Scale ◽  
Solar Zenith Angle ◽  
Forecast Model ◽  
Simple Extension ◽  
Scale Models ◽  
Field Geometry ◽  
The Mean ◽  
The Impact ◽  
First Time

Shortwave radiative transfer depends on the cloud field geometry as viewed from the direction of the sun. To date, the radiation schemes of large-scale models only consider a zenith view of the cloud field, and the apparent change in the cloud geometry with decreasing solar zenith angle is neglected. A simple extension to an existing cloud overlap scheme is suggested to account for this for the first time. It is based on the assumption that at low sun angles, the overlap between cloud elements is random for an unscattered photon. Using cloud scenes derived from radar retrievals at two European sites, it is shown that the increase of the apparent cloud cover with a descending sun is reproduced very well with the new scheme. Associated with this, there is a marked reduction in the mean radiative biases averaged across all solar zenith angles with respect to benchmark calculations. The scheme is implemented into the ECMWF global forecast model using imposed sea surface temperatures, and while the impact on the radiative statistics is significant, the feedback on the large-scale dynamics is minimal.

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