scholarly journals The Cloud-to-Ground Lightning Parameterization Development with the Canadian Local Climate Model

2016 ◽  
Author(s):  
Abderrazak Arif
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Warm Season ◽  
Precipitable Water ◽  
Absolute Error ◽  
Lightning Activity ◽  
Static State ◽  
Local Climate ◽  
Freezing Level ◽  
Cg Lightning

We use the third version of the Canadian Local Climate Model  as a diagnostic tool to study the climatology of observed CG lightning activity at Maniwaki (latitude: 46,23°N; Longitude: 75,58°W). We examine the dependence between the hourly lightning activity and the related atmospheric variables during the warm season of sixteen years (between 1984 and 2004). The goal of this research is: a) to evaluate the atmospheric static state evolution and its moisture contents for conditions having generated lightning occurrence, b) to develop a CG lightning parameterization, and c) to verify this CG lightning parameterization on other Canadian areas. The freezing level altitude and the precipitable water content are used to estimate the static air instability and its moisture content respectively. These two parameters are served to develop the CG lightning parameterization. A comparison between the observations and simulations CG lightning occurrence and frequency at Maniwaki showed a mean absolute error rate of 27% and 55% respectively. We apply this parameterization at four Canadian regions, distributed from west to east. The simulated CG lightning results are comparable to observed CG lightning at Maniwaki and tested regions. The application of the CG lightning parameterization to the daily data enabled us to find the monthly results. This application represents a preliminary stage for validation this parameterization in regional numerical models in Canada during the historic period.

scholarly journals Distinct aerosol effects on cloud-to-ground lightning in the plateau and basin regions of Sichuan, Southwest China

2020 ◽  
Vol 20 (21) ◽  
pp. 13379-13397
Author(s):  
Pengguo Zhao ◽  
Zhanqing Li ◽  
Hui Xiao ◽  
Fang Wu ◽  
Youtong Zheng ◽  
...  
Keyword(s):  
Cloud Droplet ◽  
Lightning Activity ◽  
Freezing Process ◽  
Plateau Region ◽  
Freezing Level ◽  
Aerosol Loading ◽  
Cloud To Ground Lightning ◽  
Basin Region ◽  
Total Column ◽  
Cg Lightning

Abstract. The joint effects of aerosol, thermodynamic, and cloud-related factors on cloud-to-ground lightning in Sichuan were investigated by a comprehensive analysis of ground-based measurements made from 2005 to 2017 in combination with reanalysis data. Data include aerosol optical depth, cloud-to-ground (CG) lightning density, convective available potential energy (CAPE), mid-level relative humidity, lower- to mid-tropospheric vertical wind shear, cloud-base height, total column liquid water (TCLW), and total column ice water (TCIW). Results show that CG lightning density and aerosols are positively correlated in the plateau region and negatively correlated in the basin region. Sulfate aerosols are found to be more strongly associated with lightning than total aerosols, so this study focuses on the role of sulfate aerosols in lightning activity. In the plateau region, the lower aerosol concentration stimulates lightning activity through microphysical effects. Increasing the aerosol loading decreases the cloud droplet size, reducing the cloud droplet collision–coalescence efficiency and inhibiting the warm-rain process. More small cloud droplets are transported above the freezing level to participate in the freezing process, forming more ice particles and releasing more latent heat during the freezing process. Thus, an increase in the aerosol loading increases CAPE, TCLW, and TCIW, stimulating CG lightning in the plateau region. In the basin region, by contrast, the higher concentration of aerosols inhibits lightning activity through the radiative effect. An increase in the aerosol loading reduces the amount of solar radiation reaching the ground, thereby lowering the CAPE. The intensity of convection decreases, resulting in less supercooled water being transported to the freezing level and fewer ice particles forming, thereby increasing the total liquid water content. Thus, an increase in the aerosol loading suppresses the intensity of convective activity and CG lightning in the basin region.

scholarly journals Assessment of Local Climate Change: Historical Trends and RCM Multi-Model Projections Over the Salento Area (Italy)

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 978 ◽  
Author(s):  
Marco D’Oria ◽  
Maria Tanda ◽  
Valeria Todaro
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Regional Climate ◽  
Warm Season ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Local Climate

This study provides an up-to-date analysis of climate change over the Salento area (southeast Italy) using both historical data and multi-model projections of Regional Climate Models (RCMs). The accumulated anomalies of monthly precipitation and temperature records were analyzed and the trends in the climate variables were identified and quantified for two historical periods. The precipitation trends are in almost all cases not significant while the temperature shows statistically significant increasing tendencies especially in summer. A clear changing point around the 80s and at the end of the 90s was identified by the accumulated anomalies of the minimum and maximum temperature, respectively. The gradual increase of the temperature over the area is confirmed by the climate model projections, at short—(2016–2035), medium—(2046–2065) and long-term (2081–2100), provided by an ensemble of 13 RCMs, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). All the models agree that the mean temperature will rise over this century, with the highest increases in the warm season. The total annual rainfall is not expected to significantly vary in the future although systematic changes are present in some months: a decrease in April and July and an increase in November. The daily temperature projections of the RCMs were used to identify potential variations in the characteristics of the heat waves; an increase of their frequency is expected over this century.

scholarly journals Distinct aerosol effects on cloud-to-ground lightning in the plateau and basin regions of Sichuan, Southwest China

2020 ◽  
Author(s):  
Pengguo Zhao ◽  
Zhanqing Li ◽  
Hui Xiao ◽  
Fang Wu ◽  
Youtong Zheng ◽  
...  
Keyword(s):  
Cloud Droplet ◽  
Lightning Activity ◽  
Freezing Process ◽  
Plateau Region ◽  
Freezing Level ◽  
Aerosol Loading ◽  
Cloud To Ground Lightning ◽  
Basin Region ◽  
Total Column ◽  
Cg Lightning

Abstract. The joint effects of aerosol, thermodynamic, and cloud-related factors on cloud-to-ground lightning in Sichuan were investigated by a comprehensive analysis of ground measurements made from 2005 to 2017 in combination with reanalysis data. Data include aerosol optical depth, cloud-to-ground (CG) lightning density, convective available potential energy (CAPE), mid-level relative humidity, lower- to mid-tropospheric vertical wind shear, cloud-base height, total column liquid water (TCLW), and total column ice water (TCIW). Results show that CG lightning density and aerosols are positively correlated in the plateau region and negatively correlated in the basin region. Sulfate aerosols are found to be more strongly associated with lightning than total aerosols, so this study focuses on the role of sulfate aerosols in lightning activity. In the plateau region, the lower aerosol concentration stimulates lightning activity through microphysical effects. Increasing the aerosol loading reduces the cloud droplet size, reducing the cloud droplet collision-coalescence efficiency and inhibiting the warm-rain process. More small cloud droplets are transported above the freezing level to participate in the freezing process, forming more ice particles and releasing more latent heat during the freezing process. Thus, an increase in aerosol loading increases CAPE, TCLW, and TCIW, stimulating CG lightning in the plateau region. In the basin region, by contrast, the higher concentration of aerosols inhibits lightning activity through the radiative effect. An increase in aerosol loading reduces the amount of solar radiation reaching the ground, thereby lowering CAPE. The intensity of convection decreases, resulting in less supercooled water transported to the freezing level and fewer ice particles forming, thus increasing the total liquid water content. Therefore, an increase in aerosol loading suppresses the intensity of convective activity and CG lightning in the basin region.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

NAM Model Forecasts of Warm-Season Quasi-Stationary Frontal Environments in the Central United States

2010 ◽  
Vol 25 (4) ◽  
pp. 1281-1292 ◽  
Author(s):  
Shih-Yu Wang ◽  
Adam J. Clark
Keyword(s):  
United States ◽  
Mesoscale Model ◽  
Convective Available Potential Energy ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Precipitable Water ◽  
Wind Fields ◽  
Vapor Flux ◽  
Convective Systems ◽  
Central United States

Abstract Using a composite procedure, North American Mesoscale Model (NAM) forecast and observed environments associated with zonally oriented, quasi-stationary surface fronts for 64 cases during July–August 2006–08 were examined for a large region encompassing the central United States. NAM adequately simulated the general synoptic features associated with the frontal environments (e.g., patterns in the low-level wind fields) as well as the positions of the fronts. However, kinematic fields important to frontogenesis such as horizontal deformation and convergence were overpredicted. Surface-based convective available potential energy (CAPE) and precipitable water were also overpredicted, which was likely related to the overprediction of the kinematic fields through convergence of water vapor flux. In addition, a spurious coherence between forecast deformation and precipitation was found using spatial correlation coefficients. Composite precipitation forecasts featured a broad area of rainfall stretched parallel to the composite front, whereas the composite observed precipitation covered a smaller area and had a WNW–ESE orientation relative to the front, consistent with mesoscale convective systems (MCSs) propagating at a slight right angle relative to the thermal gradient. Thus, deficiencies in the NAM precipitation forecasts may at least partially result from the inability to depict MCSs properly. It was observed that errors in the precipitation forecasts appeared to lag those of the kinematic fields, and so it seems likely that deficiencies in the precipitation forecasts are related to the overprediction of the kinematic fields such as deformation. However, no attempts were made to establish whether the overpredicted kinematic fields actually contributed to the errors in the precipitation forecasts or whether the overpredicted kinematic fields were simply an artifact of the precipitation errors. Regardless of the relationship between such errors, recognition of typical warm-season environments associated with these errors should be useful to operational forecasters.

scholarly journals A Mechanistically Credible, Poleward Shift in Warm-Season Precipitation Projected for the U.S. Southern Great Plains?

2017 ◽  
Vol 30 (20) ◽  
pp. 8275-8298 ◽  
Author(s):  
Melissa S. Bukovsky ◽  
Rachel R. McCrary ◽  
Anji Seth ◽  
Linda O. Mearns
Keyword(s):  
Great Plains ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Dry Season ◽  
Wet Season ◽  
Southern Great Plains ◽  
Poleward Shift

Abstract Global and regional climate model ensembles project that the annual cycle of rainfall over the southern Great Plains (SGP) will amplify by midcentury. Models indicate that warm-season precipitation will increase during the early spring wet season but shift north earlier in the season, intensifying late summer drying. Regional climate models (RCMs) project larger precipitation changes than their global climate model (GCM) counterparts. This is particularly true during the dry season. The credibility of the RCM projections is established by exploring the larger-scale dynamical and local land–atmosphere feedback processes that drive future changes in the simulations, that is, the responsible mechanisms or processes. In this case, it is found that out of 12 RCM simulations produced for the North American Regional Climate Change Assessment Program (NARCCAP), the majority are mechanistically credible and consistent in the mean changes they are producing in the SGP. Both larger-scale dynamical processes and local land–atmosphere feedbacks drive an earlier end to the spring wet period and deepening of the summer dry season in the SGP. The midlatitude upper-level jet shifts northward, the monsoon anticyclone expands, and the Great Plains low-level jet increases in strength, all supporting a poleward shift in precipitation in the future. This dynamically forced shift causes land–atmosphere coupling to strengthen earlier in the summer, which in turn leads to earlier evaporation of soil moisture in the summer, resulting in extreme drying later in the summer.

Accelerating Climate Model Computation by Neural Networks: A Comparative Study

2021 ◽  
Author(s):  
Maha Mdini ◽  
Takemasa Miyoshi ◽  
Shigenori Otsuka
Keyword(s):  
Neural Networks ◽  
Computational Complexity ◽  
Statistical Model ◽  
Physical Model ◽  
Climate Model ◽  
Numerical Models ◽  
Computation Time ◽  
High Accuracy ◽  
Physical Models ◽  
Computer Resources

<p>In the era of modern science, scientists have developed numerical models to predict and understand the weather and ocean phenomena based on fluid dynamics. While these models have shown high accuracy at kilometer scales, they are operated with massive computer resources because of their computational complexity.  In recent years, new approaches to solve these models based on machine learning have been put forward. The results suggested that it be possible to reduce the computational complexity by Neural Networks (NNs) instead of classical numerical simulations. In this project, we aim to shed light upon different ways to accelerating physical models using NNs. We test two approaches: Data-Driven Statistical Model (DDSM) and Hybrid Physical-Statistical Model (HPSM) and compare their performance to the classical Process-Driven Physical Model (PDPM). DDSM emulates the physical model by a NN. The HPSM, also known as super-resolution, uses a low-resolution version of the physical model and maps its outputs to the original high-resolution domain via a NN. To evaluate these two methods, we measured their accuracy and their computation time. Our results of idealized experiments with a quasi-geostrophic model [SO3] show that HPSM reduces the computation time by a factor of 3 and it is capable to predict the output of the physical model at high accuracy up to 9.25 days. The DDSM, however, reduces the computation time by a factor of 4 and can predict the physical model output with an acceptable accuracy only within 2 days. These first results are promising and imply the possibility of bringing complex physical models into real time systems with lower-cost computer resources in the future.</p>

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

scholarly journals Atmospheric circulation and hydroclimate impacts of alternative warming scenarios for the Eocene

2017 ◽  
Vol 13 (8) ◽  
pp. 1037-1048 ◽  
Author(s):  
Henrik Carlson ◽  
Rodrigo Caballero
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Cloud Albedo ◽  
Cloud Properties ◽  
Atmospheric Co2 Concentrations ◽  
Global Mean

Abstract. Recent work in modelling the warm climates of the early Eocene shows that it is possible to obtain a reasonable global match between model surface temperature and proxy reconstructions, but only by using extremely high atmospheric CO2 concentrations or more modest CO2 levels complemented by a reduction in global cloud albedo. Understanding the mix of radiative forcing that gave rise to Eocene warmth has important implications for constraining Earth's climate sensitivity, but progress in this direction is hampered by the lack of direct proxy constraints on cloud properties. Here, we explore the potential for distinguishing among different radiative forcing scenarios via their impact on regional climate changes. We do this by comparing climate model simulations of two end-member scenarios: one in which the climate is warmed entirely by CO2 (which we refer to as the greenhouse gas (GHG) scenario) and another in which it is warmed entirely by reduced cloud albedo (which we refer to as the low CO2–thin clouds or LCTC scenario) . The two simulations have an almost identical global-mean surface temperature and equator-to-pole temperature difference, but the LCTC scenario has  ∼  11 % greater global-mean precipitation than the GHG scenario. The LCTC scenario also has cooler midlatitude continents and warmer oceans than the GHG scenario and a tropical climate which is significantly more El Niño-like. Extremely high warm-season temperatures in the subtropics are mitigated in the LCTC scenario, while cool-season temperatures are lower at all latitudes. These changes appear large enough to motivate further, more detailed study using other climate models and a more realistic set of modelling assumptions.

scholarly journals Synoptic Conditions, Clouds, and Sea Ice Melt Onset in the Beaufort and Chukchi Seasonal Ice Zone

2017 ◽  
Vol 30 (17) ◽  
pp. 6999-7016 ◽  
Author(s):  
Zheng Liu ◽  
Axel Schweiger
Keyword(s):  
Sea Ice ◽  
Numerical Models ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Cloud Microphysics ◽  
Low Level ◽  
Ice Melt ◽  
Synoptic Conditions ◽  
High Level ◽  
Seasonal Ice Zone

Cloud response to synoptic conditions over the Beaufort and Chukchi seasonal ice zone is examined. Four synoptic states with distinct thermodynamic and dynamic signatures are identified using ERA-Interim reanalysis data from 2000 to 2014. CloudSat and CALIPSO observations suggest control of clouds by synoptic states. Warm continental air advection is associated with the fewest low-level clouds, while cold air advection generates the most low-level clouds. Low-level clouds are related to lower-tropospheric stability and both are regulated by synoptic conditions. High-level clouds are associated with humidity and vertical motions in the upper atmosphere. Observed cloud vertical and spatial variability is reproduced well in ERA-Interim, but winter low-level cloud fraction is overestimated. This suggests that synoptic conditions constrain the spatial extent of clouds through the atmospheric structure, while the parameterizations for cloud microphysics and boundary layer physics are critical for the life cycle of clouds in numerical models. Sea ice melt onset is related to synoptic conditions. Melt onsets occur more frequently and earlier with warm air advection. Synoptic conditions with the highest temperatures and precipitable water are most favorable for melt onsets even though fewer low-level clouds are associated with these conditions.

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