scholarly journals Extreme windstorms and sting jets in convection-permitting climate simulations over Europe

2021 ◽  
Author(s):  
Colin Manning ◽  
Elizabeth J. Kendon ◽  
Hayley J. Fowler ◽  
Nigel M. Roberts ◽  
Ségolène Berthou ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Low Cost ◽  
Global Climate Model ◽  
Surface Wind ◽  
Reanalysis Data ◽  
Large Contribution ◽  
High Wind ◽  
Wind Speeds ◽  
Wind Gusts

AbstractExtra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a sting jet are extremely damaging. A sting jet is a mesoscale core of very high wind speeds that occurs in Shapiro–Keyser type cyclones, and high-resolution models are required to adequately model sting jets. Here, we develop a low-cost methodology to automatically detect sting jets, using the characteristic warm seclusion of Shapiro–Keyser cyclones and the slantwise descent of high wind speeds, within pan-European 2.2 km convection-permitting climate model (CPM) simulations. The representation of wind gusts is improved with respect to ERA-Interim reanalysis data compared to observations; this is linked to better representation of cold conveyor belts and sting jets in the CPM. Our analysis indicates that Shapiro–Keyser cyclones, and those that develop sting jets, are the most damaging windstorms in present and future climates. The frequency of extreme windstorms is projected to increase by 2100 and a large contribution comes from sting jet storms. Furthermore, extreme wind speeds and their future changes are underestimated in the global climate model (GCM) compared to the CPM. We conclude that the CPM adds value in the representation of extreme winds and surface wind gusts and can provide improved input for impact models compared to coarser resolution models.

scholarly journals Extreme windstorms and sting-jets in convection-permitting climate simulations over Europe

2021 ◽  
Author(s):  
Colin Manning ◽  
Elizabeth J. Kendon ◽  
Hayley J. Fowler ◽  
Nigel M. Roberts ◽  
Ségolène Berthou ◽  
...  
Keyword(s):  
Climate Model ◽  
Low Cost ◽  
Surface Wind ◽  
Reanalysis Data ◽  
Large Contribution ◽  
High Wind ◽  
Wind Speeds ◽  
Wind Gusts ◽  
High Resolution Models ◽  
Extreme Wind Speeds

Abstract Extra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a sting-jet are extremely damaging. A sting-jet is a mesoscale core of very high wind speeds that occurs in Shapiro-Keyser type cyclones, and high-resolution models are required to adequately model sting-jets. Here, we develop a low-cost methodology to automatically detect sting jets, using the characteristic warm seclusion of Shapiro-Keyser cyclones and the slantwise descent of high wind speeds, within pan-European 2.2km convection-permitting climate model (CPM) simulations over Europe. The representation of wind gusts is improved with respect to ERA-Interim reanalysis data compared to observations; this is linked to better representation of cold conveyor belts and sting-jets in the CPM. Our analysis indicates that Shapiro-Keyser cyclones, and those that develop sting-jets, are the most damaging windstorms in present and future climates. The frequency of extreme windstorms is projected to increase by 2100 and a large contribution comes from sting-jet storms. Furthermore, extreme wind speeds and their future changes are underestimated in the GCM compared to the CPM. We conclude that the CPM adds value in the representation of extreme winds and surface wind gusts and can provide improved input for impact models compared to coarser resolution models.

Midlatitude cyclones in convection permitting climate simulations: the added value offered for extreme wind speeds and sting-jets

2021 ◽  
Author(s):  
Colin Manning ◽  
Elizabeth Kendon ◽  
Hayley Fowler ◽  
Nigel Roberts ◽  
Segolene Berthou ◽  
...  
Keyword(s):  
Climate Model ◽  
Surface Wind ◽  
Added Value ◽  
Large Contribution ◽  
High Wind ◽  
Wind Speeds ◽  
Wind Gusts ◽  
Climate Simulations ◽  
Extreme Wind ◽  
Extreme Wind Speeds

<p>Extra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a phenomenon known as a sting-jet account for some of the most damaging storms. A sting-jet (SJ) is a mesoscale core of high wind speeds that occurs in particular types of cyclones, specifically Shapiro-Keyser (SK) cyclones, and can produce extremely damaging surface wind gusts. High-resolution climate models are required to adequately model SJs and so it is difficult to gauge their contribution to current and future wind risk. In this study, we develop a low-cost methodology to automate the detection of sting jets, using the characteristic warm seclusion of SK cyclones and the slantwise descent of high wind speeds, within pan-European 2.2km convection-permitting climate model (CPM) simulations. Following this, we quantify the contribution of such storms to wind risk in Northern Europe in current and future climate simulations, and secondly assess the added value offered by the CPM compared to a traditional coarse-resolution climate model. This presentation will give an overview of the developed methods and the results of our analysis.</p><p>Comparing with observations, we find that the representation of wind gusts is improved in the CPM compared to ERA-Interim reanalysis data. Storm severity metrics indicate that SK cyclones account for the majority of the most damaging windstorms. The future simulation produces a large increase (>100%) in the number of storms exceeding high thresholds of the storm metric, with a large contribution to this change (40%) coming from windstorms in which a sting-jet is detected. Finally, we see a systematic underestimation in the GCM compared to the CPM in the frequency of extreme wind speeds at 850hPa in the cold sector of cyclones, likely related to better representation of sting-jets and the cold conveyor belt in the CPM. This underestimation is between 20-40% and increases with increasing wind speed above 35m/s. We conclude that the CPM adds value in the representation of severe surface wind gusts, providing more reliable future projections and improved input for impact models.</p>

scholarly journals Technical Note: Description and assessment of a nudged version of the new dynamics Unified Model

2007 ◽  
Vol 7 (6) ◽  
pp. 17261-17297 ◽  
Author(s):  
P. J. Telford ◽  
P. Braesicke ◽  
O. Morgenstern ◽  
J. A. Pyle
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Technical Note ◽  
Unified Model ◽  
Quantitative Assessments

Abstract. We present a "nudged" version of the Met Office general circulation model, the Unified Model. We constrain this global climate model using ERA-40 reanalysis data with the aim of reproducing the observed "weather" over a year from September 1999. Quantitative assessments are made of its performance, focussing on dynamical aspects of nudging and demonstrating that the "weather" is well simulated.

scholarly journals Accounting for Several Infrared Radiation Processes in Climate Models

2019 ◽  
Vol 32 (15) ◽  
pp. 4601-4620 ◽  
Author(s):  
Kun Wu ◽  
Jiangnan Li ◽  
Jason Cole ◽  
Xianglei Huang ◽  
Knut von Salzen ◽  
...  
Keyword(s):  
Solar Energy ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Wind ◽  
Lower Troposphere ◽  
Single Band ◽  
Tropospheric Temperature ◽  
Radiation Processes

AbstractThree aspects of longwave (LW) radiation processes are investigated using numerical experiments with the Canadian Atmospheric Global Climate Model version 4.3 (CanAM4.3). These are the overlapping LW and shortwave (SW) radiation, scattering by clouds, and specification of ocean emissivity. For the overlapping of solar and infrared spectra, using a single band scheme was compared against a method directly inputting solar energy. Offline calculations show that for high clouds using the single band can cause an overestimate of the downward LW flux, whereas a method that accounts for input solar energy in the LW yields results that are more accurate. Longwave scattering by clouds traps more infrared energy in the atmosphere and reduces the outgoing radiation to space. Simulations with CanAM4.3 show that cloud LW scattering can enhance the LW cooling rate above the tropopause and reduce it inside the troposphere, resulting in warmer temperatures, especially in the tropics and low latitudes. This implies a larger temperature gradient toward the polar region, which causes a strengthening of the Hadley circulation and shifting of the intertropical convergence zone (ITCZ). The increase in lower tropospheric temperature also affects the lower troposphere water vapor and precipitation. Sensitivity to the specification of ocean emissivity is examined by comparing a broadband scheme dependent on the surface wind and solar zenith angle against one that resolves the wavelength dependence. Experiments with CanAM4.3 show that the two oceanic emissivity schemes can produce over 1 W m−2 seasonal mean difference of the upward flux at the surface.

scholarly journals Climatology of Borneo Vortices in the HadGEM3-GC31 General Circulation Model

2021 ◽  
pp. 1-61
Author(s):  
Ju Liang ◽  
Jennifer L. Catto ◽  
Matthew Hawcroft ◽  
Kevin I. Hodges ◽  
Mouleong Tan ◽  
...  
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Deep Convection ◽  
Circulation Model ◽  
Winter Precipitation ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Hadley Centre

AbstractBorneo Vortices (BVs) are intense precipitating winter storms that develop over the equatorial South China Sea and strongly affect the weather and climate over the western Maritime Continent due to their association with deep convection and heavy rainfall. In this study, the ability of the HadGEM3-GC31 (Hadley Centre Global Environment Model 3 - Global Coupled vn. 3.1) global climate model to simulate the climatology of BVs at different horizontal resolutions are examined using an objective feature tracking algorithm. The HadGEM3-GC31 at the N512 ( 25 km) horizontal resolution simulates BVs with well-represented characteristics, including their frequency, spatial distribution and their lower-tropospheric structures when compared with BVs identified in a climate reanalysis, whereas the BVs in the N96 (∼135 km) and N216 (∼65 km) simulations are much weaker and less frequent. Also, the N512 simulation better captures the contribution of BVs to the winter precipitation in Borneo and Malay Peninsula compared with precipitation from a reanalysis data and from observations, while the N96 and N216 simulations underestimate this contribution due to the overly weak low-level convergence of the simulated BVs. The N512 simulation also exhibits an improved ability to reproduce the modulation of BV activity by the occurrence of northeasterly cold surges and active phases of Madden-Julian Oscillation in the region, including increased BV track densities, intensities and lifetimes. A sufficiently high model resolution is thus found to be important to realistically simulate the present-climate precipitation extremes associated with BVs and to study their possible changes in a warmer climate.

scholarly journals Filling the matrix: an ANOVA-based method to emulate regional climate model simulations for equally-weighted properties of ensembles of opportunity

2021 ◽  
Author(s):  
Ole B. Christensen ◽  
Erik Kjellström
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Climate Data ◽  
Large Ensembles ◽  
Climate Model Simulations

AbstractCollections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less arbitrary weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs. Also, the method is only useful when inter-simulation variability is limited. This is the case for the average fields that have been studied, but not for the extremes. We have developed analytical expressions for the degree of improvement obtained with the present method, which quantify this conclusion.

scholarly journals Changes in Surface Wind Speed over North America from CMIP5 Model Projections and Implications for Wind Energy

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sujay Kulkarni ◽  
Huei-Ping Huang
Keyword(s):  
Wind Speed ◽  
North America ◽  
Greenhouse Gas ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Current Estimate

The centennial trends in the surface wind speed over North America are deduced from global climate model simulations in the Climate Model Intercomparison Project—Phase 5 (CMIP5) archive. Using the 21st century simulations under the RCP 8.5 scenario of greenhouse gas emissions, 5–10 percent increases per century in the 10 m wind speed are found over Central and East-Central United States, the Californian Coast, and the South and East Coasts of the USA in winter. In summer, climate models projected decreases in the wind speed ranging from 5 to 10 percent per century over the same coastal regions. These projected changes in the surface wind speed are moderate and imply that the current estimate of wind power potential for North America based on present-day climatology will not be significantly changed by the greenhouse gas forcing in the coming decades.

The Impact of Climate Change on Air Quality–Related Meteorological Conditions in California. Part I: Present Time Simulation Analysis

2011 ◽  
Vol 24 (13) ◽  
pp. 3344-3361 ◽  
Author(s):  
Zhan Zhao ◽  
Shu-Hua Chen ◽  
Michael J. Kleeman ◽  
Mary Tyree ◽  
Dan Cayan
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Surface Temperature ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Wind ◽  
Valley Wind ◽  
Simulation Results ◽  
The Impact

Abstract This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years’ (2000–06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality–related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047–53) and present (2000–06) simulation results driven by PCM data and is presented in a separate paper.

scholarly journals Filling the Matrix: An ANOVA-Based Method to Emulate Regional Climate Model Simulations for Equally-Weighted Properties of Ensembles of Opportunity

2021 ◽  
Author(s):  
Ole Bøssing Christensen ◽  
Erik Kjellström
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Climate Data ◽  
Large Ensembles ◽  
Climate Model Simulations

Abstract Collections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less random weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs.

scholarly journals Reconciling high resolution climate datasets using KrigR

2021 ◽  
Author(s):  
Richard Davy ◽  
Erik Kusch
Keyword(s):  
High Resolution ◽  
Temporal Resolution ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
R Package ◽  
Reanalysis Data ◽  
Climate Data ◽  
Community Of Researchers ◽  
The Difference

Abstract There is an increasing need for high spatial and temporal resolution climate data for the wide community of researchers interested in climate change and its consequences. Currently, there is a large mismatch between the spatial resolutions of global climate model and reanalysis datasets (at best around 0.25o and 0.1o respectively) and the resolutions needed by many end-users of these datasets, which are typically on the scale of 30 arcseconds (~900m). This need for improved spatial resolution in climate datasets has motivated several groups to statistically downscale various combinations of observational or reanalysis datasets. However, the variety of downscaling methods and inputs used makes it difficult to reconcile the resultant differences between these high-resolution datasets. Here we make use of the KrigR R-package to statistically downscale the world-leading ERA5(-Land) reanalysis data using kriging. We show that kriging can accurately recover spatial heterogeneity of climate data given strong relationships with co-variates; that by preserving the uncertainty associated with the statistical downscaling, one can investigate and account for confidence in high-resolution climate data; and that the statistical uncertainty provided by KrigR can explain much of the difference between widely used high resolution climate datasets (CHELSA, TerraClimate, and WorldClim2) depending on variable, timescale, and region. This demonstrates the advantages of using KrigR to generate customized high spatial and/or temporal resolution climate data.

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