scholarly journals Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag

2016 ◽  
Vol 43 (13) ◽  
pp. 7231-7240 ◽  
Author(s):  
Felix Pithan ◽  
Theodore G. Shepherd ◽  
Giuseppe Zappa ◽  
Irina Sandu
Keyword(s):  
Climate Model ◽  
Storm Tracks ◽  
Jet Streams ◽  
Model Biases ◽  
Orographic Drag

scholarly journals The response of the Northern Hemisphere storm tracks and jetstreams to climate change in the CMIP3, CMIP5, and CMIP6 climate models

2020 ◽  
Author(s):  
Ben Harvey ◽  
Peter Cook ◽  
Len Shaffrey ◽  
Reinhard Schiemann
Keyword(s):  
Climate Change ◽  
Spatial Pattern ◽  
North Atlantic ◽  
Climate Models ◽  
Climate Model ◽  
Storm Track ◽  
Coupled Model ◽  
Storm Tracks ◽  
Cmip5 Models ◽  
Jet Streams

<p>Understanding and predicting how extratropical cyclones might respond to climate change is essential for assessing future weather risks and informing climate change adaptation strategies. Climate model simulations provide a vital component of this assessment, with the caveat that their representation of the present-day climate is adequate. In this study the representation of the NH storm tracks and jet streams and their responses to climate change are evaluated across the three major phases of the Coupled Model Intercomparison Project: CMIP3 (2007), CMIP5 (2012), and CMIP6 (2019). The aim is to quantity how present-day biases in the NH storm tracks and jet streams have evolved with model developments, and to further our understanding of their responses to climate change.</p><p>The spatial pattern of the present-day biases in CMIP3, CMIP5, and CMIP6 are similar. However, the magnitude of the biases in the CMIP6 models is substantially lower in the DJF North Atlantic storm track and jet stream than in the CMIP3 and CMIP5 models. In summer, the biases in the JJA North Atlantic and North Pacific storm tracks are also much reduced in the CMIP6 models. Despite this, the spatial pattern of the climate change response in the NH storm tracks and jet streams are similar across the CMIP3, CMIP5, and CMIP6 ensembles. The SSP2-4.5 scenario responses in the CMIP6 models are substantially larger than in the corresponding RCP4.5 CMIP5 models, consistent with the larger climate sensitivities of the CMIP6 models compared to CMIP5.</p>

Observed Associations Among Storm Tracks, Jet Streams and Midlatitude Oceanic Fronts

2013 ◽  
pp. 329-345 ◽  
Author(s):  
Hisashi Nakamura ◽  
Takeaki Sampe ◽  
Youichi Tanimoto ◽  
Akihiko Shimpo
Keyword(s):  
Storm Tracks ◽  
Jet Streams ◽  
Oceanic Fronts

Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

2018 ◽  
Vol 31 (16) ◽  
pp. 6591-6610 ◽  
Author(s):  
Martin Aleksandrov Ivanov ◽  
Jürg Luterbacher ◽  
Sven Kotlarski
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Analytical Description ◽  
Analytical Theory ◽  
Climate Change Signal ◽  
Future Research ◽  
Model Biases

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

scholarly journals Exploring the sensitivity of Northern Hemisphere atmospheric circulation to different surface temperature forcing using a statistical–dynamical atmospheric model

2019 ◽  
Vol 26 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Sonja Totz ◽  
Stefan Petri ◽  
Jascha Lehmann ◽  
Erik Peukert ◽  
Dim Coumou
Keyword(s):  
Temperature Gradient ◽  
Large Scale ◽  
Planetary Waves ◽  
Hadley Cell ◽  
Storm Tracks ◽  
Meridional Temperature Gradient ◽  
Jet Streams ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Abstract. Climate and weather conditions in the mid-latitudes are strongly driven by the large-scale atmosphere circulation. Observational data indicate that important components of the large-scale circulation have changed in recent decades, including the strength and the width of the Hadley cell, jets, storm tracks and planetary waves. Here, we use a new statistical–dynamical atmosphere model (SDAM) to test the individual sensitivities of the large-scale atmospheric circulation to changes in the zonal temperature gradient, meridional temperature gradient and global-mean temperature. We analyze the Northern Hemisphere Hadley circulation, jet streams, storm tracks and planetary waves by systematically altering the zonal temperature asymmetry, the meridional temperature gradient and the global-mean temperature. Our results show that the strength of the Hadley cell, storm tracks and jet streams depend, in terms of relative changes, almost linearly on both the global-mean temperature and the meridional temperature gradient, whereas the zonal temperature asymmetry has little or no influence. The magnitude of planetary waves is affected by all three temperature components, as expected from theoretical dynamical considerations. The width of the Hadley cell behaves nonlinearly with respect to all three temperature components in the SDAM. Moreover, some of these observed large-scale atmospheric changes are expected from dynamical equations and are therefore an important part of model validation.

Statistical Projection of the North Atlantic Storm Tracks

2019 ◽  
Vol 58 (7) ◽  
pp. 1509-1522 ◽  
Author(s):  
Kajsa M. Parding ◽  
Rasmus Benestad ◽  
Abdelkader Mezghani ◽  
Helene B. Erlandsen
Keyword(s):  
North Atlantic ◽  
Geopotential Height ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Storm Tracks ◽  
Cmip5 Models ◽  
The North ◽  
The Future ◽  
The North Atlantic

AbstractA method for empirical–statistical downscaling was adapted to project seasonal cyclone density over the North Atlantic Ocean. To this aim, the seasonal mean cyclone density was derived from instantaneous values of the 6-h mean sea level pressure (SLP) reanalysis fields. The cyclone density was then combined with seasonal mean reanalysis and global climate model projections of SLP or 500-hPa geopotential height to obtain future projections of the North Atlantic storm tracks. The empirical–statistical approach is computationally efficient because it makes use of seasonally aggregated cyclone statistics and allows the future cyclone density to be estimated from the full ensemble of available CMIP5 models rather than from a smaller subset. However, the projected cyclone density in the future differs considerably depending on the choice of predictor, SLP, or 500-hPa geopotential height. This discrepancy suggests that the relationship between the cyclone density and SLP, 500-hPa geopotential height, or both is nonstationary; that is, that the statistical model depends on the calibration period. A stationarity test based on 6-hourly HadGEM2-ES data indicated that the 500-hPa geopotential height was not a robust predictor of cyclone density.

Scale-dependent analysis of climate model biases in relation to dynamics

2020 ◽  
Author(s):  
Katarina Kosovelj ◽  
Nedjeljka Žagar
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Physical Space ◽  
Single Mode ◽  
Kelvin Wave ◽  
Bias Analysis ◽  
Simultaneous Treatment ◽  
Model Biases ◽  
Bias Variance

<p>The assessment of climate model biases in an important part of their validation, in particular with respect to the application of the outputs of global models as lateral boundaries in regional climate models. The coupled nature of thermodynamics and circulation asks for their simultaneous treatment in the model bias analysis. This can be achieved by applying the normal-mode decomposition of model outputs and reanalysis that provides biases associated with the two dominant atmospheric regimes, the Rossby (or balanced) and inertia-gravity (or unbalanced) regime. The regime decomposition provides the spectrum of bias in terms of zonal wavenumbers, meridional modes and vertical modes. This can be especially useful in the tropics, where the Rossby and IG regimes are difficult to separate and biases in simulated circulation, just like the circulation itself, have global impacts. </p><p>The method is applied to the intermediate complexity climate model SPEEDY. Fifty-year long simulations  are performed in AMIP-mode with the prescribed SST. Biases are computed with respect to ERA-20C  upscaled to the resolution of SPEEDY (T30L8). We evaluate biases both in modal and physical space and study regional biases associated with the  balanced and unbalanced components of circulation. This work thus expands the results presented by Žagar et al. (2019, Clim. Dyn.) to the two regimes-related bias analysis..</p><p>The results show that the bias is strongly scale dependent, just like the simulated variability. The largest biases in SPEEDY are at planetary scales (waveumbers 0-3). Biases associated with the extratropical Rossby modes explain more than the half of bias variance. The Rossby n=1 mode is a single mode with the largest bias variance in balanced circulation whereas the Kelvin wave contains the largest bias among the IG modes. These biases are shown to originate mostly in the stratosphere and the upper-troposphere westerlies in the Southern hemisphere. </p>

A global perspective on CMIP5 climate model biases

2014 ◽  
Vol 4 (3) ◽  
pp. 201-205 ◽  
Author(s):  
Chunzai Wang ◽  
Liping Zhang ◽  
Sang-Ki Lee ◽  
Lixin Wu ◽  
Carlos R. Mechoso
Keyword(s):  
Climate Model ◽  
Global Perspective ◽  
Model Biases

scholarly journals Assessment of Bias Assumptions for Climate Models

2014 ◽  
Vol 27 (17) ◽  
pp. 6799-6818 ◽  
Author(s):  
Christian Kerkhoff ◽  
Hans R. Künsch ◽  
Christoph Schär
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Lower Troposphere ◽  
Regional Climate Models ◽  
Added Value ◽  
Model Biases ◽  
Interaction Component

Abstract Climate scenarios make implicit or explicit assumptions about the extrapolation of climate model biases from current to future time periods. Such assumptions are inevitable because of the lack of future observations. This manuscript reviews different bias assumptions found in the literature and provides measures to assess their validity. The authors explicitly separate climate change from multidecadal variability to systematically analyze climate model biases in seasonal and regional surface temperature averages, using global and regional climate models (GCMs and RCMs) from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project over Europe. For centennial time scales, it is found that a linear bias extrapolation for GCMs is best supported by the analysis: that is, it is generally not correct to assume that model biases are independent of the climate state. Results also show that RCMs behave markedly differently when forced with different drivers. RCM and GCM biases are not additive, and there is a significant interaction component in the bias of the RCM–GCM model chain that depends on both the RCM and GCM considered. This result questions previous studies that deduce biases (and ultimately projections) in RCM–GCM combinations from reanalysis-driven simulations. The authors suggest that the aforementioned interaction component derives from the refined RCM representation of dynamical and physical processes in the lower troposphere, which may nonlinearly depend upon the larger-scale circulation stemming from the driving GCM. The authors’ analyses also show that RCMs provide added value and that the combined RCM–GCM approach yields, in general, smaller biases in seasonal surface temperature and interannual variability, particularly in summer and even for spatial scales that are, in principle, well resolved by the GCMs.

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