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

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.

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>

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.

Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions

2013 ◽  
Vol 43 (1-2) ◽  
pp. 289-303 ◽  
Author(s):  
Felix Pithan ◽  
Brian Medeiros ◽  
Thorsten Mauritsen
Keyword(s):  
Climate Model ◽  
Mixed Phase ◽  
Model Biases ◽  
Temperature Inversions ◽  
Mixed Phase Clouds

scholarly journals Assessing the sensitivity of the hydroxyl radical to model biases in composition and temperature using a single-column photochemical model for Lauder, New Zealand

2016 ◽  
Vol 16 (22) ◽  
pp. 14599-14619 ◽  
Author(s):  
Laura López-Comí ◽  
Olaf Morgenstern ◽  
Guang Zeng ◽  
Sarah L. Masters ◽  
Richard R. Querel ◽  
...  
Keyword(s):  
New Zealand ◽  
Hydroxyl Radical ◽  
Atmospheric Chemistry ◽  
Climate Model ◽  
Photochemical Model ◽  
Model Biases ◽  
Photolysis Rate ◽  
Single Column ◽  
Major Factors ◽  
The Impact

Abstract. We assess the major factors contributing to local biases in the hydroxyl radical (OH) as simulated by a global chemistry–climate model, using a single-column photochemical model (SCM) analysis. The SCM has been constructed to represent atmospheric chemistry at Lauder, New Zealand, which is representative of the background atmosphere of the Southern Hemisphere (SH) mid-latitudes. We use long-term observations of variables essential to tropospheric OH chemistry, i.e. ozone (O3), water vapour (H2O), methane (CH4), carbon monoxide (CO), and temperature, and assess how using these measurements affect OH calculated in the SCM, relative to a reference simulation only using modelled fields. The analysis spans 1994 to 2010. Results show that OH responds approximately linearly to correcting biases in O3, H2O, CO, CH4, and temperature. The biggest impact on OH is due to correcting an overestimation by approximately 20 to 60 % of H2O, using radiosonde observations. Correcting this moist bias leads to a reduction of OH by around 5 to 35 %. This is followed by correcting predominantly overestimated O3. In the troposphere, the model biases are mostly in the range of −10 to 30 %. The impact of changing O3 on OH is due to two pathways; the OH responses to both are of similar magnitude but different seasonality: correcting in situ tropospheric ozone leads to changes in OH in the range −14 to 4 %, whereas correcting the photolysis rate of O3 in accordance with overhead column ozone changes leads to increases of OH of 8 to 16 %. The OH sensitivities to correcting CH4, CO, and temperature biases are all minor effects. The work demonstrates the feasibility of quantitatively assessing OH sensitivity to biases in longer-lived species, which can help explain differences in simulated OH between global chemistry models and relative to observations. In addition to clear-sky simulations, we have performed idealized sensitivity simulations to assess the impact of clouds (ice and liquid) on OH. The results indicate that the impacts on the ozone photolysis rate and OH are substantial, with a general decrease of OH below the clouds of up to 30 % relative to the clear-skies situation, and an increase of up to 15 % above. Using the SCM simulation we calculate recent OH trends at Lauder. For the period of 1994 to 2010, all trends are insignificant, in agreement with previous studies. For example, the trend in total-column OH is 0.5 ± 1.3 % over this period.

scholarly journals Earth System Chemistry Integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy, version 2.51)

2015 ◽  
Vol 8 (10) ◽  
pp. 8635-8750 ◽  
Author(s):  
P. Jöckel ◽  
H. Tost ◽  
A. Pozzer ◽  
M. Kunze ◽  
O. Kirner ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Climate Model ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Global Perspective ◽  
Integrated Modelling ◽  
Sources And Sinks ◽  
Tropospheric Aerosol ◽  
On Line ◽  
Quality Check

Abstract. With version 2.51 of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model three types of reference simulations as recommended by the Chemistry-Climate Model Initiative (CCMI) have been performed: hindcast simulations (1950–2011), hindcast simulations with specified dynamics (1978–2013), i.e., nudged towards ERA-Interim reanalysis data, and combined hindcast and projection simulations (1950–2100). The manuscript summarises the updates of the model system and details the different model setups used, including the on-line calculated diagnostics. Simulations have been performed with two different nudging setups, with and without interactive tropospheric aerosol, and with and without a coupled ocean model. Two different vertical resolutions have been applied. The on-line calculated sources and sinks of reactive species are quantified and a first evaluation of the simulation results from a global perspective is provided as a quality check of the data. The focus is on the inter-comparison of the different model setups. The simulation data will become publicly available via CCMI and the CERA database of the German Climate Computing Centre (DKRZ). This manuscript is intended to serve as an extensive reference for further analyses of the ESCiMo simulations.

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

Metrics and Diagnostics for Precipitation-Related Processes in Climate Model Short-Range Hindcasts

2013 ◽  
Vol 26 (5) ◽  
pp. 1516-1534 ◽  
Author(s):  
H.-Y. Ma ◽  
S. Xie ◽  
J. S. Boyle ◽  
S. A. Klein ◽  
Y. Zhang
Keyword(s):  
Short Range ◽  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Regional Analysis ◽  
Atmospheric Model ◽  
Department Of Energy ◽  
Temperature Fields ◽  
Lower Probability ◽  
Model Biases

Abstract In this study, several metrics and diagnostics are proposed and implemented to systematically explore and diagnose climate model biases in short-range hindcasts and quantify how fast hindcast biases approach to climate biases with an emphasis on tropical precipitation and associated moist processes. A series of 6-day hindcasts with NCAR and the U.S. Department of Energy Community Atmosphere Model, version 4 (CAM4) and version 5 (CAM5), were performed and initialized with ECMWF operational analysis every day at 0000 UTC during the Year of Tropical Convection (YOTC). An Atmospheric Model Intercomparison Project (AMIP) type of ensemble climate simulations was also conducted for the same period. The analyses indicate that initial drifts in precipitation and associated moisture processes (“fast processes”) can be identified in the hindcasts, and the biases share great resemblance to those in the climate runs. Comparing to Tropical Rainfall Measuring Mission (TRMM) observations, model hindcasts produce too high a probability of low- to intermediate-intensity precipitation at daily time scales during northern summers, which is consistent with too frequently triggered convection by its deep convection scheme. For intense precipitation events (>25 mm day−1), however, the model produces a much lower probability partially because the model requires a much higher column relative humidity than observations to produce similar precipitation intensity as indicated by the proposed diagnostics. Regional analysis on precipitation bias in the hindcasts is also performed for two selected locations where most contemporary climate models show the same sign of bias. Based on moist static energy diagnostics, the results suggest that the biases in the moisture and temperature fields near the surface and in the lower and middle troposphere are primarily responsible for precipitation biases. These analyses demonstrate the usefulness of these metrics and diagnostics to diagnose climate model biases.

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