scholarly journals Surface salinity under transitioning ice cover in the Canada Basin: Climate model biases linked to vertical 2 distribution of freshwater

2021 ◽  
Author(s):  
Erica Rosenblum ◽  
Robert Fajber ◽  
Julienne Stroeve ◽  
Sarah Gille ◽  
Bruno Tremblay ◽  
...  
Keyword(s):  
Climate Model ◽  
Ice Cover ◽  
Canada Basin ◽  
Surface Salinity ◽  
Model Biases

Subpolar Gyre – AMOC – Atmosphere Interactions on Multidecadal Timescales in a Version of the Kiel Climate Model

2021 ◽  
Author(s):  
Jing Sun ◽  
Mojib Latif ◽  
Wonsun Park
Keyword(s):  
North Atlantic ◽  
Climate Model ◽  
Deep Convection ◽  
Meridional Overturning Circulation ◽  
Subpolar Gyre ◽  
Surface Salinity ◽  
The North ◽  
Ocean Coupling ◽  
Meridional Overturning ◽  
The North Atlantic

<p>There is a controversy about the nature of multidecadal climate variability in the North Atlantic (NA) region, concerning the roles of ocean circulation and atmosphere-ocean coupling. Here we describe NA multidecadal variability from a version of the Kiel Climate Model, in which both subpolar gyre (SPG)-Atlantic Meridional Overturning Circulation (AMOC) and atmosphere-ocean coupling are essential. The oceanic barotropic streamfuntions, meridional overturning streamfunctions, and sea level pressure are jointly analyzed to derive the leading mode of Atlantic variability. This mode accounting for about 23.7 % of the total combined variance is oscillatory with an irregular periodicity of 25-50 years and an e-folding time of about a decade. SPG and AMOC mutually influence each other and together provide the delayed negative feedback necessary for maintaining the oscillation. An anomalously strong SPG, for example, drives higher surface salinity and density in the NA’s sinking region. In response, oceanic deep convection and AMOC intensify, which, with a time delay of about a decade, reduces SPG strength by enhancing upper-ocean heat content. The weaker gyre circulation leads to lower surface salinity and density in the sinking region, which eventually reduces deep convection and AMOC strength. There is a positive ocean-atmosphere feedback between the sea surface temperature and low-level atmospheric circulation over the Southern Greenland area, with related wind stress changes reinforcing SPG changes, thereby maintaining the (damped) multidecadal oscillation against dissipation. Stochastic surface heat-flux forcing associated with the North Atlantic Oscillation drives the eigenmode.</p>

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>

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.

scholarly journals Sea-ice and North Atlantic climate response to CO2-induced warming and cooling conditions

2006 ◽  
Vol 52 (178) ◽  
pp. 433-439 ◽  
Author(s):  
Larissa Nazarenko ◽  
Nickolai Tausnev ◽  
James Hansen
Keyword(s):  
Sea Ice ◽  
Northern Hemisphere ◽  
Surface Pressure ◽  
Climate Model ◽  
Global Climate Model ◽  
Ice Cover ◽  
The Arctic ◽  
Normal Surface ◽  
Sea Ice Extent ◽  
Polar Cell

AbstractUsing a global climate model coupled with an ocean and a sea-ice model, we compare the effects of doubling CO2 and halving CO2 on sea-ice cover and connections with the atmosphere and ocean. An overall warming in the 2 × CO2 experiment causes reduction of sea-ice extent by 15%, with maximum decrease in summer and autumn, consistent with observed seasonal sea-ice changes. The intensification of the Northern Hemisphere circulation is reflected in the positive phase of the Arctic Oscillation (AO), associated with higher-than-normal surface pressure south of about 50° N and lower-than-normal surface pressure over the high northern latitudes. Strengthening the polar cell causes enhancement of westerlies around the Arctic perimeter during winter. Cooling, in the 0.5 × CO2 experiment, leads to thicker and more extensive sea ice. In the Southern Hemisphere, the increase in ice-covered area (28%) dominates the ice-thickness increase (5%) due to open ocean to the north. In the Northern Hemisphere, sea-ice cover increases by only 8% due to the enclosed land/sea configuration, but sea ice becomes much thicker (108%). Substantial weakening of the polar cell due to increase in sea-level pressure over polar latitudes leads to a negative trend of the winter AO index. The model reproduces large year-to-year variability under both cooling and warming conditions.

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