scholarly journals Early-winter North Atlantic low-level jet latitude biases in climate models: implications for simulated regional atmosphere-ocean linkages

2021 ◽  
Author(s):  
Thomas John Bracegirdle ◽  
Hua Lu ◽  
Jon I Robson
Keyword(s):  
North Atlantic ◽  
Climate Models ◽  
Climate Model ◽  
Heat Fluxes ◽  
Climate System ◽  
Sea Surface ◽  
Cmip5 Models ◽  
Turbulent Heat ◽  
Early Winter ◽  
Low Level

Abstract Climate model biases in the North Atlantic (NA) low-level tropospheric westerly jet are a major impediment to reliably representing variability of the NA climate system and its wider influence, in particular over western Europe. A major aspect of the biases is the occurrence of a prominent early-winter equatorward jet bias in Coupled Model Inter-comparison Project Phase 5 (CMIP5) models that has implications for NA atmosphere-ocean coupling. Here we assess whether this bias is reduced in the new CMIP6 models and assess implications for model representation of NA atmosphere-ocean linkages, in particular over the sub-polar gyre (SPG) region. Historical simulations from the CMIP5 and CMIP6 model datasets were compared against reanalysis data over the period 1862-2005. The results show that the early-winter equatorward bias remains present in CMIP6 models, although with an approximately one-fifth reduction compared to CMIP5. The equatorward bias is mainly associated with a weaker-than-observed frequency of poleward excursions of the jet to its northern position. A potential explanation is provided through the identification of a strong link between NA jet latitude bias and systematically too-weak model-simulated low-level baroclinicity over eastern North America in early-winter. CMIP models with larger equatorward jet biases exhibit weaker correlation between temporal variability in speed of the jet and sea surface conditions (sea surface temperatures and turbulent heat fluxes) over the SPG. The results imply that the early-winter equatorward bias in jet latitude in CMIP models could partially explain other known biases, such as the weaker-than-observed seasonal-decadal predictability of the NA climate system.

scholarly journals Air–Sea Turbulent Heat Fluxes in Climate Models and Observational Analyses: What Drives Their Variability?

2019 ◽  
Vol 32 (8) ◽  
pp. 2397-2421 ◽  
Author(s):  
R. Justin Small ◽  
Frank O. Bryan ◽  
Stuart P. Bishop ◽  
Robert A. Tomas
Keyword(s):  
Heat Flux ◽  
Climate Models ◽  
Climate Model ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Small Scale ◽  
Turbulent Heat ◽  
Realistic Representation ◽  
Frontal Zones ◽  
The Tropics

Abstract A traditional view is that the ocean outside of the tropics responds passively to atmosphere forcing, which implies that air–sea heat fluxes are mainly driven by atmosphere variability. This paper tests this viewpoint using state-of-the-art air–sea turbulent heat flux observational analyses and a climate model run at different resolutions. It is found that in midlatitude ocean frontal zones the variability of air–sea heat fluxes is not predominantly driven by the atmosphere variations but instead is forced by sea surface temperature (SST) variations arising from intrinsic oceanic variability. Meanwhile in most of the tropics and subtropics wind is the dominant driver of heat flux variability, and atmosphere humidity is mainly important in higher latitudes. The predominance of ocean forcing of heat fluxes found in frontal regions occurs on scales of around 700 km or less. Spatially smoothing the data to larger scales results in the traditional atmosphere-driving case, while filtering to retain only small scales of 5° or less leads to ocean forcing of heat fluxes over most of the globe. All observational analyses examined (1° OAFlux; 0.25° J-OFURO3; 0.25° SeaFlux) show this general behavior. A standard resolution (1°) climate model fails to reproduce the midlatitude, small-scale ocean forcing of heat flux: refining the ocean grid to resolve eddies (0.1°) gives a more realistic representation of ocean forcing but the variability of both SST and of heat flux is too high compared to observational analyses.

Could the cloud cover in future climate models be improved by incorporating the role of sea spray in surface heat fluxes?

2020 ◽  
Author(s):  
Yajuan Song ◽  
Fangli Qiao ◽  
Qi Shu ◽  
Jiping Liu ◽  
Ying Bao ◽  
...  
Keyword(s):  
Latent Heat ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Heat Fluxes ◽  
Global Climate Models ◽  
Shortwave Radiation ◽  
Sea Spray ◽  
Low Level

<p>Accurate cloud cover and radiative effect simulation remains a long-standing challenge for global climate models (GCMs). The Southern Ocean (SO) cloud cover is substantially underestimated by most GCMs. Therefore, too much shortwave radiation is absorbed by oceans, which causes an overly warm sea surface temperature (SST) bias over the SO. For the first time, sea spray effects on latent and sensible heat fluxes are considered in a climate model. The most notable sea spray impacts on heat fluxes occur over the SO, with anomalous latent heat fluxes up to -7.74 W m<sup>-2</sup>. Enhanced latent heat release lead to SST cooling. In addition, more clouds are formed over the SO to reflect excessive downward shortwave radiation, especially low-level clouds at 1.51% increments. Our results provide a feasible solution to mitigate the lack of low-level clouds and overly warm SST biases over the SO in GCMs.</p>

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>

scholarly journals Spatio-temporal consistent bias pattern detection on MIROC5 andFGOALS-g2

2019 ◽  
Author(s):  
Bo Cao ◽  
Ying Zhao ◽  
Ziheng Zhou
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Model Performance ◽  
Climate System ◽  
Cmip5 Models ◽  
Seasonal Characteristics ◽  
Double Itcz ◽  
Model Improvement ◽  
Spatio Temporal ◽  
Impacts Of Climate Change

Abstract. Building climate models is a typical means of studying the dynamics of the climate system and assessing the impacts of climate change. However, model-related biases are common in existing climate models, such as the double ITCZ bias in most CMIP5 models. Recent studies suggest that biases showing distinct spatio-temporal characteristics may involve different mechanisms and sources in climate models. More dedicated studies on bias patterns is important not only for improving model performance, but also for helping modelers to better understand the climate system. In this paper, we focus on detecting spatio-temporal consistent bias patterns from climate model outputs. A spatio-temporal pattern is a bias pattern that is present in contiguous space with significant and coherent biases in continuous time periods. These patterns are ideal for revealing regional and seasonal characteristics of biases and worth further investigation by modelers. Due to the high computation cost, most of the existing analysis methods can only detect bias patterns that are either spatial consistent or temporal consistent, but not both at the same time. We proposed a bottom-up algorithm to tackle this problem. The proposed method first detects regions showing significant and consistent biases at each time slot and then merges them iteratively to form bias instances. The resulting bias instances are further clustered into different families to depict corresponding spatio-temporal consistent bias patterns. The experiments on both MIROC5 and FGOALS-g2 precipitation outputs show that the proposed approach can detect some important bias patterns that are consistent with previous studies and can produce other interesting findings. Modelers can adopt the proposed method as an exploratory tool to develop insights for bias correction and model improvement.

scholarly journals Simulations of the Mid-Pliocene Warm Period using the NASA/GISS ModelE2-R Earth System Model

2012 ◽  
Vol 5 (3) ◽  
pp. 2811-2842 ◽  
Author(s):  
M. A. Chandler ◽  
L. E. Sohl ◽  
J. A. Jonas ◽  
H. J. Dowsett
Keyword(s):  
North Atlantic ◽  
Climate Models ◽  
Climate Model ◽  
Geographic Region ◽  
Earth System Model ◽  
Warm Period ◽  
System Model ◽  
Earth System ◽  
Intergovernmental Panel ◽  
Future Global Warming

Abstract. Climate reconstructions of the mid-Pliocene Warm Period (mPWP) bear many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change. In particular, marine and terrestrial paleoclimate data point to high latitude temperature amplification, with associated decreases in sea ice and land ice and altered vegetation distributions that show expansion of warmer climate biomes into higher latitudes. NASA GISS climate models have been used to study the Pliocene climate since the USGS PRISM project first identified that the mid-Pliocene North Atlantic sea surface temperatures were anomalously warm. Here we present the most recent simulations of the Pliocene using the AR5/CMIP5 version of the GISS Earth System Model known as ModelE2-R. These simulations constitute the NASA contribution to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasize features in the ModelE2-R simulations that are unlike the ensemble means. We provide discussion of features that show considerable improvement compared with simulations from previous versions of the NASA GISS models, improvement defined here as simulation results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene climate. In some regions even qualitative agreement between model results and paleodata are an improvement over past studies, but the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea in these new simulations is by far the most accurate portrayal ever of this key geographic region by the GISS climate model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterizations in the ocean model, have led to an Earth System Model that will produce more accurate projections of future climate.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

scholarly journals A statistical examination of the effects of stratospheric sulphate geoengineering on tropical storm genesis

2018 ◽  
Author(s):  
Qin Wang ◽  
John C. Moore ◽  
Duoying Ji
Keyword(s):  
Relative Humidity ◽  
Wind Shear ◽  
Climate Models ◽  
Climate Model ◽  
Vertical Wind Shear ◽  
Stratospheric Aerosol ◽  
Vertical Wind ◽  
Radiative Heating ◽  
Cmip5 Models ◽  
The North

Abstract. The thermodynamics of the ocean and atmosphere partly determine variability in tropical cyclone (TC) number and intensity and are readily accessible from climate model output, but a complete description of TC variability requires much more dynamical data than climate models can provide at present. Genesis potential index (GPI) and ventilation index (VI) are combinations of potential intensity, vertical wind shear, relative humidity, midlevel entropy deficit, and absolute vorticity that can quantify both thermodynamic and dynamic forcing of TC activity under different climate states. Here we use six CMIP5 models that have run the RCP4.5 experiment and the Geoengineering Model Intercomparison Project (GeoMIP) stratospheric aerosol injection G4 experiment, to calculate the two TC indices over the 2020 to 2069 period across the 6 ocean basins that generate tropical cyclones. Globally, GPI under G4 is lower than under RCP4.5, though both have a slight increasing trend. Spatial patterns in the effectiveness of geoengineering show reductions in TC in the North Atlantic basin, and Northern Indian Ocean in all models except NorESM1-M. In the North Pacific, most models also show relative reductions under G4. Most models project potential intensity and relative humidity to be the dominant variables affecting genesis potential. Changes in vertical wind shear are significant, but both it and vorticity exhibit relatively small changes with large variation across both models and ocean basins. We find that tropopause temperature is not a useful addition to sea surface temperature in projecting TC genesis, despite radiative heating of the stratosphere due to the aerosol injection, and heating of the upper troposphere affecting static stability and potential intensity. Thus, simplified statistical methods that quantify the thermodynamic state of the major genesis basins may reasonably be used to examine stratospheric aerosol geoengineering impacts on TC activity.

scholarly journals Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

2018 ◽  
Vol 32 (1) ◽  
pp. 195-212 ◽  
Author(s):  
Sicheng He ◽  
Jing Yang ◽  
Qing Bao ◽  
Lei Wang ◽  
Bin Wang
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Global Ocean ◽  
East China ◽  
Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

The Impact of Sea Surface Temperature Biases on North American Precipitation in a High-Resolution Climate Model

2020 ◽  
Vol 33 (6) ◽  
pp. 2427-2447 ◽  
Author(s):  
Nathaniel C. Johnson ◽  
Lakshmi Krishnamurthy ◽  
Andrew T. Wittenberg ◽  
Baoqiang Xiang ◽  
Gabriel A. Vecchi ◽  
...  
Keyword(s):  
North America ◽  
Surface Temperature ◽  
North Atlantic ◽  
North American ◽  
North Pacific ◽  
Climate Model ◽  
Sea Surface ◽  
Tropical Atlantic ◽  
Western North America ◽  
The North

AbstractPositive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic subtropical high and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts.

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