scholarly journals Characterisation of the Antarctic stratospheric vortex mixing barrier

2021 ◽  
Author(s):  
◽  
Christopher Cameron
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Effective Diffusivity ◽  
Global Climate Models ◽  
Model Representation ◽  
Climate Modelling ◽  
Air Masses ◽  
The Face ◽  
The Antarctic

<p>The strongest stratospheric circulation in the Southern Hemisphere is the Antarctic Circumpolar Vortex (ACV) which forms each winter and spring as a zone of westerly winds surrounding Antarctica, presenting a barrier to transport of air masses between middle and high-latitudes. This barrier contributes to stratospheric temperatures above the polar region dropping sufficiently low in spring to allow for the processes leading to ozone destruction. Unfortunately, the ACV is generally not well simulated in Global Climate Models (GCMs), and this presents a challenge for model accuracy and projections in the face of a changing climate and a recovering ozone hole.  In this research, an assessment is made of the performance of a range of mixing metrics in representing the ACV based on reanalyses, including: Effective Diffusivity, Contour Crossing, the Lagrangian function $M$, and Meridional Impermeability. It is shown that Meridional Impermeability -- which provides a measure of the strength of the meridional mixing barrier as a function of potential vorticity (PV) gradient and wind-speed -- acts as a useful proxy for more complex metrics. In addition, Meridional Impermeability displays a well-defined vortex profile across equivalent latitude, which is not seen to the same degree in the other metrics assessed.  Representation of the ACV is further compared between climate models and reanalyses based on Meridional Impermeability. It is shown that while climate models have improved in their representation of the vortex barrier over time, there are still significant discrepancies between models and reanalyses. One cause of these discrepancies may result from the use of prescribed ozone fields rather than interactive ozone chemistry. This is further examined by comparing Chemistry Climate Model (CCM) simulations using interactive ozone chemistry, with those using prescribed ozone at either 3-D (i.e., height, latitude and longitude) or 2-D (i.e., height, latitude) dimensionality.   Considerable improvement in the representation of the ACV can be achieved by shifting from 2-D to 3-D prescribed ozone fields, and interactive ozone chemistry further improves its representation. However, discrepancies in model representation of the ACV still remain. Previous researchers have also attributed discrepancies in model representation of the polar vortices to the model resolution, and the parameterization of gravity waves at the sub-grid scale -- these factors are considered to contribute to the discrepancies found in simulations undertaken here also.   The results of this research are expected to provide guidance to improve the representation of vortex processes in climate modelling.</p>

scholarly journals Characterisation of the Antarctic stratospheric vortex mixing barrier

2021 ◽  
Author(s):  
◽  
Christopher Cameron
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Effective Diffusivity ◽  
Global Climate Models ◽  
Model Representation ◽  
Climate Modelling ◽  
Air Masses ◽  
The Face ◽  
The Antarctic

<p>The strongest stratospheric circulation in the Southern Hemisphere is the Antarctic Circumpolar Vortex (ACV) which forms each winter and spring as a zone of westerly winds surrounding Antarctica, presenting a barrier to transport of air masses between middle and high-latitudes. This barrier contributes to stratospheric temperatures above the polar region dropping sufficiently low in spring to allow for the processes leading to ozone destruction. Unfortunately, the ACV is generally not well simulated in Global Climate Models (GCMs), and this presents a challenge for model accuracy and projections in the face of a changing climate and a recovering ozone hole.  In this research, an assessment is made of the performance of a range of mixing metrics in representing the ACV based on reanalyses, including: Effective Diffusivity, Contour Crossing, the Lagrangian function $M$, and Meridional Impermeability. It is shown that Meridional Impermeability -- which provides a measure of the strength of the meridional mixing barrier as a function of potential vorticity (PV) gradient and wind-speed -- acts as a useful proxy for more complex metrics. In addition, Meridional Impermeability displays a well-defined vortex profile across equivalent latitude, which is not seen to the same degree in the other metrics assessed.  Representation of the ACV is further compared between climate models and reanalyses based on Meridional Impermeability. It is shown that while climate models have improved in their representation of the vortex barrier over time, there are still significant discrepancies between models and reanalyses. One cause of these discrepancies may result from the use of prescribed ozone fields rather than interactive ozone chemistry. This is further examined by comparing Chemistry Climate Model (CCM) simulations using interactive ozone chemistry, with those using prescribed ozone at either 3-D (i.e., height, latitude and longitude) or 2-D (i.e., height, latitude) dimensionality.   Considerable improvement in the representation of the ACV can be achieved by shifting from 2-D to 3-D prescribed ozone fields, and interactive ozone chemistry further improves its representation. However, discrepancies in model representation of the ACV still remain. Previous researchers have also attributed discrepancies in model representation of the polar vortices to the model resolution, and the parameterization of gravity waves at the sub-grid scale -- these factors are considered to contribute to the discrepancies found in simulations undertaken here also.   The results of this research are expected to provide guidance to improve the representation of vortex processes in climate modelling.</p>

scholarly journals Comparison of warming trends over the last century around Antarctica from three coupled models

1998 ◽  
Vol 27 ◽  
pp. 565-570 ◽  
Author(s):  
William M. Connolley ◽  
Siobhan P. O'Farrell
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Large Temperature ◽  
Coupled Models ◽  
Temperature Changes ◽  
Model Variability ◽  
The Antarctic

We compare observed temperature variations in Antarctica with climate-model runs over the last century. The models used are three coupled global climate models (GCMs) — the UKMO, the CSIRO and the MPI forced by the CO2 increases observed over the last century, and an atmospheric model experiment forced with observed sea-surface temperatures and sea-ice extents over the last century. Despite some regions of agreement, in general the GCM runs appear to be incompatible with each other and with the observations, although the short observational record and high natural variability make verification difficult. One of the best places for a more detailed study is the Antarctic Peninsula where the density of stations is higher and station records are longer than elsewhere in Antarctica. Observations show that this area has seen larger temperature rises than anywhere else in Antarctica. None of the three GCMs simulate such large temperature changes in the Peninsula region, in either climate-change runs radiatively forced by CO2 increases or control runs which assess the level of model variability.

Using PRIMAVERA high-resolution global climate models for European windstorm risk assessment in present and future climates for the (re)insurance industry

2020 ◽  
Author(s):  
Julia Lockwood ◽  
Erika Palin ◽  
Galina Guentchev ◽  
Malcolm Roberts
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Climate Model ◽  
Insurance Industry ◽  
Global Climate ◽  
Storm Track ◽  
Global Climate Models ◽  
Climate Modelling ◽  
Business And Society ◽  
High Resolution Models

&lt;p&gt;PRIMAVERA is a European Union Horizon2020 project about creating a new generation of advanced and well-evaluated high-resolution global climate models, for the bene&amp;#64257;t of governments, business and society in general. The project has been engaging with several sectors, including &amp;#64257;nance, transport, and energy, to understand the extent to which any improved process understanding arising from high-resolution global climate modelling can &amp;#8211; in turn &amp;#8211; help with using climate model output to address user needs.&lt;/p&gt;&lt;p&gt;In this talk we will outline our work for the &amp;#64257;nance and (re)insurance industries.&amp;#160; Following consultation with members of the industry, we are using PRIMAVERA climate models to generate a European windstorm event set for use in catastrophe modelling and risk analysis.&amp;#160; The event set is generated from five different climate models, each run at a selection of resolutions ranging from 18-140km, covering the period 1950-2050, giving approximately 1700 years of climate model data in total.&amp;#160; High-resolution climate models tend to have reduced biases in storm track position (which is too zonal in low-resolution climate models) and windstorm intensity.&amp;#160; We will compare the properties of the windstorm footprints and associated risk across the different models and resolutions, to assess whether the high-resolution models lead to improved estimation of European windstorm risk.&amp;#160; We will also compare windstorm risk in present and future climates, to see if a consistent picture emerges between models.&amp;#160; Finally we will address the question of whether the event sets from each PRIMAVERA model can be combined to form a multi-model event set ensemble covering thousands of years of windstorm data.&lt;/p&gt;

scholarly journals Assessing climate model software quality: a defect density analysis of three models

2012 ◽  
Vol 5 (1) ◽  
pp. 347-382 ◽  
Author(s):  
J. Pipitone ◽  
S. Easterbrook
Keyword(s):  
Software Quality ◽  
Climate Models ◽  
Climate Model ◽  
Defect Density ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Modelling ◽  
Density Analysis ◽  
Bug Tracking ◽  
Software Trustworthiness

Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.

scholarly journals Assessing climate model software quality: a defect density analysis of three models

2012 ◽  
Vol 5 (4) ◽  
pp. 1009-1022 ◽  
Author(s):  
J. Pipitone ◽  
S. Easterbrook
Keyword(s):  
Software Quality ◽  
Climate Models ◽  
Climate Model ◽  
Defect Density ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Modelling ◽  
Density Analysis ◽  
Bug Tracking ◽  
Software Trustworthiness

Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model, one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.

The MJO-QBO Relationship in a GCM with Stratospheric Nudging

2021 ◽  
pp. 1-69
Author(s):  
Zane Martin ◽  
Clara Orbe ◽  
Shuguang Wang ◽  
Adam Sobel
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Boreal Winter ◽  
Quasi Biennial Oscillation ◽  
Meridional Winds ◽  
Goddard Institute ◽  
Minimal Bias

AbstractObservational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

scholarly journals A prognostic pollen emissions model for climate models (PECM1.0)

2017 ◽  
Author(s):  
Matthew C. Wozniak ◽  
Allison Steiner
Keyword(s):  
United States ◽  
Land Cover ◽  
Climate Models ◽  
Climate Model ◽  
Vegetation Type ◽  
Global Climate ◽  
The United States ◽  
Global Climate Models ◽  
Pollen Counts ◽  
Surface Pollen

Abstract. We develop a prognostic model of Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in a regional climate model (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model: (1) using a taxa-specific land cover database, phenology and emission potential, and (2) a PFT-based land cover, phenology and emission potential. The resulting surface concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model, however we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

scholarly journals Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

2016 ◽  
Vol 155 (3) ◽  
pp. 407-420 ◽  
Author(s):  
R. S. SILVA ◽  
L. KUMAR ◽  
F. SHABANI ◽  
M. C. PICANÇO
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Open Field ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Current Model ◽  
Global Climate Models ◽  
The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

scholarly journals Back to the Future: Using Long-Term Observational and Paleo-Proxy Reconstructions to Improve Model Projections of Antarctic Climate

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 255 ◽  
Author(s):  
Thomas J. Bracegirdle ◽  
Florence Colleoni ◽  
Nerilie J. Abram ◽  
Nancy A. N. Bertler ◽  
Daniel A. Dixon ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Ice Core ◽  
Global Climate Models ◽  
Climate Projections ◽  
Past Performance ◽  
Wide Range ◽  
Proxy Reconstructions

Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.

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.

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