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.

scholarly journals Downscaling the climate change for oceans around Australia

2012 ◽  
Vol 5 (1) ◽  
pp. 425-458 ◽  
Author(s):  
M. A. Chamberlain ◽  
C. Sun ◽  
R. J. Matear ◽  
M. Feng ◽  
S. J. Phipps
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Ocean Model ◽  
Global Climate Models ◽  
Global Ocean ◽  
Coupled Models

Abstract. At present, global climate models used to project changes in climate do not resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically downscaling coarse climate change projections utilising a global ocean model that resolves these features in the Australian region. The downscaling model used here is ocean-only. The ocean feedback on the air-sea fluxes is explored by restoring to surface temperature and salinity, as well as a calculated feedback to wind stress. These feedback approximations do not replace the need for fully coupled models, but they allow us to assess the sensitivity of the ocean in downscaled climate change simulations. Significant differences are found in sea surface temperature, salinity, stratification and transport between the downscaled projections and those of the climate model. While the magnitude of the climate change differences may vary with the feedback parameterisation used, the patterns of the climate change differences are consistent and develop rapidly indicating they are mostly independent of feedback that ocean differences may have on the air-sea fluxes. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change downscaling provides an attractive way to explore how climate change may affect the mesoscale ocean environment.

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 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.

Investigating the seasonal response of precipitation extremes to global warming using observations and large-ensembles of coupled climate models

2020 ◽  
Author(s):  
Andrew Williams ◽  
Paul O'Gorman
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Precipitation Extremes ◽  
Cmip5 Models ◽  
Historical Period ◽  
Societal Relevance ◽  
Coupled Climate Models

<p>Changes in extreme precipitation are amongst the most impactful consequences of global warming, with potential effects ranging from increased flood risk and landslides to crop failures and impacts on ecosystems. Thus, understanding historical and future changes in extreme precipitation is not only important from a scientific perspective, but also has direct societal relevance.</p><p>However, while most current research has focused on annual precipitation extremes and their response to warming, it has recently been noted that climate model projections show a distinct seasonality to future changes in extreme precipitation. In particular, CMIP5 models suggest that over Northern Hemisphere (NH) land the summer response is weaker than the winter response in terms of percentage changes.</p><p>Here we investigate changes in seasonal precipitation extremes using observations and simulations with coupled climate models. First, we analyse observed trends from the Hadley Centre’s global climate extremes dataset (HadEX2) to investigate to what extent there is already a difference between summer and winter trends over NH land. Second, we use 40 ensemble members from the CESM Large Ensemble to characterize the role played by internal variability in trends over the historical period. Lastly, we use CMIP5 simulations to explore the possibility of a link between the seasonality of changes in precipitation extremes and decreases in surface relative humidity over land.</p>

Regionally Varying Assessments of Upper-Level Tropical Width in Reanalyses and CMIP5 Models Using a Tropopause Break Metric

2020 ◽  
Vol 33 (14) ◽  
pp. 5885-5903 ◽  
Author(s):  
Elinor R. Martin ◽  
Cameron R. Homeyer ◽  
Roarke A. McKinzie ◽  
Kevin M. McCarthy ◽  
Tao Xian
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
The Past ◽  
East Pacific ◽  
Change Climate ◽  
Upper Level ◽  
The Tropics

AbstractChanges in tropical width can have important consequences in sectors including ecosystems, agriculture, and health. Observations suggest tropical expansion over the past 30 years although studies have not agreed on the magnitude of this change. Climate model projections have also indicated an expansion and show similar uncertainty in its magnitude. This study utilizes an objective, longitudinally varying, tropopause break method to define the extent of the tropics at upper levels. The location of the tropopause break is associated with enhanced stratosphere–troposphere exchange and thus its structure influences the chemical composition of the stratosphere. The method shows regional variations in the width of the upper-level tropics in the past and future. Four modern reanalyses show significant contraction of the tropics over the eastern Pacific between 1981 and 2015, and slight but significant expansion in other regions. The east Pacific narrowing contributes to zonal mean narrowing, contradicting prior work, and is attributed to the use of monthly and zonal mean data in prior studies. Six global climate models perform well in representing the climatological location of the tropical boundary. Future projections show a spread in the width trend (from ~0.5° decade−1 of narrowing to ~0.4° decade−1 of widening), with a narrowing projected across the east Pacific and Northern Hemisphere Americas. This study illustrates that this objective tropopause break method that uses instantaneous data and does not require zonal averaging is appropriate for identifying upper-level tropical width trends and the break location is connected with local and regional changes in precipitation.

scholarly journals Saharan Heat Low Biases in CMIP5 Models

2017 ◽  
Vol 30 (8) ◽  
pp. 2867-2884 ◽  
Author(s):  
Ross D. Dixon ◽  
Anne Sophie Daloz ◽  
Daniel J. Vimont ◽  
Michela Biasutti
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
West African Monsoon ◽  
West African ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
African Climate ◽  
Heat Low

Representing the West African monsoon (WAM) is a major challenge in climate modeling because of the complex interaction between local and large-scale mechanisms. This study focuses on the representation of a key aspect of West African climate, namely the Saharan heat low (SHL), in 22 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel dataset. Comparison of the CMIP5 simulations with reanalyses shows large biases in the strength and location of the mean SHL. CMIP5 models tend to develop weaker climatological heat lows than the reanalyses and place them too far southwest. Models that place the climatological heat low farther to the north produce more mean precipitation across the Sahel, while models that place the heat low farther to the east produce stronger African easterly wave (AEW) activity. These mean-state biases are seen in model ensembles with both coupled and fixed sea surface temperatures (SSTs). The importance of SSTs on West African climate variability is well documented, but this research suggests SSTs are secondary to atmospheric biases for understanding the climatological SHL bias. SHL biases are correlated across the models to local radiative terms, large-scale tropical precipitation, and large-scale pressure and wind across the Atlantic, suggesting that local mechanisms that control the SHL may be connected to climate model biases at a much larger scale.

Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model

2008 ◽  
Vol 366 (1875) ◽  
pp. 2559-2577 ◽  
Author(s):  
J Berner ◽  
F.J Doblas-Reyes ◽  
T.N Palmer ◽  
G Shutts ◽  
A Weisheimer
Keyword(s):  
Cellular Automaton ◽  
Systematic Error ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Seasonal Prediction ◽  
Global Climate Models ◽  
Seasonal Forecasts ◽  
The Impact

The impact of a nonlinear dynamic cellular automaton (CA) model, as a representation of the partially stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium Range Weather Forecasts coupled ocean–atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the CA at the near-grid scale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the CA's representation of inherent model uncertainty.

Model Uncertainty in Cloud–Circulation Coupling, and Cloud-Radiative Response to Increasing CO2, Linked to Biases in Climatological Circulation

2018 ◽  
Vol 31 (24) ◽  
pp. 10013-10020
Author(s):  
Bernard R. Lipat ◽  
Aiko Voigt ◽  
George Tselioudis ◽  
Lorenzo M. Polvani
Keyword(s):  
Model Uncertainty ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Hadley Cell ◽  
Temperature Structure ◽  
Climatological Circulation

Recent analyses of global climate models suggest that uncertainty in the coupling between midlatitude clouds and the atmospheric circulation contributes to uncertainty in climate sensitivity. However, the reasons behind model differences in the cloud–circulation coupling have remained unclear. Here, we use a global climate model in an idealized aquaplanet setup to show that the Southern Hemisphere climatological circulation, which in many models is biased equatorward, contributes to the model differences in the cloud–circulation coupling. For the same poleward shift of the Hadley cell (HC) edge, models with narrower climatological HCs exhibit stronger midlatitude cloud-induced shortwave warming than models with wider climatological HCs. This cloud-induced radiative warming results predominantly from a subsidence warming that decreases cloud fraction and is stronger for narrower HCs because of a larger meridional gradient in the vertical velocity. A comparison of our aquaplanet results with comprehensive climate models suggests that about half of the model uncertainty in the midlatitude cloud–circulation coupling stems from this impact of the circulation on the large-scale temperature structure of the atmosphere, and thus could be removed by improving the climatological circulation in models. This illustrates how understanding of large-scale dynamics can help reduce uncertainty in clouds and their response to climate change.

scholarly journals Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques

2017 ◽  
Vol 56 (12) ◽  
pp. 3245-3262 ◽  
Author(s):  
A. Wootten ◽  
A. Terando ◽  
B. J. Reich ◽  
R. P. Boyles ◽  
F. Semazzi
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Model Experiments ◽  
Complete Set ◽  
Regional Adaptation ◽  
Model Ensembles

AbstractIn recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

scholarly journals The representation of Northern Hemisphere blocking in current global climate models

2020 ◽  
Author(s):  
Reinhard Schiemann ◽  
Panos Athanasiadis ◽  
David Barriopedro ◽  
Francisco Doblas-Reyes ◽  
Katja Lohmann ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Model Development ◽  
Reanalysis Data ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
The Pacific ◽  
Intercomparison Project ◽  
Blocking Index

Abstract. Global Climate Models (GCMs) are known to suffer from biases in the simulation of atmospheric blocking, and this study provides an assessment of how blocking is represented by the latest generation of GCMs. It is evaluated (i) how historical CMIP6 (Climate Model Intercomparison Project Phase 6) simulations perform compared to CMIP5 simulations, and (ii) how horizontal model resolution affects the simulation of blocking in the CMIP6-HighResMIP (PRIMAVERA) model ensemble, which is designed to address this type of question. Two blocking indices are used to evaluate the simulated mean blocking frequency and blocking persistence for the Euro-Atlantic and Pacific regions in winter and summer against the corresponding estimates from atmospheric reanalysis data. There is robust evidence that CMIP6 models simulate blocking frequency and persistence better than CMIP5 models in the Atlantic and Pacific and in winter and summer. This improvement is sizeable so that, for example, winter blocking frequency in the median CMIP5 model in a large Euro-Atlantic domain is underestimated by 32 % using the absolute geopotential height (AGP) blocking index, whereas the same number is 19 % for the median CMIP6 model. As for the sensitivity of simulated blocking to resolution, it is found that the resolution increase, from typically 100 km to 20 km grid spacing, in the PRIMAVERA models, which are not re-tuned at the higher resolutions, benefits the mean blocking frequency in the Atlantic in winter and summer, and in the Pacific in summer. Simulated blocking persistence, however, is not seen to improve with resolution. Our results are consistent with previous studies suggesting that resolution is one of a number of interacting factors necessary for an adequate simulation of blocking in GCMs. The improvements reported in this study hold promise for further reductions in blocking biases as model development continues.

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