scholarly journals Simulations of the 1979–88 polar climates by global climate models

1995 ◽  
Vol 21 ◽  
pp. 83-90 ◽  
Author(s):  
Biao Chen ◽  
David H. Bromwich ◽  
Keith M. Hines ◽  
Xuguang Pan
Keyword(s):  
Climate Models ◽  
Comprehensive Evaluation ◽  
Global Climate ◽  
Precipitable Water ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Atmospheric Models ◽  
Sea Level Pressure ◽  
Polar Climate

The simulation of the northern and southern polar climates for 1979–88 by 14 global climate models (GCMs), using the observed monthly averaged sea-surface temperatures and sea-ice extents as boundary conditions, is part of an international effort to determine the systematic errors of atmospheric models under realistic conditions, the so-called Atmospheric Model Intercomparison Project (AMIP), In this study, intercomparison of the models’ simulation of polar climate is discussed in terms of selected surface and vertically integrated monthly averaged quantities, such as sea-level pressure, cloudiness, precipitable water, precipitation and evaporation/sublimation. The results suggest that the accuracy of model-simulated climate features in high latitudes primarily depends on the horizontal resolution and the treatment of physical processes in the GCMs. AMIP offers an unprecedented opportunity for the comprehensive evaluation and validation of current atmospheric models and provides valuable information for model improvement.

scholarly journals Simulations of the 1979–88 polar climates by global climate models

1995 ◽  
Vol 21 ◽  
pp. 83-90 ◽  
Author(s):  
Biao Chen ◽  
David H. Bromwich ◽  
Keith M. Hines ◽  
Xuguang Pan
Keyword(s):  
Climate Models ◽  
Comprehensive Evaluation ◽  
Global Climate ◽  
Precipitable Water ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Atmospheric Models ◽  
Sea Level Pressure ◽  
Polar Climate

The simulation of the northern and southern polar climates for 1979–88 by 14 global climate models (GCMs), using the observed monthly averaged sea-surface temperatures and sea-ice extents as boundary conditions, is part of an international effort to determine the systematic errors of atmospheric models under realistic conditions, the so-called Atmospheric Model Intercomparison Project (AMIP), In this study, intercomparison of the models’ simulation of polar climate is discussed in terms of selected surface and vertically integrated monthly averaged quantities, such as sea-level pressure, cloudiness, precipitable water, precipitation and evaporation/sublimation. The results suggest that the accuracy of model-simulated climate features in high latitudes primarily depends on the horizontal resolution and the treatment of physical processes in the GCMs. AMIP offers an unprecedented opportunity for the comprehensive evaluation and validation of current atmospheric models and provides valuable information for model improvement.

scholarly journals A new world natural vegetation map for global change studies

2008 ◽  
Vol 80 (2) ◽  
pp. 397-408 ◽  
Author(s):  
David M. Lapola ◽  
Marcos D. Oyama ◽  
Carlos A. Nobre ◽  
Gilvan Sampaio
Keyword(s):  
Large Scale ◽  
New World ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Vegetation Classification ◽  
Natural Vegetation ◽  
Global Climate Models ◽  
Vegetation Map ◽  
Vegetation Maps

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

scholarly journals Future Australian Severe Thunderstorm Environments. Part II: The Influence of a Strongly Warming Climate on Convective Environments

2014 ◽  
Vol 27 (10) ◽  
pp. 3848-3868 ◽  
Author(s):  
John T. Allen ◽  
David J. Karoly ◽  
Kevin J. Walsh
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Vertical Wind Shear ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Historical Period ◽  
Severe Thunderstorm ◽  
Warming Climate ◽  
Eastern Australia

Abstract The influence of a warming climate on the occurrence of severe thunderstorm environments in Australia was explored using two global climate models: Commonwealth Scientific and Industrial Research Organisation Mark, version 3.6 (CSIRO Mk3.6), and the Cubic-Conformal Atmospheric Model (CCAM). These models have previously been evaluated and found to be capable of reproducing a useful climatology for the twentieth-century period (1980–2000). Analyzing the changes between the historical period and high warming climate scenarios for the period 2079–99 has allowed estimation of the potential convective future for the continent. Based on these simulations, significant increases to the frequency of severe thunderstorm environments will likely occur for northern and eastern Australia in a warmed climate. This change is a response to increasing convective available potential energy from higher continental moisture, particularly in proximity to warm sea surface temperatures. Despite decreases to the frequency of environments with high vertical wind shear, it appears unlikely that this will offset increases to thermodynamic energy. The change is most pronounced during the peak of the convective season, increasing its length and the frequency of severe thunderstorm environments therein, particularly over the eastern parts of the continent. The implications of this potential increase are significant, with the overall frequency of potential severe thunderstorm days per year likely to rise over the major population centers of the east coast by 14% for Brisbane, 22% for Melbourne, and 30% for Sydney. The limitations of this approach are then discussed in the context of ways to increase the confidence of predictions of future severe convection.

IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

2021 ◽  
Author(s):  
SOURABH SHRIVASTAVA ◽  
RAM AVTAR ◽  
PRASANTA KUMAR BAL
Keyword(s):  
Machine Learning ◽  
North American ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Summer Monsoon Rainfall ◽  
Global Climate Models ◽  
Himalayan Region ◽  
Model Ensemble ◽  
The North

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

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.

Projected precipitation changes over China for global warming levels at 1.5°C and 2°C in an ensemble of regional climate simulations: Impact of bias-correction algorithms

2020 ◽  
Author(s):  
Lianyi Guo
Keyword(s):  
Global Warming ◽  
Bias Correction ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Comprehensive Evaluation ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Cumulative Distribution ◽  
Mathematical Framework

<p>Four bias-correction methods, i.e. Gamma Cumulative Distribution Function (GamCDF), Quantile-Quantile Adjustment (QQadj), Equidistant CDF Matching (EDCDF) and Transform CDF (CDF-t), were applied to five daily precipitation datasets over China produced by LMDZ4-regional that was nested into five global climate models (GCMs), BCC-CSM1-1m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR and MPI-ESM-MR, respectively. A unified mathematical framework can be used to define the four methods, which helps understanding their nature and essence in identifying the most reliable probability distributions of projected climate. CDF-t is shown to be the best bias-correction algorithm based on a comprehensive evaluation of different rainfall indices. Future precipitation projections corresponds to the global warming levels of 1.5°C and 2°C under RCP8.5 were obtained using the bias correction methods. The multi-algorithm and multi-model ensemble characteristics allow to explore the spreading of results, considered as a surrogate of climate projection uncertainty, and to attribute such uncertainties to different sources. It was found that the spread among bias-correction methods is smaller than that among dynamical downscaling simulations. The four bias-correction methods with CDF-t at the top all reduce the spread among the downscaled results. Future projection using CDF-t is thus considered having higher credibility.</p>

scholarly journals Sensitivity of Limiting Hurricane Intensity to SST in the Atlantic from Observations and GCMs

2013 ◽  
Vol 26 (16) ◽  
pp. 5949-5957 ◽  
Author(s):  
James B. Elsner ◽  
Sarah E. Strazzo ◽  
Thomas H. Jagger ◽  
Timothy LaRow ◽  
Ming Zhao
Keyword(s):  
Climate Models ◽  
North Atlantic Ocean ◽  
Global Climate ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Lower Sensitivity ◽  
Hurricane Intensity ◽  
University Center ◽  
The North ◽  
Low Sensitivity

Abstract A statistical model for the intensity of the strongest hurricanes has been developed and a new methodology introduced for estimating the sensitivity of the strongest hurricanes to changes in sea surface temperature. Here, the authors use this methodology on observed hurricanes and hurricanes generated from two global climate models (GCMs). Hurricanes over the North Atlantic Ocean during the period 1981–2010 show a sensitivity of 7.9 ± 1.19 m s−1 K−1 (standard error; SE) when over seas warmer than 25°C. In contrast, hurricanes over the same region and period generated from the GFDL High Resolution Atmospheric Model (HiRAM) show a significantly lower sensitivity with the highest at 1.8 ± 0.42 m s−1 K−1 (SE). Similar weaker sensitivity is found using hurricanes generated from the Florida State University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) model with the highest at 2.9 ± 2.64 m s−1 K−1 (SE). A statistical refinement of HiRAM-generated hurricane intensities heightens the sensitivity to a maximum of 6.9 ± 3.33 m s−1 K−1 (SE), but the increase is offset by additional uncertainty associated with the refinement. Results suggest that the caution that should be exercised when interpreting GCM scenarios of future hurricane intensity stems from the low sensitivity of limiting GCM-generated hurricane intensity to ocean temperature.

Some Counterintuitive Dependencies of Tropical Cyclone Frequency on Parameters in a GCM

2012 ◽  
Vol 69 (7) ◽  
pp. 2272-2283 ◽  
Author(s):  
Ming Zhao ◽  
Isaac M. Held ◽  
Shian-Jiann Lin
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Global Climate ◽  
Atmospheric Model ◽  
Global Climate Models ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Mixing Rate ◽  
Continuous Increase ◽  
Mean Intensity ◽  
Two Parameters

Abstract High-resolution global climate models (GCMs) have been increasingly utilized for simulations of the global number and distribution of tropical cyclones (TCs), and how they might change with changing climate. In contrast, there is a lack of published studies on the sensitivity of TC genesis to parameterized processes in these GCMs. The uncertainties in these formulations might be an important source of uncertainty in the future projections of TC statistics. This study investigates the sensitivity of the global number of TCs in present-day simulations using the Geophysical Fluid Dynamics Laboratory High Resolution Atmospheric Model (GFDL HIRAM) to alterations in physical parameterizations. Two parameters are identified to be important in TC genesis frequency in this model: the horizontal cumulus mixing rate, which controls the entrainment into convective cores within the convection parameterization, and the strength of the damping of the divergent component of the horizontal flow. The simulated global number of TCs exhibits nonintuitive response to incremental changes of both parameters. As the cumulus mixing rate increases, the model produces nonmonotonic response in global TC frequency with an initial sharp increase and then a decrease. However, storm mean intensity rises monotonically with the mixing rate. As the strength of the divergence damping increases, the model produces a continuous increase of global number of TCs and hurricanes with little change in storm mean intensity. Mechanisms for explaining these nonintuitive responses are discussed.

scholarly journals Glacial Inception on Baffin Island: The Interaction of Ice Flow and Meteorology

2018 ◽  
Author(s):  
Leah Birch ◽  
Timothy Cronin ◽  
Eli Tziperman
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Forecast Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Baffin Island ◽  
Ice Flow ◽  
Geological Evidence ◽  
Ice Caps ◽  
Outer Domain

Abstract. Over the past 0.8 million years, 100 kyr ice ages have dominated Earth's climate with geological evidence suggesting the last glacial inception began in the mountains of Baffin Island. Currently, state-of-the-art global climate models (GCMs) have difficulty simulating glacial inception, possibly due in part to their coarse horizontal resolution and the neglect of ice flow dynamics in some models. We attempt to address the initial inception problem on Baffin Island by asynchronously coupling the Weather Research and Forecast model (WRF), configured as a high resolution inner domain over Baffin and an outer domain incorporating much of North America, to an ice flow model using the shallow ice approximation. The mass balance is calculated from WRF simulations, and used to drive the ice model, which updates the ice extent and elevation, that then serve as inputs to the next WRF run. We drive the regional WRF configuration using atmospheric boundary conditions from 1986 that correspond to a relatively cold summer, and with 115 kya insolation. Initially, ice accumulates on mountain glaciers, driving downslope ice flow which expands the size of the ice caps. However, continued iterations of the atmosphere and ice models reveal a stagnation of the ice sheet on Baffin Island, driven by melting due to warmer temperatures at the margins of the ice caps. This warming is caused by changes in the larger-scale circulation that are forced by elevation changes due to the ice growth. A stabilizing feedback between ice elevation and atmospheric circulation thus prevents full inception from occurring.

Assessment of AOD by 16 CMIP6 Models Based on Satellite-Derived Dataset from 2000-2014 over Eastern Center China

2021 ◽  
Author(s):  
xiao li ◽  
minghuai wang ◽  
yawen liu ◽  
yiquan jiang ◽  
xinyi dong
Keyword(s):  
Climate Models ◽  
Comprehensive Evaluation ◽  
Global Climate ◽  
Chemical Properties ◽  
Sulfate Aerosol ◽  
Global Climate Models ◽  
Aerosol Concentration ◽  
Central China ◽  
Liquid Water Path ◽  
Simulated Precipitation

<h3>Knowledge of aerosol concentration, type, and physical and chemical properties is necessary to understand their role in Earth’s climate system. However, CMIP6 models’ performance of AOD simulation in China lacks a comprehensive evaluation and the potential improvement for CMIP6 models is still unclear. Here, we assess the performance of CMIP6 models in simulating annual mean AOD climatology and its seasonality over China from 2000 to 2014 and explore the underlying reasons for its performance. Compared with CMIP5, CMIP6 models can better capture the annual mean AOD climatology magnitude over Eastern Central China (ECC) with a notable enhancement of 52.98% due to a significant increase in the dominate sulfate aerosol. However, the majority of CMIP6 models fail to capture the observed inverted “V-like” pattern that depicts two centers of maximum AOD in spring over northeast China (NEC) and in summer over southeast China (SEC), respectively. The deficiency of two maximums by CMIP6 models is separately due to the negative bias in the simulation of organic aerosol (OA) AOD and sulfate AOD. Our analysis suggests that the deviation of simulated precipitation, relative humidity (RH), and liquid water path (LWP) in CMIP6 models contributes to the deviation of simulated sulfate AOD through affecting sulfate aerosol concentration by wet deposition and aqueous-phase production. Therefore, this study illustrates the urgent need to improve AOD simulation in global climate models.</h3>

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