scholarly journals Glacial Inception on Baffin Island: The Role of Insolation, Meteorology, and Topography

2017 ◽  
Vol 30 (11) ◽  
pp. 4047-4064 ◽  
Author(s):  
Leah Birch ◽  
Timothy Cronin ◽  
Eli Tziperman
Keyword(s):  
Mass Balance ◽  
Snow Cover ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Orbital Parameter ◽  
Small Scale ◽  
Baffin Island ◽  
Realistic Topography

Abstract Geologic evidence suggests that the last glacial inception (115 kya) occurred within the mountains of Baffin Island. Global climate models (GCMs) have difficulty simulating this climate transition, likely because of their coarse horizontal resolution that smooths topography and necessitates the use of cumulus parameterizations. A regional configuration of the Weather Research and Forecasting (WRF) Model is used to simulate the small-scale topographic and cloud processes neglected by GCMs, and the sensitivity of the region to Milankovitch forcing, topography, and meteorology is tested. It is found that ice growth is possible with 115-kya insolation, realistic topography, and slightly colder-than-average meteorology, represented by specific years within the past three decades. The simulation with low GCM-like topography shows a negative surface mass balance, even with the relevant orbital parameter configuration, demonstrating the criticality of realistic topography. The downslope growth of the ice sheets is studied by looking at the sensitivity of the mass balance to initial snow cover prescribed beyond that of the present day. It is found that the snow-albedo feedback, via its effects on the mass balance, allows such larger snow cover to persist. Implications for GCM studies of glacial inception 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.

scholarly journals The role of regional feedbacks in glacial inception on Baffin Island: the interaction of ice flow and meteorology

2018 ◽  
Vol 14 (10) ◽  
pp. 1441-1462
Author(s):  
Leah Birch ◽  
Timothy Cronin ◽  
Eli Tziperman
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
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 role of regional feedbacks in the initial inception problem on Baffin Island by asynchronously coupling the Weather Research and Forecast (WRF) model, 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 regional 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.

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).

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.

Reconstructing the distribution of surface mass balance over East Antarctica (DML) from 1850 to present day

2020 ◽  
Author(s):  
Nicolas Ghilain ◽  
Stéphane Vannitsem ◽  
Quentin Dalaiden ◽  
Hugues Goosse
Keyword(s):  
Spatial Distribution ◽  
Mass Balance ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Ice Cores ◽  
Global Climate Models ◽  
East Antarctica ◽  
Surface Mass Balance ◽  
Surface Mass

<p>Over recent decades, the Antarctic Ice Sheet has witnessed large spatial variations at its surface through the surface mass balance (SMB). Since the complex Antarctic topography, working at high resolution is crucial to represent accurately the dynamics of SMB. While ice cores provide a mean to infer the SMB over centuries, the view is very spatially constrained. Global Climate models estimate the spatial distribution of SMB over centuries, but with a too coarse resolution with regards to the large variations due to local orographic effects. We have therefore explored a methodology to statistically downscale the SMB components from the climate model historical simulations (1850-present day). An analogue method is set up over a period of 30 years with the ERA-Interim reanalysis (1979-2010 AD) and associated with SMB components from the Regional Atmospheric Climate Model (RACMO) at 5 km spatial resolution over Dronning Maud in East Antarctica. The same method is then applied to the period from 1850 to present days using an ensemble of 10 simulations from the CESM2 model. This method enables to derive a spatial distribution of SMB. In addition, the changes in precipitation delivery mechanisms can be unveiled.</p>

scholarly journals Stochastic Rainfall Downscaling of Climate Models

2014 ◽  
Vol 15 (2) ◽  
pp. 830-843 ◽  
Author(s):  
D. D’Onofrio ◽  
E. Palazzi ◽  
J. von Hardenberg ◽  
A. Provenzale ◽  
S. Calmanti
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Amplitude Distribution ◽  
Global Climate Models ◽  
Added Value ◽  
Small Scale ◽  
Physically Based ◽  
Stochastic Downscaling

Abstract Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model.

Statistical Downscaling for Climate Science

2019 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
Climatic Changes ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

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 Differences between downscaling with spectral and grid nudging using WRF

2012 ◽  
Vol 12 (8) ◽  
pp. 3601-3610 ◽  
Author(s):  
P. Liu ◽  
A. P. Tsimpidi ◽  
Y. Hu ◽  
B. Stone ◽  
A. G. Russell ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Global Climate Models ◽  
Small Scale ◽  
Data Set ◽  
Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

scholarly journals Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
Author(s):  
David E. Rupp ◽  
Philip W. Mote ◽  
Nathaniel L. Bindoff ◽  
Peter A. Stott ◽  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Forcing ◽  
Detection And Attribution ◽  
Snow Cover Extent

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

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