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Mark A. Snyder
and
Lisa C. Sloan

on Climate Change (IPCC; Houghton et al. 2001 ) presented the most compelling and complete climate change research to date. However, a deficiency in our understanding is the nature of regional-scale climate changes within the broader context of global climate change. Coupled atmosphere–ocean general circulation models (AOGCMs) have been the primary tools for investigating future climate change through the end of this century in the IPCC TAR. AOGCMs are run with relatively coarse horizontal

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J. F. Scinocca
,
V. V. Kharin
,
Y. Jiao
,
M. W. Qian
,
M. Lazare
,
L. Solheim
,
G. M. Flato
,
S. Biner
,
M. Desgagne
, and
B. Dugas

1. Introduction At the Canadian Centre for Climate Modelling and Analysis (CCCma), a new regional climate model, the CCCma Regional Climate Model (CanRCM4), has been developed. CanRCM4’s novelty does not arise from the method of solution in its dynamical core or the climate-based physics package it employs. Both of these are well known and currently operational for global model applications. The novelty of CanRCM4 stems from a new philosophy of coordinating the development and application of

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Xudong Yin
,
Juanjuan Liu
, and
Bin Wang

1. Introduction There are various empirical parameters in climate models, and these parameters have great uncertainties, which may lead to significant uncertainties in climate simulations. There are many studies that quantify uncertainties in climate simulations due to uncertainties in model parameters ( Webster and Sokolov 1998 ; Murphy et al. 2004 ; Stainforth et al. 2005 ; Tebaldi et al. 2005 ; Jackson et al. 2008 ; Meinshausen et al. 2009 ; Rogelj et al. 2012 ). Moolenaar and Selten

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E. Adam Paxton
,
Matthew Chantry
,
Milan Klöwer
,
Leo Saffin
, and
Tim Palmer

have previously implemented single- and mixed-precision codes respectively ( Rüdisühli et al. 2014 ; Gilham 2018 ). As operational weather forecasters experiment with more efficient low-precision hardware, it is natural to ask whether low precision is suitable for climate modeling (i.e., long time scales) and this is the question addressed by the current paper. Compared to weather forecasting, where research in low precision has focused to date, climate modeling presents a different problem

Open access
Wanli Wu
,
Amanda H. Lynch
,
Sheldon Drobot
,
James Maslanik
,
A. David McGuire
, and
Ute Herzfeld

because of technical and environmental limitations. It has been suggested that an alternative to estimating terrestrial water and energy cycles is to use land surface models (LSMs; Bonan 2002 ) or regional climate models (RCMs; Wu and Lynch 2000 ; Wu et al. 2005 ). The models close the water and energy budget by design. Thus, if the large-scale forcing data, which drive LSMs and RCMs, are accurate, and if model biases are small, these modeled water and energy fluxes might be used in lieu of

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Hiroaki Miura
,
Tamaki Suematsu
,
Yuta Kawai
,
Yoko Yamagami
,
Daisuke Takasuka
,
Yuki Takano
,
Ching-Shu Hung
,
Kazuya Yamazaki
,
Chihiro Kodama
,
Yoshiyuki Kajikawa
, and
Yukio Masumoto

Deep Numerical Analysis for Climate (DNA-Climate), started in 2020, is a pilot project to endorse diverse approaches of climate modeling. The acronym “DNA” was chosen in consideration of the analogies between the hierarchical structures of atmospheric phenomena and living organisms ( Held 2005 ). Similar to the multiscale nature of atmospheric phenomena, biological organisms have the multiscale structure of organelles, cells, and organs organized according to the blueprint, deoxyribonucleic

Open access
D. Stammer
,
A. Köhl
,
A. Vlasenko
,
I. Matei
,
F. Lunkeit
, and
S. Schubert

1. Introduction For several decades data assimilation has played a critical role in numerical weather forecasts by providing initial conditions for numerical forecast models. The same holds for seasonal forecasts ( Anderson 2010 ), which today, like weather forecast, is provided on a routine basis, often even by the same numerical forecast centers. In contrast to weather and seasonal forecasts, however, the subject of initialized climate predictions on interannual–decadal time scales has only

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Yongkang Xue
,
Ratko Vasic
,
Zavisa Janjic
,
Fedor Mesinger
, and
Kenneth E. Mitchell

1. Introduction Despite increases in computer power, most atmospheric general circulation model (GCM) simulations still use coarse resolution with the horizontal resolution being about 200–500 km. Therefore, they only produce large- and synoptic-scale atmospheric features. Because of the resolution limitation, which could be crucial for land–atmosphere interaction studies with highly heterogeneous land surface conditions and steep orography, regional climate models (RCMs) have been developed

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Jianjun Ge
,
Nathan Torbick
, and
Jiaguo Qi

Introduction By changing the fluxes of mass and energy between ecosystems and the atmosphere, human modification of the land surface impacts regional and global climate processes ( Pielke et al. 2002 ; Foley et al. 2003 ). Using land–climate modeling techniques, impacts of land-use and land-cover changes on the Earth system can be studied and monitored. Most regional and global atmospheric models developed 20 years ago either ignored or oversimplified the interactions of the atmosphere with

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Ute C. Herzfeld
,
Sheldon Drobot
,
Wanli Wu
,
Charles Fowler
, and
James Maslanik

1. Introduction and objectives The goal of the Western Arctic Linkage Experiment (WALE) is to investigate the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change. To further this goal, climate datasets and climate model results are compiled, collected, and compared for the WALE study region, which includes land areas in Alaska and northwestern Canada at 55°–70°N, 165°–110°W approximately [see McGuire et al. 2006, manuscript submitted to Earth

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