TY  - SER
AU  - Duplessy,Jean-Claude
AU  - Corti,Susanna
AU  - Kirtman,Ben P.
AU  - Lu,Jian
AU  - Li,Jianping
AU  - WU,Zhaohua
AU  - Krishnamurthy,V.
AU  - Ha,Kyung-Ja
AU  - Misra,Vasubandhu
AU  - Takahashi Guevara,Ken
AU  - Sun,Lantao
ED  - SpringerLink (Online service)
TI  - Climate Dynamics: Observational, Theoretical and Computational Research on the Climate System 
SN  - 1432-0894
CY  - Berlin/Heidelberg
PB  - Springer Berlin Heidelberg, Imprint: Springer.
KW  - Geophysics
KW  - Climatology
KW  - Oceanography
KW  - Climate Sciences
KW  - Ocean Sciences
N2  - Climate Dynamics publishes high-quality original research that advances understanding of the dynamics of the coupled climate system. The journal focuses on the physical and biophysical processes and interactions that govern climate variability and change across timescales from intraseasonal to centennial and longer. We welcome observational, paleoclimatic, theoretical, diagnostic, and numerical modeling studies that: Identify and explain dynamical mechanisms underlying the climate state and its variability and change, including teleconnections, decadal predictability, regime behavior/tipping points, and climate sensitivity. Clarify physical and biophysical interactions among the atmosphere, ocean, cryosphere, land surface, and biosphere that shape large-scale climate structure and evolution, considering the coupling with biogeochemical processes. Advance understanding of predictability, feedback processes, and dynamical constraints on internal variability and externally forced change. Diagnose and interpret climate model behavior, including systematic biases, inter-model spread, and physically based constraints on projections. Develop or apply methodological advances that provide clear new insight into climate-system dynamics. Regional and basin-scale studies, including high-resolution modeling or downscaling, may be considered when they illuminate underlying dynamical mechanisms or reveal a new understanding of how large-scale climate dynamics manifest at smaller scales. Studies employing machine learning or other data-driven methods are considered when they provide clear physical insight or advance understanding of climate dynamics and predictability. Applications focused primarily on algorithmic performance or technical implementation without new dynamical insight are generally outside the journal’s scope. Manuscripts primarily focused on impacts, adaptation or mitigation, product development, or technical method applications/comparisons without substantive implications for climate-system dynamics are beyond the scope of this journal.
UR  - https://doi.org/10.1007/382.1432-0894
ER  -