- CSIRO, Oceans and Atmosphere, Adjunctadd
- Physical Oceanography, Ocean Heat exchanges, Global change, Hydrological Cycle, Oceanography, Global Water Cycle Change, and 12 moreInterocean exchanges, Water and Energy, Climate Change, Water Cycle, Ocean freshwater exchanges, Climate modeling, Climate variability, Climate Change Impacts, The Global Ocean's Role in the Water Cycle, Sustainable Development, Environmental Sustainability, and Global Warmingedit
Climate change is fundamentally altering marine and coastal ecosystems on a global scale. While the effects of ocean warming and acidification on ecology and ecosystem functions and services are being comprehensively researched, less... more
Climate change is fundamentally altering marine and coastal ecosystems on a global scale. While the effects of ocean warming and acidification on ecology and ecosystem functions and services are being comprehensively researched, less attention is directed toward understanding the impacts of human‐driven ocean salinity changes. The global water cycle operates through water fluxes expressed as precipitation, evaporation, and freshwater runoff from land. Changes to these in turn modulate ocean salinity and shape the marine and coastal environment by affecting ocean currents, stratification, oxygen saturation, and sea level rise. Besides the direct impact on ocean physical processes, salinity changes impact ocean biological functions with the ecophysiological consequences are being poorly understood. This is surprising as salinity changes may impact diversity, ecosystem and habitat structure loss, and community shifts including trophic cascades. Climate model future projections (of end of the century salinity changes) indicate magnitudes that lead to modification of open ocean plankton community structure and habitat suitability of coral reef communities. Such salinity changes are also capable of affecting the diversity and metabolic capacity of coastal microorganisms and impairing the photosynthetic capacity of (coastal and open ocean) phytoplankton, macroalgae, and seagrass, with downstream ramifications on global biogeochemical cycling. The scarcity of comprehensive salinity data in dynamic coastal regions warrants additional attention. Such datasets are crucial to quantify salinity‐based ecosystem function relationships and project such changes that ultimately link into carbon sequestration and freshwater as well as food availability to human populations around the globe. It is critical to integrate vigorous high‐quality salinity data with interacting key environmental parameters (e.g., temperature, nutrients, oxygen) for a comprehensive understanding of anthropogenically induced marine changes and its impact on human health and the global economy.
Research Interests:
Research Interests:
Research Interests:
Ultrascale Visualization Climate Data Analysis Tools
Research Interests:
Research Interests:
Research Interests:
<strong>CMOR tables in sync with 01.beta.30 CMIP6 data request</strong> fix traceback color; fix email address and website in CV<br> Fix wrong JSON error message<br> Create default directory automatically; Add... more
<strong>CMOR tables in sync with 01.beta.30 CMIP6 data request</strong> fix traceback color; fix email address and website in CV<br> Fix wrong JSON error message<br> Create default directory automatically; Add cmip6_cv module; Add CMIP6Validator.py<br> Fix dataset_json return value<br> Fix sub_experiment to s1968 to avoid warning in test<br> Fix wrong_activity test; change variant_id to variant_label in cmor.h<br> Update experiments with Karkl Taylor's file 062116.cvs ; Add unittest for baddirectory ; Fix Control Vocabulary file file;<br>
Includes a sample set of mean climate jsons for mean climate of CMIP5 historical simulations
A new initiative collects, archives, and documents climate forcing data sets to support coordinated modeling activities that study past, present, and future climates.
Research Interests:
A new climate model evaluation package will deliver objective comparisons between models and observations for research and model development and provide a framework for community engagement.
Research Interests:
Research Interests:
The Working Group I contribution to the Sixth Assessment Report is the most up-to-date physical understanding of the climate system and climate change, bringing together the latest advances in climate science, and combining multiple lines... more
The Working Group I contribution to the Sixth Assessment Report is the most up-to-date physical understanding of the climate system and climate change, bringing together the latest advances in climate science, and combining multiple lines of evidence from paleoclimate, observations, process understanding, and global and regional climate simulations
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Climate Model Output Rewriter
CMIP6 Forcing Datasets (input4MIPs): These data includes all datasets published for 'input4MIPs.CMIP6.CMIP.PCMDI' according to the Data Reference Syntax defined as... more
CMIP6 Forcing Datasets (input4MIPs): These data includes all datasets published for 'input4MIPs.CMIP6.CMIP.PCMDI' according to the Data Reference Syntax defined as 'activity_id.mip_era.target_mip.institution_id.source_id.realm.frequency.variable_id.grid_label'. Project: The forcing datasets (and boundary conditions) needed for CMIP6 experiments are being prepared by a number of different experts. Initially many of these datasets may only be available from those experts, but over time as part of the 'input4MIPs' activity most of them will be archived by PCMDI and served by the Earth System Grid Federation (https://esgf-node.llnl.gov/search/input4mips/ ). More information is available in the living document: http://goo.gl/r8up31 .
Research Interests:
Climate Model Output Rewriter
Markdown version: Main changes from last release (3.2.6 -> 3.2.7) Update netcdf libraries in order to run UV-CDAT and CMOR in the same conda environment. Create a conda shared environment to facilitate installation... more
Markdown version: Main changes from last release (3.2.6 -> 3.2.7) Update netcdf libraries in order to run UV-CDAT and CMOR in the same conda environment. Create a conda shared environment to facilitate installation https://github.com/PCMDI/cmor/blob/master/recipes/conda-envs/cmor.yml conda env create -f cmor.yml Fix HISTORY metadata for Obs4MIPS. CMOR 3.2.7 documentation http://cmor.llnl.gov or in pdf form at http://cmor.llnl.gov/pdf/ DOI (source code) https://doi.org/10.5281/zenodo.863751 Github (source code) https://github.com/PCMDI/cmor/releases/tag/CMOR-3.2.7 Conda installation To install cmor into your root anaconda environment conda install -c conda-forge -c pcmdi cmor Or to generate a dedicated anaconda environment conda create -n cmor3.2.7 -c conda-forge -c pcmdi cmor
Research Interests:
CMIP6 Forcing Datasets (input4MIPs): These data includes all datasets published for 'input4MIPs.CMIP6.CMIP.PCMDI.PCMDI-AMIP-1-1-4' according to the Data Reference Syntax defined as... more
CMIP6 Forcing Datasets (input4MIPs): These data includes all datasets published for 'input4MIPs.CMIP6.CMIP.PCMDI.PCMDI-AMIP-1-1-4' according to the Data Reference Syntax defined as 'activity_id.mip_era.target_mip.institution_id.source_id.realm.frequency.variable_id.grid_label'. The model PCMDI-AMIP-1-1-4 (PCMDI-AMIP 1.1.4: Merged SST based on UK MetOffice HadISST and NCEP OI2 (observations - satellite_blended: Based on Hurrell SST/sea ice consistency criteria applied to merged HadISST (1870-01 to 1981-10) & NCEP-0I2 (1981-11 to 2017-12))) was run by the Program for Climate Model Diagnosis and Intercomparison, Lawrence Liver more National Laboratory, Livermore, CA 94550, USA (PCMDI) in native nominal resolutions: 1x1 degree longitude x latitude. Project: The forcing datasets (and boundary conditions) needed for CMIP6 experiments are being prepared by a number of different experts. Initially many of these datasets may only be available from those experts, but over time as part of the 'input4MIPs' activity most of them will be archived by PCMDI and served by the Earth System Grid Federation (https://esgf-node.llnl.gov/search/input4mips/ ). More information is available in the living document: http://goo.gl/r8up31 .
CMIP6_CVs-6.2.55.1/DREQ-01.00.33/CMOR-3.6.1 Changes End of support for Python 2.7. CMOR now supports Python 3.9. Bugfixes Fixed handling of Unicode string values in <code>cmor.axis</code> for the Python API. Closed Issues... more
CMIP6_CVs-6.2.55.1/DREQ-01.00.33/CMOR-3.6.1 Changes End of support for Python 2.7. CMOR now supports Python 3.9. Bugfixes Fixed handling of Unicode string values in <code>cmor.axis</code> for the Python API. Closed Issues Discontinue Python 2.7 support in the next version of CMOR Python 3.9 support cmor.axis is not handling unicode string values correctly in Python GitHub
The UV-CDAT team is pleased to announce the release of UV-CDAT version 2.10. <strong>IMPORTANT NOTE</strong> Due to a bug in conda, the following version of conda will not install properly 4.3.13, 4.3.14, 4.3.15, 4.3.16,... more
The UV-CDAT team is pleased to announce the release of UV-CDAT version 2.10. <strong>IMPORTANT NOTE</strong> Due to a bug in conda, the following version of conda will not install properly 4.3.13, 4.3.14, 4.3.15, 4.3.16, 4.3.17. Hence please make sure your version of conda is not one of the above-metioned. To be safe run: conda install -n root "conda<4.3.13" UV-CDAT is distributed via anaconda: conda create -n uvcdat-2.10 uvcdat -c conda-forge -c uvcdat Major changes in v2.10 <strong>NOX versions discontinued as stand-alone</strong> Starting with 2.10 there is no need to install a special version of uvcdat (or vcs, dv3d, vcsaddons, etc...) to obtain a mesa version of UV-CDAT packages, simply add the mesalib package to your environment <strong>CDMS</strong>: Switch from esmp legacy to esmpy for python interface to ESMF regridder<br> <strong>CDMS</strong>: Numpy 1.12<br> <strong>Build</strong>: Conda ...
First release available through anaconda/durack1
Ultrascale Visualization Climate Data Analysis Tools
Research Interests:
Markdown version: Main changes from last release (3.2.6 -> 3.2.7) Update netcdf libraries in order to run UV-CDAT and CMOR in the same conda environment. Create a conda shared environment to facilitate installation... more
Markdown version: Main changes from last release (3.2.6 -> 3.2.7) Update netcdf libraries in order to run UV-CDAT and CMOR in the same conda environment. Create a conda shared environment to facilitate installation https://github.com/PCMDI/cmor/blob/master/recipes/conda-envs/cmor.yml conda env create -f cmor.yml Fix HISTORY metadata for Obs4MIPS. CMOR 3.2.7 documentation http://cmor.llnl.gov or in pdf form at http://cmor.llnl.gov/pdf/ DOI (source code) https://doi.org/10.5281/zenodo.863751 Github (source code) https://github.com/PCMDI/cmor/releases/tag/CMOR-3.2.7 Conda installation To install cmor into your root anaconda environment conda install -c conda-forge -c pcmdi cmor Or to generate a dedicated anaconda environment conda create -n cmor3.2.7 -c conda-forge -c pcmdi cmor
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
ABSTRACT Long-term global ocean salinity variation provides an insight into water cycle change. This connection reflects changes to the evaporation and precipitation (E–P) fields along with terrestrial runoff, which comprises the global... more
ABSTRACT Long-term global ocean salinity variation provides an insight into water cycle change. This connection reflects changes to the evaporation and precipitation (E–P) fields along with terrestrial runoff, which comprises the global water cycle and sets the spatial pattern of salinity on the ocean surface. The dynamic nature of the global ocean ensures that along with E–P, temperature and circulation changes also play a role in driving patterns of salinity change. This chapter provides an introduction to the global water cycle, briefly outlines the history of ocean salinity observation, and introduces results that relate resolved salinity change to water cycle change. Because of sparse observational coverage, the use of climate models are necessary to investigate these relationships. Long-term changes to global ocean salinity suggest that an unambiguous and coherent water cycle change has occurred over the twentieth and early twenty-first centuries. Climate model simulations project that such changes will intensify in the twenty-first century in response to continued greenhouse gas emissions.