I have a .nc file that contains data every 1 hour of precipitation for 1 full year, my interest is to calculate the daily precipitation and compare with observed data.I have some doubts about the units that the CMIP6 output has since it shows me the same in Kg/m2.sec and when I add the 24 hourly data to transform them into a daily, the values are very small.How could I correct these values so that they are displayed in mm/day?
The summary above tells us that this catalog contains 261 data assets.We can get more information on the individual data assets contained in thecatalog by looking at the underlying dataframe created when we load the catalog:
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The search() method allows the user toperform a query on a catalog using keyword arguments. The keyword argument namesmust match column names in the catalog. The search method returns asubset of the catalog with all the entries that match the provided query.
Intake-esm implements convenience utilities for loading the query results intohigher level xarray datasets. The logic for merging/concatenating the queryresults into higher level xarray datasets is provided in the input JSON file andis available under .aggregation_info property of the catalog:
When comparing many models it is often necessary to preprocess (e.g. renamecertain variables) them before running some analysis step. The preprocessargument lets the user pass a function, which is executed for each loaded assetbefore combining datasets.
Note that both models follow a different naming scheme. We can define a littlehelper function and pass it to .to_dataset_dict() to fix this. Fordemonstration purposes we will focus on the vertical level dimension which iscalled lev in CanESM5 and olevel in IPSL-CM6A-LR.
The summary above tells us that this catalog contains over 268,000 data assets.We can get more information on the individual data assets contained in thecatalog by calling the underlying dataframe created when it is initialized:
NCAR staff: CMIP Analysis Platform data sets that are on GLADE are available to you by default. If you would like to ask for an addition to the repository, contact the NCAR Research Computing help desk to request access to the platform before you submit the request form.
The vast amount of work that has been put into the standardization of these experiments enables climate scientists to use a wealth of data to answer their specific questions, thus refining future models and increasing our understanding of the complex system that is our home planet.
Most of the problems arise from differences in the convention the model output is provided in. This includes, but is not limited to different naming conventions for coordinate variables, units, grid variables.xmip aims to provide lightweight tools, that let you get right to the science, without spending hours on cleaning up the data.
Users with accounts on NERSC can directly access the original model output from the E3SMv1 DECK simulation campaign. The output has been archived on NERSC HPSS using zstash. See below for some examples on how to retrieve files using zstash. For a more information, refer to the zstash documentation.
Below are some basic examples on how to retrieve DECK output data on NERSC. You can retrieve directly from NERSC Cori machine, but for better performance we strongly recommend login to one of the NERSC DTNs (data transfer nodes: dtn.nersc.gov). The example below relies on the E3SM unified environment. Alternatively, you can also install zstash in your own conda environment (see zstash documentation).
If you have any feedback on the CMIP6 data available on AWS please email sustainability-data-initiative@amazon.com. We are acepting requests for additional CMIP6 variables and/or models to be made available to AWS but cannot guarantee that your request will be fulfilled. We will prioritize requests that bring value to the largest number of users. Note that we are not providing technical support through this email account.
We also seek to identify case studies on how CMIP6 data is being used and will be featuring those stories in future publications and events. If you are interested in seeing your story highlighted, please share it with the ASDI team here: sustainability-data-initiative@amazon.com.
A CMIP6 Special Issue is published in GMD (see here). This special issue describes the new design and organization of CMIP and the suite of experiments of its next phase (i.e., CMIP6) in a series of invited contributions. The description of the experiments and forcing data sets define CMIP6 in detail. The papers provide the required information to produce a consistent set of climate model simulations that can be scientifically exploited to address the three broad scientific questions of CMIP6: (1) How does the Earth system respond to forcing?, (2) What are the origins and consequences of systematic model biases?, and (3) How can we assess future climate changes given climate variability, predictability and uncertainties in scenarios? The special issue will include an overview paper on the CMIP6 design and organization, contributions from CMIP6-endorsed MIPs and descriptions of the forcing data sets.
(a) the Earth System Model Evaluation Tool (ESMValTool, Eyring et al., 2016a) is a community-developed diagnostic and performance metrics tool for the evaluation of Earth system models with observations. It includes other well-established model evaluation packages such as the NCAR Climate Variability Diagnostics Package (CVDP, Phillips et al., 2014). The collection of standard namelists for example allows to reproduce the figures from the climate model evaluation chapter of IPCC AR5 (Chapter 9) and parts of the projection chapter (Chapter 12). The ESMValTool is available as open source software on GitHub. The website here shows results produced with the ESMValTool for CMIP5 simulations. This website will be updated with CMIP6 results as soon as the model output is submitted to the ESGF. All modelling groups are encouraged to check the results for their model.
(b) the PCMDI Metrics Package (PMP, Gleckler et al., 2016) emphasises a diverse suite of summary statistics to objectively gauge the level of agreement between model simulations and observations across a broad range of space and time scales. It is built on the Python based Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), a powerful software tool kit that provides cutting-edge data management, diagnostic and visualisation capabilities. The PMP is available as open source software on GitHub.
Since these tools are freely available on GitHub, modelling groups participating in CMIP can additionally make use of these packages. They could choose, for example, to utilize the tools during the model development process in order to identify relative strengths and weaknesses of new model versions also in the context of the performance of other models or they could run the tools locally before publishing the model output to the ESGF. Mechanisms are in place to enable contributions from the broader community. Both tools are designed to readily work across ESGF nodes with the intent of ultimately expediting routine analysis by alleviating the needs for data transfer. We expect the benefits of this activity to become increasingly apparent during the research phase of CMIP6. We encourage the community to consider contributing additional diagnostics and metrics to these CMIP6 evaluation tools. More details on this approach can be found in Eyring et al. (2016b).
CMIP is a project of the World Climate Research Programme (WCRP) providing climate projections to understand past, present and future climate changes. CMIP and its associated data infrastructure have become essential to the Intergovernmental Panel on Climate Change (IPCC) and other international and national climate assessments.
GISS has submitted a number of different configurations to the CMIP6 model data repository via the Earth System Grid Federation (ESGF). A local dataportal for all these results is available here. In addition, we have archived some key derived data (MSU/SSU diagnostics, ocean heat content, some key indices) here.
The following configurations will be used for the DECK, historical and various MIP simulations: GISS-E2.1-G: ModelE/GISS Ocean 22.5L40.This is an updated and improved version of GISS-E2-R used in CMIP5. It uses the ModelE atmospheric code on a lat-lon grid, with 40 layers in the vertical, a model top at 0.1 hPa and is coupled to the GISS ocean model (11.25L40). There are four possible versions of this model that vary in how aerosols and atmospheric chemistry are handled: physics_version=1 (NINT), aerosols and ozone are read in via pre-computed transient aerosol and ozone fields. The aerosol indirect effect is parameterized. physics_version=3 (TCADI), atmospheric composition is calculated using the OMA scheme including the aerosol impacts on clouds. physics_version=4 (TOMAS), atmospheric composition is calculated using the TOMAS scheme including the aerosol impacts on clouds (Lee et al, 2012). physics_version=5 (MATRIX), atmospheric composition is calculated using the MATRIX scheme including the aerosol impacts on clouds (Bauer et al, 2008). (Note that physics_version=2 corresponded to the CMIP5 TCAD configuration which is not being used in CMIP6).Data available here.GISS-E2.1-H: ModelE/Hycom 22.5L32. This uses the same ModelE atmospheric code as above but is coupled to the HYCOM ocean model (tripolar grid ~11L32 - Note that HYCOM output diagnostics are made available remapped to a 1x1 grid with a uniform 33 levels). There are also four possible physics versions as described above. Data available here.GISS-E2.1-G/H-CC: As above but with an interactive Carbon Cycle As for GISS-E2.1-G/H with interactive terrestrial carbon cycle and oceanic bio-geochemistry.Data available here.GISS-E2.2-G/H: As above but with L102 in the atmosphere and a higher model top (0.002 hPa). Additional vertical resolution sufficient to self-generate a QBO and with improved strat/trop exchange and different tuning. Data available here.Our newest updated configurations will soon be available: 006ab0faaa
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