The monthly optimum interpolation (OI) fields are derived by a linear interpolation of the weekly OI fields to daily fields then averaging the daily values over a month. The monthly fields are in the same format and spatial resolution as the weekly fields.

The anomalies (SSTA) are computed from these monthly fields.

Data from the Joint World Meteorological Organization/Intergovernmental Oceanographic Commission Technical Commision for Oceanography and Marine Meteorology (JCOMM) Products Bulletin Data Products. The organization was formally known as the Integrated Global Ocean Services System (IGOSS) Data Products Bulletin.

For further data products see: "http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/"

"http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/.data_products.html"

and "http://iri.ldeo.columbia.edu/climate/monitoring/ipb/"

The IRI Climate Data Library contains over 300 datasets from a variety of earth science disciplines and climate-related topics. It is web-accessible via a browser interface. Functions for display, analysis, and sub-setting of global climate datasets are available. Data extraction and download for multiple data and mapping formats are easily performed.

The OI sea surface temperature (SST) analysis is produced weekly (Sunday to Saturday) on a one-degree grid. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). A description of the OI analysis can be found in Reynolds and Smith (1994). The bias correction improves the large scale accuracy of the OI. Examples of the effect of recent corrections is given by Reynolds (1993).

For the more recent period, 1990-present, the in situ data were obtained from radio messages carried on the Global Telecommunication System. The satellite observations were obtained from operational data produced by the National Environmental Satellite, Data and Information Service (NESDIS)

During the period 1981-1989, the in situ data were obtained from the Comprehensive Ocean Atmosphere Data Set (COADS) for the 1980s. These data (see Slutz, et al., 1985, and Woodruff, et al., 1993) consist of logbook and radio reports. The satellite data were obtained from analyses of NESDIS data produced at the University of Miami's Rosentiel School of Marine and Atmospheric Sciences.

The OI analysis is done over all ocean areas. There is no analysis over land. The land values are filled by a Cressman interpolation to produce a complete grid for possible interpolation.

Data from the Joint World Meteorological Organization/Intergovernmental Oceanographic Commission Technical Commision for Oceanography and Marine Meteorology (JCOMM) Products Bulletin Data Products. The organization was formally known as the Integrated Global Ocean Services System (IGOSS) Data Products Bulletin.

For further data products see: "http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/"

"http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/.data_products.html"

and "http://iri.ldeo.columbia.edu/climate/monitoring/ipb/"

The IRI Data Library is a powerful and freely accessible online data repository and analysis tool that allows a user to view, manipulate, and download over 400 climate-related data sets through a standard web browser. The Data Library contains a wide variety of publicly available data sets, including station and gridded atmospheric and oceanic observations and analyses, model-based analyses and forecasts, and land surface and vegetation data sets, from a range of sources. It includes a flexible, interactive data viewer that allows a user to visualize. multi-dimensional data sets in several combinations, create animations, and customize and download plots and maps in a variety of image formats. The Data Library is also a powerful computational engine that can perform analyses of varying complexity using an extensive array of statistical analysis tools. Online tutorials and function documentation are available to aid the user in applying these tools to the holdings available in the Data Library. Data sets and the results of any calculations performed by the user can be downloaded in a wide variety of file formats, from simple ascii text to GIS-compatible files to fully self-describing formats, or transferred directly to software applications that use the OPeNDAP protocol. This flexibility allows the Data Library to be used as a collaborative tool among different disciplines and to build new data discovery and analysis tools.

Hemispheric land surface temperature anomalies from P.D. Jones at University of East Anglia/Climate Research Unit. A. Kaplan at LDEO/IRI modified the Jones temperature data set archived at the National Center for Atmospheric Research (NCAR). See: "http://dss.ucar.edu/datasets/ds215.0/"

These data sets consist of locations of global occurrences of volcanoes and earthquakes. Each data set contains the latitude and longitude of the event. The earthquake data sets contains the magnitude of the earthquake and depth. The volcano data set contains the name of the volcano. An interactive map is available to view the information contained in the data sets.

The data set contains data on almost 8,000 institutions that cooperate or have cooperated in building CEZL/CASLIN union catalogs, participate in interlibrary services, are in contact with the Library Institute of the National Library of the Czech Republic or are registered in the Ministry of Culture of the Czech Republic.

The IRI-Scotland project (http://www.iriscotland.lib.ed.ac.uk/) carried out a series of online questionnaires in 2006 to assess the attitudes towards open access and institutional repositories within the higher education community in Scotland. In total, three questionnaires were targeted at different stakeholder groups within the community - academic authors, technical staff responsible for repository development, and senior management from academic libraries. The second IRI-Scotland survey was targeted at technical staff, usually based in academic libraries or aligned information services support groups, who would be responsible for developing a digital repository in their institution. The questions were aimed to help determine the functional requirements needed to build a national hosted repository service that would be suitable for the current and future repository infrastructure in Scotland. We present here anonymous data from the technical staff survey in comma separated value format.

Monthly mean surface pseudo-stress using variational, subjective, and objective-subjective techniques in the Indian, Pacific, and Atlantic oceans.

Data from the Joint World Meteorological Organization/Intergovernmental Oceanographic Commission Technical Commision for Oceanography and Marine Meteorology (JCOMM) Products Bulletin Data Products. The organization was formally known as the Integrated Global Ocean Services System (IGOSS) Data Products Bulletin.

For further data products see: http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/, http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/.data_products.html, and http://iri.ldeo.columbia.edu/climate/monitoring/ipb/.

This dataset contains the provenance information (in N-Triples format) of all the citation data included in COCI, released on 23 January 2023. In particular, any citation in the dataset includes the following provenance information:

[citation IRI] the Open Citation Identifier (OCI) for the citation, defined in the final part of the URL identifying the citation (https://w3id.org/oc/index/coci/ci/[OCI]);; [property "prov:wasAttributedTo"] the IRI of the agent that have created the citation data; [property "prov:hadPrimarySource"] the IRI of the source dataset from where the citation data have been extracted; [property "prov:generatedAtTime"] the creation time of the citation data. [propert "prov:invalidatedAtTime"] the start of the destruction, cessation, or expiry of an existing entity by an activity. [property "oco:hasUpdateQuery"] the UPDATE SPARQL query that keeps track of which metadata have been modified.

The size of the zipped archive is 78 GB, while the size of the unzipped N-Triples file is 3.3 TB.Additional information about COCI are available at official webpage.

The dataset includes over three million bibliographic records describing materials held by the National Library of the Czech Republic. So far about one fifth of the National Library’s holdings are represented. All new books acquired since 1995 are included and older records are continuously being added. Most Czech titles published during the 20th century are part of the dataset.

The accompanying data used for an analysis of animal-related outages in the state of Massachusetts from 2013-2018. All data formating and analysis took place in R version 4.0.3. The original outage data comes from Eversource Energy, National Grid, and Unitil Corporation, made available through the MA office of Energy and Environmental Affairs. The outage dataset used in this analysis is available on Columbia University's International Research Institute (IRI) for Climate and Society Data Library at http://iridl.ldeo.columbia.edu/SOURCES/.EOEEA/. The original bird abundance data comes from the eBird Basic Dataset (May 2020) and the modeled relative abundance estimates for Massachusetts towns are also available on the IRI Data Library at http://iridl.ldeo.columbia.edu/SOURCES/.PRISM/.eBird/.derived/.detectionProbability/.

Sea level pressure indices from Tahiti for monitoring El Nino Southern Oscillation (ENSO).

Description: These data represent gridded monthly SST anomalies for 399 consecutive months from January 1970 through March 2003. The data were obtained from the IRI/LDEO Climate Data Library at Columbia University (http://iridl.ldeo.columbia.edu/). The data are gridded at a 2 degree by 2 degree resolution and represent anomalies from a January 1970 - December 1985 monthly (average) climatology. A more complete description can be found at http://iridl.ldeo.columbia.edu/SOURCES/.CAC/ References: In addition to Chapter 5 and 9 of Cressie and Wikle (2011), these data have been described and modeled in the following: Berliner, L.M., Wikle, C.K. and N. Cressie, (2000). Long-lead prediction of Pacific SSTs via Bayesian Dynamic Modeling. Journal of Climate, 13 , 3953-3968. Wikle, C.K. and M.B. Hooten, 2010: A general science-based framework for spatio-temporal dynamical models. Invited discussion paper for Test. 19, 417-451. Wikle, C.K. and S.H. Holan, 2011: Polynomial nonlinear spatio-temporal integro-difference equation models Journal of Time Series Analysis. DOI: 10.1111/j.1467-9892.2011.00729.x

The Scripps Institution of Oceanography heat storage climatology was derived from the NODC Global Ocean Temperature/Salinity data set (1955-1988) and the Global Temperature/Salinity Pilot Project (GTSPP) data set (1989-1994). Temperatures were interpolated at 15 standard levels from 0 to 800 meters. The climatological series was based on the January 1980-December 1989 data.

Data from the Joint World Meteorological Organization/Intergovernmental Oceanographic Commission Technical Commision for Oceanography and Marine Meteorology (JCOMM) Products Bulletin Data Products. The organization was formally known as the Integrated Global Ocean Services System (IGOSS) Data Products Bulletin.

For further data products see: "http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/"

"http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/.data_products.html"

and "http://iri.ldeo.columbia.edu/climate/monitoring/ipb/"

Intro

This repository contains replication data for Schwarzwald, Kevin and Richard Seager (2024), "'Revisiting the “East African Paradox': CMIP6 models also struggle to reproduce strong observed MAM long rain drying trends." (under revision at Journal of Climate). This includes the data necessary to replicate all main text figures and most figures in Supplementary Materials. Additional figures in Supplementary Materials require raw precipitation time series, detailed below. This repository also includes a copy of the code necessary to replicate the entire study; the latest version of the code, in addition to instructions on how to use it, is kept at this GitHub archive.

Structure

The repository is structured as follows:

• code: Static / stable version of reproduction code for Schwarzwald and Seager (2024) that uses and creates these data (see here for more detailed instructions)
• figures: Static / stable versions of main and supplemental figures for Schwarzwald and Seager (2024).
• climate_raw: "raw" (often pre-processed) climate data files; only some files are included, see below for more details
• climate_proc: Processed climate data files upon which the analysis is based, used by main and supplemental figure code
• aux_data: Certain auxiliary data files (fonts, critical values) and intermediate files for long code processes. Created and used by the replication code.

Notes on raw climate data

Due to space limitations (and a desire to not create yet another cloud copy of CMIP6 data), this repository only contains processed CMIP6 data: the calculated linear trends of rainfall, sea surface temperatures, and 500 hPa geopotential height used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the ESGF, and consists of every available CMIP6 monthly rainfall, sea surface temperature (SST), and geopotential height file on the archive at the time of processing for the experiments detailed in the manuscript. For precipitation, only a bounding box (-3 S to 12.5 N, 32 E to 55 E) around East Africa was downloaded, and saved with the file suffix "_HoAfrica" (see replication code README for more details). One example precipitation file (one ensemble member of ACCESS-CM2 historical precipitation) is included in this repository for reference.

Similarly, this repository only contains processed NMME data: calculated linear trends of rainfall used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the IRI Data Library and consists of every available monthly rainfall hindcast / forecast file from the NMME archive for the models used (see manuscript Table S1). As above, only a bounding box around East Africa was downloaded, and files were standardized to a partial CMIP* file format (one file per variable, with CMIP file and variable name conventions, but with forecast lead time as an additional dimension). Preprocessing is a bit more extensive than for CMIP6 models: first, hindcasts and forecasts were concatenated into a single file (since hindcasts are saved in the archive only up to the time when the model was operationalized), and then reindexed such that the "time" variable refers to the time for which the forecast is made, and not the time at which the forecast is made. One example precipitation file (CanCM4i rainfall hindcasts/forecasts) is included in this repository for reference.

This repository does, however, contain preprocessed copies of the "raw" gridded observational rainfall data products used (in addition to the processed trends), since harmonizing and standardizing the data into a format easily compatible with the CMIP6 data was a nontrivial amount of work that would be tedious to replicate. For the ten gridded observational data products used, monthly rainfall was brought into the CMIP* file format (one file per variable, with CMIP file and variable name conventions), with one notable exception: all observational rainfall is saved in units of mm/day.

Reanalysis and gridded ocean observations can be downloaded from each product's respective repositories. As before, the code assumes the data have been preprocessed into something akin to the CMIP* file format.

Notes on processed climate data

Note that the repository includes a file (aux_data/pr_doyavg_CHIRPS_historical_seasstats_dunning_19810101-20141231_HoAfrica.nc) containing CHIRPS seasonal characteristics in East Africa, created as part of Schwarzwald et al., 2023, Climate Dynamics. This file is primarily used to set the boundaries of the study region, see the manuscript for details of how it was calculated.

Licensing, citing, questions

These data are offered under a CC 4.0 license, which allows redistribution and reuse as long as they are correctly cited; note that for much of these data (especially for "raw" data), this requires citations to the original creators.

For questions, please feel free to reach out to corresponding author Kevin Schwarzwald.

Vertically averaged (0/400m) heat storage anomaly computed from a 1979-1988 climatology are available as a dataset, including an interactive viewer and downloadable datafiles.

 Data from the Joint World Meteorological
 Organization/Intergovernmental Oceanographic Commission Technical
 Commision for Oceanography and Marine Meteorology (JCOMM) Products
 Bulletin Data Products. The organization was formally known as the
 Integrated Global Ocean Services System (IGOSS) Data Products
 Bulletin.

 For further data products see:
 "http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/"

 "http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/.data_products.html"

 and
 "http://iri.ldeo.columbia.edu/climate/monitoring/ipb/"

【Courtesy of the C. V. Starr East Asian Library University of California, Berkeley】 Japanese date Hōei 7 [1710]. In color. Folded. Wood block print. In Japanese. Relief shown pictorially. Includes distance chart and index.

English description below. The data set contains individual cultural institutions and other cultural places such as theaters, cinemas, galleries, museums, music clubs, libraries, as well as hubs and other businesses with a cultural program. The data set of cultural institutions and other cultural places such as theaters, cinemas, galleries, museums, music clubs, libraries, as well as musical and other companies with a cultural program. Basic contacts, a link to the website and the program, information on accessibility for people with reduced mobility and photographs are added to each.

The dataset includes subject categories used for describing library collections. It contains 624 categories (records).