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 OI sea surface temperature (SST) analysis is produced weekly 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 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.

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/.

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/"

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/.

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/"

Data set consists of a time series of sea height derived from altimeter measured on the TOPEX/Poseidon satellite from the University of Texas at Austin Center for Space Research (UTCSR).

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 perspective discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision-making. Following an unprecedented sequence of five droughts, in 2022, 23 million east Africans faced starvation, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution-based insights can be combined with the latest dynamic models to predict droughts at eight-month lead-times. We then discuss behavioral and social barriers to forecast use and review literature examining how EWS might (or might not) enhance agro-pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization (WMO) “Early Warning for All” plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African-led EWS, and building better links between EWS and agricultural development efforts can support long-term adaptation, reducing chronic needs for billions of dollars in reactive assistance. Methods This data set draws from four widely used sources: the Climate Hazard Center Infrared Precipitation with Stations archive (CHIRPS), the NOAA Extended Reconstruction sea surface temperature data set (version 5), seasonal SST forecasts from the North American Multi-Model Ensemble (NMME) and projected SST time-series from Phase 6 of the Climate Model Intercomparison Project (CMIP6). While all of these data are publicly available, we pull together here all the salient time series supporting the basic results of our paper. The NMME seasonal climate forecasts are based on coupled ocean-atmosphere models, intialized monthly with observed conditions. The coupled ocean-atmosphere models in the CMIP6 archive, on the other hand, are initialized in the early 19th century, and then run into the future, constrained by changes in aerosols and greenhouse gasses. The NMME provide operational forecasts. The CMIP6 provides climate change simulations. The data are organized in a spreadsheet with tabs corresponding to figure panels. The Figure 1B tab contains 1981–2022 March-April-May (MAM) and October-November-December (OND) CHIRPS rainfall totals averaged over the eastern Horn of Africa (Ethiopia, Kenya and Somalia east and south of 38E, 8N). This extremely food-insecure area suffers from sequential droughts. There has also been a well-documented decline in the MAM rains beginning around 1999. This tab also contains seasonal totals expressed as 'Standardized Precipitation Index' (SPI) values. These were calculated by fitting a Gamma distribution to the MAM and OND rainfall time-series and then translating the associated quantile values to a standard normal distribution. Seasons with SPI values of less than -0.44Z or greater than +0.44Z fall within the below-normal or above-normal terciles. The Figure 1E tab contains observed standardized 'West Pacific Gradient' (WPG) and 'Western V Gradient' (WVG) time-series for, respectively, the OND and MAM seasons. These gradients measure the difference between standardized equatorial east Pacific (NINO3.4) and standardized west Pacific SST time series. The data are standardized because relatively small temperature increases in the very warm west Pacific can be dynamically important. The observed gradient values show that warming in the west Pacific, combined with a lack of warming in the NINO3.4 region, has led to large increases in Pacific SST gradients. This sets the stage for sequential droughts in the eastern Horn. The Figure 1F tab contains Indo-Pacific SST time-series from 152 CMIP6 climate change simulations. These simulations are based on the moderate warming Shared Socio-economic Pathway 245 scenario (SSP245). Time-series are provided for the OND equatorial west Pacific, MAM Western V region, and OND western Indian Ocean region. Observed NOAA SST time series are also provided. The human-induced warming signal is pronounced in the CMIP6 simulations. During the 2016/17 and 2020/2022 La Niña sequences, climate change contributed to exceptionally warm equatorial west Pacific and Western V SST. During the positive Indian Ocean Dipole event in 2019, climate change contributed to exceptionally warm western Indian Ocean SST. The western Indian Ocean region corresponds with the western box used to calculate the Indian Ocean Dipole (IOD). The 2019 IOD event was associated with flooding and a desert locust outbreak. The 2020–2022 period was associated with five sequential droughts in East Africa. The Figure 2A tab contains observed and predicted 1982–2022 MAM and OND Pacific gradient time series (WVG and WPG). The forecasts are based on six models from the North American Multi-model Ensemble (NMME). The OND forecasts are based on NMME predictions made in May. The MAM forecasts are based on NMME predictions from September. The data have been accessed via the IRI data library. Six individual standardized SST forecasts for the NINO3.4 and west Pacific regions are extracted for each model and then combined using a weighted average proportional to each model's skill (R2). The NINO3.4 and west Pacific SST are then used to calculate the WVG and WPG forecasts. Observed WVG and WPG values are based on NOAA Extended reconstruction version 5 SST. The Figure 2B tab is very similar to 2A but contains the west Pacific OND and MAM time series. While SST observations and CMIP6 simulations indicate more frequent extremely warm SSTs (tabs 1E and 1F), these can be predicted surprisingly well, offering opportunities to anticipate associated climate extremes. The Figure 3A tab contains the CMIP6 simulation data supporting panel 3A. The standardized WPG and WVG time series are provided for 152 CMIP6 SSP245 simulations, and the individual changes in event frequencies have been calculated for each simulation. These changes contrast WPG and WVG event frequencies in 2020–2030 versus 1920-1979. An increase in event frequency is a very robust result, due to the very robust warming in the west Pacific. This latter warming can be verified via the data in the Figure 1F tab if desired. Note that a few CMIP6 models only had one simulation. Results for these models were not listed in the inset in Fig. 3A, due to space limitations.

Westerly Moisture Transport Events (WMTEs): A flexible framework for studying intraseasonal variability in East Africa

Robert Peal and Emily Collier

Department of Atmospheric and Cryospheric Sciences (ACINN) University of Innsbruck.

Contact: robert.peal@uibk.ac.at

This repository contains the data and code used in "Drivers and impacts of westerly moisture transport events in East Africa" by Robert Peal and Emily Collier (submitted to Weather and Climate Dynamics)

What's inside?

The data

Code

Code inside utils.tar

Utility programs used for data processing and for generating the WMTE masks

NOTE: Nearly all the code requires the scripts tctools2.py, cartopy_local.py, and pytime.py and the folder cartopyData to be in the python path in order to run. These are all in utils.tar. If you want to run the code, I recommend to unpack utils.tar and then copy tctools2.py, cartopy_local.py, pytime.py and cartopyData into the folder of the script you are running so that they can definitely be imported. Otherwise you can edit the path using sys.path.append() to add the appropriate location.

Code inside figures.tar

each folder contains the code and data for a different figure from the paper. Processing is done by the python script inside, and the figure is generated in the notebook.

References

Bureau of Meteorology (BoM).: Real-time Multivariate MJO (RMM) Phase Index, https://iridl.ldeo.columbia.edu/SOURCES/.BoM/.MJO/.RMM/phase/index.html, available from IRI/LDEO Climate Data Library, Accessed 06.02.2024.

Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data, Bulletin of the American Meteorological Society, 91, 363 – 376, https://doi.org/https://doi.org/10.1175/2009BAMS2755.1, 2010

Konstali, K., Spensberger, C., Spengler, T., and Sorteberg, A.: Global Attribution of Precipitation to Weather Features, Journal of Climate, 37, 1181 – 1196, https://doi.org/10.1175/JCLI-D-23-0293.1, 2024.

Spensberger, C.: Dynlib: A library of diagnostics, feature detection algorithms, plotting and convenience functions for dynamic meteorology. https://doi.org/10.5281/zenodo.4639624, 2021.

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 Scripps Institution of Oceanography mixed layer depth 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/"

The Lamont-Doherty Deep-Sea Sample Repository, located in the Core Laboratory at the Lamont-Doherty Earth Observatory (LDEO) of Columbia University, contains archived sediment cores from every major world ocean and sea. The Core Repository contains approximately 72,000 meters of core composed of 9,700 piston cores; 7,000 trigger weight cores; and 1,500 other cores such as box, kasten, and large diameter gravity cores. There are also 4,000 dredge and grab samples including a large collection of manganese nodules, and many samples recovered by submersibles.

The core sample can be searched through the Deep-Sea Core Database at the LDEO/IRI Climate Data Library for coordinates of the site, water depth, topography, core length or dredge size, sample device, date and time of retrieval. "http://ingrid.ldgo.columbia.edu/SOURCES/.LDEO/.Deep_Sea_Core.cuf/"

Data on the cores is stored at NOAA/National Gephysical Data Center (NGDC) Marine Geology and Geophysics (MGG): "http://www.ngdc.noaa.gov:80/mgg/curator/curator.html"

Data set consists of a time series of sea height derived from altimeter measured on the TOPEX/Poseidon satellite.

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 data set consists of blended sea level anomaly from ERS-1 altimeter data and tide gauge data over the Pacific Ocean. Annual and semiannual signals have been removed, leaving only the interannual changes relative to the April 1985-86 mean.

The data is 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 Standardized Precipitation Index (SPI; McKee et al. 1993) is the number of standard deviations that observed cumulative precipitation deviates from the climatological average. The index is based entirely on monthly precipitation accumulations and its values can be compared across different climatic and geographic regions. These characteristics of the SPI have contributed to its popularity for application towards drought and water resource monitoring.

SPI analyses were performed on the following climatological data sets: NASA GPCP V2 NOAA NCEP CPC CAMS_OPI NOAA NCEP CPC Merged_Analyis (CMAP) UEA CRU New CRU05

Studies have suggested that a minimum of 50 years of precipitation data be used to calculate SPI values. It should therefore be noted that the UEA New data set is the only precipitation data set in the current set of analyses that meets that recommendation. Extreme SPI values in the other three datasets (CAMS OPI, CMAP, GPCP) may be suspect as they are based on roughly half of the recommended amount of data.

The analyses in this data set are based on a Pearson Type III (i.e., 3-parameter gamma) distribution as suggested by Guttman (1999). Fortran 77 source code made available in that reference was used to create the SPI analyses.

The SPI analyses are available from: http://ingrid.ldeo.columbia.edu/docfind/databrief/cat-atmos.html

Wind vectors and sea surface temperatures (SSTs) from the TOGA-TAO array of current meter moorings and ATLAS thermistor chain moorings. Both quarterly menas and quarterly anomalies are given. Anomalies are calculated from the COADS wind climatology and the Reynolds and Smith (1994) Optimal Interpolation SST climatology.

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 North Atlantic Oscillation (NAO) is based on the difference of normalized sea level pressure (SLP) between Lisbon, Portugal and Stykkisholmur/Reykjavik, Iceland from 1864 through 1995.

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

This data set is a global assimilation analysis of the ocean using a General Circulation Model of the oceans based on the GFDL MOM2 code to study the ocean's role in climate variability. The assimilation algorithm is an extension of Optimum Interpolation. Winds are provided by a monthly COADS analysis. Surface and subsurface temperature updating is carried out using most historical shipboard and satellite temperature information.

 The analyses are constructed using the Simple Ocean Data Assimilation
 (SODA) analysis package. SODA is an application of data assimilation
 using a forecast model based on GFDL ocean physics driven by observed
 historical meteorology (winds, heating, and rainfall-evaporation),
 assimilating historical observations of temperature, salinity, sea
 level, SST, and surface current.

 Data available from the LDEO/IRI Climate Data Library
 "http://ingrid.ldeo.columbia.edu/SOURCES/.UMD/"