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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
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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/.
These monthly climatologies were compiled by Esbensen and Kushnir (heat budget) and by Han and Lee (stress) in 1981, and are archived at the National Center for Atmospheric Research (NCAR). Global ocean radiation and heat budget data are prepared on a 4 X 5 degree latitude/longitude grid and are described in Oregon State University (OSU) Climate Research Institute (CRI) Report 29. Files of wind stress over the oceans are prepared on a 5 X 5 degree latitude/longitude grid and are described in OSU CRI Report 26. The data include surface wind speed, sea level pressure, sea surface temperature (SST), sea/air temperature difference, air temperature, specific humidity, sea/air specific humidity difference, cloudiness, available solar radiation, net upward longwave radiation, net downward radiative flux, latent heat flux, sensible heat flux, and net downward heat flux.
Information about obtaining this dataset is available via World Wide Web from the NCAR Home Page. Link to: https://rda.ucar.edu/datasets/ds209.0/
Additional information regarding NCAR's research data collection is available on-line. Link to: https://rda.ucar.edu/
The data is also available from the LDEO/IRI Data Climate Library at: http://iridl.ldeo.columbia.edu/SOURCES/.OSUSFC/
The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Vegetation Indices data set contains rasters with the following calculations: time series average and time series monthly average for the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Red Reflectance Band 1, Near-Infrared Reflectance Band 2, Blue Reflectance Band 3, and Mid-Infrared Reflectance Band 7. These Africa continent-wide calculations for vegetation indices and surface reflectances use data from the National Aeronautics and Space Administration (NASA) MODIS MOD13Q1 product. The rasters have a 16-day temporal resolution, a spatial resolution of 250 meters, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu.
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This is a netcdf dataset containing a basin mask for distinguishing major ocean basins (Atlantic, Pacific, etc.). It has been simplified to for use with the TBI experiments coordinated by the CLIVAR Research Focus on Tropical Basin Interaction (https://www.clivar.org/research-foci/basin-interaction). The original data can be found at https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Masks/.basin/index.html?Set-Language=en
The Africa Soil Information Service (AfSIS) Climate Collection's Tropical Rainfall Measurement Mission (TRMM) data set contains rasters with the following calculations: time series average, time series Modified Fournier index (MFI), time series average number of rainy days, annual averages, annual MFI, and annual average number of rainy days, for precipitation. These Africa continent-wide calculations use the TRMM observations obtained by the National Aeronautics and Space Administration (NASA). The rasters have a daily temporal resolution, a spatial resolution of 30 kilometers, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu.
The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).
Summary We configure two existing resource management tools, originally configured to use observed (historical) ocean temperatures, to a forecasting system and conduct a retrospective forecast to test their skill. We first conducted a retrospective forecast using global forecasts (73 ensemble members) across the full historically available period (1981-2020) – termed the ‘Global’ model. Global forecasts of monthly sea surface temperature were obtained from the North American Multimodel Ensemble (NMME; Table S1; https://www.cpc.ncep.noaa.gov/products/NMME/). We then compared the performance of three forecast configurations: First, we used global forecasts (73 ensemble members) across a reduced historical period (1981-2010) - termed the ‘Global Full Ensemble’. Second, we used forecasts regionally downscaled (3 ensemble members) to the CCE for the same reduced historical period (1981-2010) - termed the ‘Downscaled Ensemble’. Third, we used a reduced subset of the global forecasts (3 ensem...
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A gridded annual runoff dataset from 1982 to 2016 has been developed using PISCO dataset (http://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/): streamflow, precipitation, and potential evapotranspiration (from temperature using Hargreaves-Samani). A probabilistic Budyko approach was used to estimate runoff using 14 spatial clusters where an empirical distribution of omega (Fu's parameter) was fitted to the data. In this sense, a maximum, mean, and minimum value is given for runoff in each grid.
[FROM: http://iridl.ldeo.columbia.edu/maproom/Fire/Regional/Amazonia/SST_Fire_Forecast.html] These graphs include July-September fire season anomaly hindcasts and forecasts in the Western Amazon. The incidence is on a standardized scale and is based on the north tropical Atlantic (NTA) sea surface temperature (SST) seasonal forecast issued in April, May and June. Positive values indicate an expected active fire season and negative values stand for a mild fire season. Lead 1 stands for the first trimester SST forecast and Lead-2 for the the second trimester SST forecast. For example, March Lead-1 forecast uses April-June SST forecast to calculate the NTA index and predict the following JAS fire season. March Lead-2 uses May-July SST forecast to calculate the NTA and predict JAS fire season and so on. As we advance in the seasons, the more accurate the forecast is expected to be. Use the drop-down menus at the top of the page to select the map field (Forecast or Observed) and the forecast issue month to display.
The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Surface Temperature data set contains rasters with the following calculations: time series average and time series monthly averages for day and night. These Africa continent-wide calculations use observations from the National Aeronautics and Space Administration (NASA) MODIS MYD11A2 product. The rasters have an 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu.
The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
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 detailsclimate_proc
: Processed climate data files upon which the analysis is based, used by main and supplemental figure codeaux_data
: Certain auxiliary data files (fonts, critical values) and intermediate files for long code processes. Created and used by the replication code. 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.
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.
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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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)
wmteMasks.tar | The WMTE masks used in the paper, derived from ERA5 daily average 700 hPa moisture transport |
precipMasks.tar | The precipitation masks identified in ERA5 daily precipitation totals using the method of Konstali et al. (2024), implemented in the dynlib package (Spensberger (2021)) |
attributedPrecipMasks.tar | Masks showing the precipitation polygons that overlapped with WMTEs |
utils.tar | Utility programs used for data processing and for generating the WMTE masks |
figures.tar | Data and code for generating the figures in "Drivers and impacts of westerly moisture transport events in East Africa" by Robert Peal and Emily Collier (submitted to Weather and Climate Dynamics) |
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.
wmte_detector --->detectorData --->event_detector3.py --->run_westerly_detector3.sh --->detector2d.py |
The WMTE detector code is in this folder detectorData is a folder with the specs of the filters used to generate the masks User options should be specified in the bash script. detector2d provides utility functions for the detector |
tctools2.py | Module with utility functions used extensively in the project. IMPORTANT: Most of the files will require tctools2.py, cartopy_local.py, and pytime.py to be in the path in order to run |
cartopy_local.py cartopyData |
Module for making cartopy plots using local shapefiles so it can be run without internet connection Folder containing some cartopy shapefile data for plots |
pytime.py | Module with some clock functions |
calculate_moist_adv.py gen_daily_moisture_transport.sh | Python code for calculating daily average moisture transport from files with hourly wind and specific humidity, and a bash script running the python code |
nctools | Folder containing some useful functions for calculating daily climatologies and anomalies of netcdf files |
run_swio_state swio_state.py swio_state5.csv | swio_state.py generates the csv file with information about the MJO phase and TCs present in SWIO on each day |
MJO.csv | Australian Bureau of meteorology (BOM) MJO indices |
ibtracs.since1980.list.v04r00.csv |
International Best Track Archive for Climate Stewardship (IBTrACS) Tropical cyclone locations from Knapp et. al., 2010 |
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.
detectorOverview | fig. 1 code repo. Plotting detection on an example day |
persistence_stats | fig. 2 code repo. Calculating and plotting basic statistics of WMTEs |
moistureComposite |
fig. 3 code repo. Plotting the composite moisture transport with and without WMTEs Needs the timeseries of days with a WMTE crossing the EEA line generated in tcWesterlyDays |
mjoWesterlyDays | fig. 4 code repo. Calculating and plotting the number of WMTE days in each season in each MJO phase |
tcWesterlyDays | fig. 5 code repo. Calculating the number of days with WMTE crossing the EEA line and plotting risk ratio to TCs. Also contains sensitivity analysis of the WMTE algorithm |
precipDays | fig. 6 code repo. Precipitation aggregation |
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.
The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Vegetation Indices data set contains rasters with the following calculations: time series average and time series monthly average for the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Red Reflectance Band 1, Near-Infrared Reflectance Band 2, Blue Reflectance Band 3, and Mid-Infrared Reflectance Band 7. These Africa continent-wide calculations for vegetation indices and surface reflectances use data from the National Aeronautics and Space Administration (NASA) MODIS MOD13Q1 product. The rasters have a 16-day temporal resolution, a spatial resolution of 250 meters, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu.
The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).
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Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
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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