International Journal of Climatology

The yield of corn is strongly affected by climatic conditions during the growing season. In the central region of Argentina, this crop is mainly managed under rainfed conditions. Hence, in most years, it is subjected to drought at some period during the growing season. El Niño Southern Oscillation (ENSO) is known to influence rainfall in this region, mainly during the warm semester, hence affecting summer crops yields. This study assessed the relationship between ENSO [analysed through the June–July–August Oceanic Niño Index (JJA-ONI)] and corn yields in the provinces of Buenos Aires, Entre Ríos and Santa Fe, which is the main corn-growing area in Argentina. This was performed for two contrasting periods regarding technology applied in the agricultural sector: 1972–1991 and 1992–2012. Remarkable increases in corn yield between periods were found for the entire region. Except for the province of Entre Ríos, we found statistically significant differences between periods in the trends of corn yield by performing the Chow test. Significant correlations (P < 0.01) between the JJA-ONI and corn yield were found in many counties of Buenos Aires, Entre Ríos and Santa Fe provinces. The correlation was higher in the second period for most counties. We consider that two hypotheses could explain this correlation increase: (1) in previous decades the best growing seasons (from a climatic point of view) were not fully exploited because of a low use of inputs and technology; and (2) the correlation between the ONI and rainfall could have increased in the last decades. We confirmed the latter hypothesis with rainfall data from conventional meteorological stations of 11 locations of the region under analysis. The JJA-ONI assessed in this research is available before farmers make their most relevant corn management decisions (fertilizer dose, sowing date, density, etc.), thus making this index highly valuable.

The dataset is a series of excel spreadsheets in .csv format that have difference characteristics of cyclones/ low pressure systems for Australia. Data has also been manipulated in MATLAB, ARCGIS and Abode Illustrator. Therefore, there is a variety of spatial files, code and figures. There is also a lot of raw climate data that was sourced from external websites. This includes sub-daily rainfall, daily rainfall, climate indices, wind and temperature as well as various TC databases.

The dataset is a series of excel spreadsheets in .csv format that have difference characteristics of cyclones/ low pressure systems for Australia. Data has also been manipulated in MATLAB, ARCGIS and Abode Illustrator. Therefore, there is a variety of spatial files, code and figures. There is also a lot of raw climate data that was sourced from external websites. This includes sub-daily rainfall, daily rainfall, climate indices, wind and temperature as well as various TC databases.

Max Temp of Warmest Month (Bio05) raster for southeastern Australia. Input file used to model the species distributions of 40 reptile species in Victoria, Australia. File obtained from the WorldClim (https://worldclim.org/) version 2 database, at a spatial resolution of ~1 km2 Cell size - 250 x 250 Original file has been clipped to southeastern Australia.Methods used to generate the input files and perform modelling are outlined in the methods section of the abovementioned publication. Citation - Fick, S.E. and Hijmans, R.J. (2017), WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol, 37: 4302-4315. https://doi.org/10.1002/joc.5086

A script that produces the monthly surfaces for each one of the covariates required to create the bioclimatic dataset: maximum and minimum temperatures, as well as precipitation. These surfaces are created applying several interpolation techniques, based on climatic information measured on-field (Supplementary Material A; DOI: 10.4121/14932947) and surfaces from GMTED2010 (digital elevation model), TRMM 3B43 v7 and NOAA (that must be downloaded from the respective sources). It also uses elevation and latitudes in the case of temperatures. Outputs are generated in GeoTiff format and 30 arc-sec resolution.

A script that produces the monthly surfaces for each one of the covariates required to create the bioclimatic dataset: maximum and minimum temperatures, as well as precipitation. These surfaces are created applying several interpolation techniques, based on climatic information measured on-field (Supplementary Material A; DOI: 10.4121/14932947) and surfaces from GMTED2010 (digital elevation model), TRMM 3B43 v7 and NOAA (that must be downloaded from the respective sources). It also uses elevation and latitudes in the case of temperatures. Outputs are generated in GeoTiff format and 30 arc-sec resolution.

Spatially-oriented graphic comparisons of the monthly precipitations (Ppt), as well as maximum and minimum temperatures (Tmax and Tmin, respectively) provided by the meteorological weather system managed by the Brazilian National Institute of Meteorology (INMET) relative to the information provided by several bioclimatic databases currently available.

Database containing the climatic information used to create BrazilClim: the bioclimatic variables for the continental megadiverse Brazil. This information was measured by meteorological gauges from the Brazilian official meteorological network, with at least 10 complete years of data within the period 2000-2019, managed by the National Institute of Meteorology (INMET). (CMG) Refers to the conventional, and (AMG) for automatic meteorological gauges,

Graphic and statistic comparisons of the monthly precipitations (Ppt), as well as maximum and minimum temperatures (Tmax and Tmin, respectively) provided by the meteorological weather system managed by the Brazilian National Institute of Meteorology (INMET) relative to the information provided by several bioclimatic databases currently available.

In fall 2019, a new set of climate change scenarios has been released for Switzerland, the CH2018 dataset (www.climate-scenarios.ch). The data are provided at daily resolution. We produced from the CH2018 dataset a new set of climate change scenarios temporally downscaled at hourly resolution. In addition, we extended this dataset integrating the meteorological stations from the Inter-Cantonal Measurement and Information System (IMIS) network, an alpine network of automatic meteorological stations operated by the WSL Institute for Snow and Avalanche Research SLF. The extension to the IMIS network is obtained using a Quantile Mapping approach in order to perform a spatial transfer of the CH2018 scenarios from the location of the MeteoSwiss stations to the location of the IMIS stations. The temporal downscaling is performed using an enhanced Delta-Change approach. This approach is based on objective criteria for assessing the quality of the determined delta and downscaled time series. In addition, this method also fixes a flaw of common quantile mapping methods (such as used in the CH2018 dataset for spatial downscaling) related to the decrease of correlation between different variables. The idea behind the delta change approach is to take the main seasonal signal (and mean) from climate change scenarios at daily resolution and to map it to a historical time series at hourly resolution in order to modify the historical time series. The obtained time series exhibit the same seasonal signal as the original climate change time series, while it keeps the sub-daily cycle from the historical time series. The applied methods (Quantile Mapping and Delta-Change) have limitations in correctly representing statistically extreme events and changes in the frequency of discontinuous events such as precipitation. In addition, the sub-daily cycle in the data is inherited from the historical time series, so there is no information of the climate change signal in this sub-daily cycle. A careful reading of the paper accompanying the dataset is necessary to understand the limitations and scope of application of this new dataset. This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).