Maps of past and simulated future microclimate in forests and agroforests in southwestern Ethiopia, 1979⁠ – ⁠2100

Francesco Zignol, Erik Kjellström, Kristoffer Hylander, Biruk Ayalew, Beyene Zewdie, Alejandro Rodríguez-Gijón, Ayco J. M. Tack

The dataset contains high spatial resolution maps showing monthly and annual averages of daily maximum temperature, daily minimum temperature, and daily mean relative humidity for the Jimma zone in southwestern Ethiopia. The maps were created by using statistical downscaling applied to widely used reanalysis data for the period 1979⁠ – ⁠2020 and climate projections from a combination of different global climate models (GCMs) and scenarios (SSPs; Shared Socioeconomic Pathways) for the period 2021⁠ – ⁠2100.

The aim of creating the dataset was to investigate how climate change affects the understory microclimate in forests and agroforests, which can help develop sustainable and climate-smart farms and landscape-level adaptations within coffee agroforestry systems. Results obtained from analyses of the maps are discussed in a published case study of Arabica coffee in its native range.

The dataset includes 42,198 maps in GeoTIFF format, covering an area of about 67×50 km at a spatial resolution of 30×30 m. The full set of maps, as well as specific subsets of the maps, can be downloaded here. Moreover, the map content is shown in an interactive graphical presentation where the user can select a desired climate variable, scenario, and model, and see how the climate changes with time.

Scientific highlight

In the article by Zignol et al. (2023), based on this data set, we created maps of the future microclimate, and we concluded that coffee farming might have to relocate to higher elevations due to increasing temperatures. In these maps you can, for example, see that the temperature increase over time is larger at lower elevations (northeastern part in the maps) than at higher elevations (central western part in the maps), as well as along roads and forest edges.

Other patterns you can see are that changes in maximum temperature are larger than changes in minimum temperature, and that the increase in temperature seems largest in the period April to June. Analyzing and comparing spatiotemporal patterns in projected maps can help us understand that climate change is not uniform, and can in this particular landscape help to design different strategies for climate adaptation in different parts of the landscape.

Features on this site

Download the data

You can download the full set of maps with climate predictions, or download the maps related to a specific climate model and emission scenario. What you download are GeoTIFF files that are useful for the visualization of a certain climate variable under a certain scenario, or be used in GIS-environments for other future studies.

View the data

To easily see the patterns discussed by Zignol et al. (2023) as well as other patterns you can, in the interactive data visualization below, select your favorite model, the emission scenario, and the climate variable you are most interested in. Then you can use an image slider to see how that variable changes with time and in space over the predicted period. What you see in the viewer are JPEG image files that have been created from the GeoTIFF files.

Download data

The dataset is arranged in a file tree structure with three main directories, one for each climate variable.

Under each main directory, all available GeoTIFF files for each combination of climate model and climate scenario are provided, compressed into one single zip file per combination.

Explanations of file content and file naming rules are given in the section Data description, together with abbreviations for models and scenarios.

Select a subset

You can download individual zip files by selecting in the file list below.

Select all data

Alternatively, you can download all data files by clicking on the link below the file list.

Because of the large data volume (156 GiB, 42 files), downloading all files can take can take long time and requires sufficent disk space on your system.

Data files

Click on the categories below to view the files. Click on a file link to download a selected file. Or, click on the link below the file list to download all data files.

Data visualization

Make your selection

Here, you can select and view a map with the climate prediction for a given year. For this, select your favorite model, an emission scenario or the past, and the climate variable you are most interested in. Also, select either a specific month for averaging or annual averaging. The default choice is annual averaging.

Image slider

Use the slider to move forwards or backwards in time.

Fixed month checkbox

• When the checkbox is ticked on, the slider only uses maps for the selected month.
• When the checkbox is ticked off, the slider uses maps for all months.

Abbreviations

Abbreviations for models and scenarios are explained in the Data description section.

Possible combinations

Please, note that some combinations of model, scenario and timepoint are not possible. This is for the following reasons:

• The ERA5 reanalysis data is only available for the period 1979⁠ – ⁠2020.
• The climate scenario data is only available for the period 2021⁠ – ⁠2100.
• Only one model (CanESM5) has data for scenario SSP1-1.9.

An attempt to choose an impossible combination will result in an error message being shown instead of a map.

Parameter

Maximum temperature Minimum temperature Relative humidity

Model

ERA5 CanESM5 MPI-ESM1.2-HR NorESM2

Scenario

Past SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5

Year

1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100

Month

Annual January February March April May June July August September October November December

Fixed month

Work in progress

Citation

Francesco Zignol, Erik Kjellström, Kristoffer Hylander, Biruk Ayalew, Beyene Zewdie, Alejandro Rodríguez-Gijón, Ayco J. M. Tack (2024) Maps of past and simulated future microclimate in forests and agroforests in southwestern Ethiopia, 1979⁠ – ⁠2100. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/zignol-2024-sw-ethiopia-microclimate-1

References

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.-N., 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049. https://doi.org/10.1002/qj.3803

Müller, W.A., Jungclaus, J.H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Tian, F., Marotzke, J., 2018. A Higher-Resolution version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR). J. Adv. Model. Earth Syst. 10, 1383–1413. https://doi.org/10.1029/2017MS001217

Riahi, K., van Vuuren, D.P., Kriegler, E., Edmonds, J., O’Neill, B.C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J.C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L.A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J.C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., Tavoni, M., 2017. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009

Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L.S., Debernard, J.B., Gupta, A.K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K.S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I.H.H., Landgren, O., Liakka, J., Moseid, K.O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., Schulz, M., 2020. Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations. Geosci. Model Dev. 13, 6165–6200. https://doi.org/10.5194/gmd-13-6165-2020

Swart, N.C., Cole, J.N.S., Kharin, V.V., Lazare, M., Scinocca, J.F., Gillett, N.P., Anstey, J., Arora, V., Christian, J.R., Hanna, S., Jiao, Y., Lee, W.G., Majaess, F., Saenko, O.A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., Winter, B., 2019. The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci. Model Dev. 12, 4823–4873. https://doi.org/10.5194/gmd-12-4823-2019

Zignol, F., Kjellström, E., Hylander, K., Ayalew, B., Zewdie, B., Rodríguez-Gijón, A., Tack, A.J.M., 2023. The understory microclimate in agroforestry now and in the future – a case study of Arabica coffee in its native range. Agric. For. Meteorol. 340, 109586. https://doi.org/10.1016/j.agrformet.2023.109586

Data description

Data files

The dataset includes 42,198 maps in GeoTIFF format, together amounting to ca. 180 GB of data (uncompressed).

Geographical information

The maps cover an area of about 67×50 km in southwestern Ethiopia (Jimma zone) and have a spatial resolution of 30×30 m. The projected coordinate system for all maps is WGS 1984 UTM zone 37N.

Our study focuses only on the forest-covered part (44% of the entire study area), which encompasses 1,645,794 pixels (1481 km²). Thus, climate variables are only estimated for these pixels.

Climate variables

We estimated monthly (12) and annual (1) averages of three climate variables for the period 1979⁠ – ⁠2100:

• daily maximum temperature (°C)
• daily minimum temperature (°C)
• daily mean relative humidity (%)

The data for the three climate variables is always stored to two decimal places (e.g., 28.45°C for maximum temperature, 19.72°C for minimum temperature, 83.67% for relative humidity).

Past and future

• For past estimates (1979⁠ – ⁠2020), we used the fifth generation ECMWF reanalysis for the global climate and weather for the past decades (ERA5; Hershbach et al. 2020).

• For future projections (2021– ⁠2100) we used outputs from three different global climate models (GCMs) under different emission scenarios, i.e., predictions of the rate at which we continue to release greenhouse gases into the atmosphere.

Climate models

We used output data from three CMIP6 GCMs spanning a range of equilibrium climate sensitivity (ECS):

• The Canadian Earth System Model version 5 (CanESM5; Swart et al. 2019) with relatively high ECS.
• The Higher Resolution version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR; Müller et al. 2018) with medium ECS.
• The Norwegian Earth System Model version 2 (NorESM2; Seland et al. 2020) with relatively low ECS.

Climate scenarios

We considered multiple Shared Socioeconomic Pathways (SSPs; Riahi et al. 2017), ranging from a more sustainable, very low emission scenario (SSP1-1.9; CO₂ emissions cut to net zero around 2050) to a fossil fuel oriented, very high emission scenario (SSP5-8.5; CO₂ emissions close to triple by 2075). Note that SSP1-1.9 is available only for CanESM5.

Data file name rules

The dataset is arranged in a file tree structure with three main directories, one for each climate variable.

Under each main directory, all available GeoTIFF files for each combination of climate model and climate scenario are provided, compressed into one single zip file per combination. The following abbreviations are used in zip file names:

• For climate variables
  • Tmx maximum temperature
  • Tmn minimum temperature
  • Rhm relative humidity
• For models
  • Can5 CanESM5
  • ERA5 ERA5
  • mpi1 MPI-ESM1.2-HR
  • Nor2 NorESM2
• For scenarios
  • 119 SSP1-1.9
  • 126 SSP1-2.6
  • 245 SSP2-4.5
  • 370 SSP3-7.0
  • 585 SSP5-8.5

The individual tif files have names that also identify the year (1979 to 2100) and time averaging period (00 to 12; where 00 denotes annual average and 01 to 12 the monthly averages).

Comments

Our motivation

We have published a scientific paper on the impact of climate change on the microclimate in forests and agroforests in southwestern Ethiopia (Zignol et al. 2023). The title of the paper was The understory microclimate in agroforestry now and in the future – a case study of Arabica coffee in its native range.

The aim of this paper was to investigate how climate change affects the understory microclimate in forests and agroforests, which can help develop sustainable and climate-smart farms and landscape-level adaptations within coffee agroforestry systems.

While the paper was a major scientific advance, we could only show a small subset of the data and in poor resolution. To ensure that the full data set is published and available to the scientific community and local stakeholders in Ethiopia, we provide all maps for download here. Moreover, we also provide an interactive visualization of microclimate changes that is informative in itself and complement the paper in illustrating the projected changes across this landscape.

GeoTIFF files

The GeoTIFF (.tif) files are useful for the visualization of a certain climate variable under a certain scenario and can be used in GIS-environments for other future studies.

JPEG image files

The associated interactive graphical presentation of the map content shows JPEG image files, with one unique .jpg file corresponding to each unique .tif file. All .jpg files for one specific climate variable have the same colour scheme, which is explained below the graphical presentation. Individual JPEG image files can be downloaded from the graphical presentation. Select a desired image and use your browser's standard way to save an image file.