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ClimateDT Description

1. Why using ClimateDT?

Interpolated climate data have become an essential tool for researchers across many disciplines. Long-term climate baseline data with appropriate spatial and temporal resolution are required, though such data are not readily available in consistent formats and with an appropriate spatial resolution. This is the reason why downscaling techniques have been developed.

2. Rationale

ClimateDT can be used to downscale a large variety of raw climatic data and indices between 1901 and 2098 from a downscaled version of CRU-TS (historical climate) and UKCP18 (future climate) surfaces [1][2]. Input data are spatial coordinates and elevation which are used for the scale-free statistical downscaling procedure.

3. Scientific Background

3.1 Historical Climatic Data Sets – CRU time series

CRU-TS [2] is probably the most referenced and well-known global database for raw climatic data (temperature and precipitation). The dataset is regularly updated and the most recent version is used here (1901-current). The ClimateDT employs a modified version of this dataset consisting in a downscaled version to 5km in Lambert Equal Area Projection (EPSG 3035) obtained from the original ~50km grid (EPSG 4326). The original grid were downscaled using the delta method [3][4] often used in the literature. This spatial resolution has been selected in order to improve the original data and allow for a further scale-free (point-by-point) downscaling.

3.2 Future Climatic Data Sets – UKCP18 projections

UKCP18 is a large set of climatic surfaces and uses cutting-edge climate science to provide updated observations and climate change projections out to 2100 in the UK and globally. The project builds upon UKCP09 to provide the most up-to-date assessment of how the climate of the UK may change over the 21st century. In ClimateDT this database has been mounted on CRU-TS using an “anomalies” approach where the climate change forcing (anomaly) was calculated from a common normal period between CRU-TS and UKCP18 (i.e. 1980-2019 normals). Then anomalies were added on CRU-TS to remove the intrinsic difference between the two datasets and to generate a unique and robust time series between 1901 an 2098.

3.3 Downscaling procedure and algorithms

The core of the system is the spatial interpolation of climatic data with a dynamic lapse rate adjustment to be applied according to the difference in elevation. This method is currently implemented in some standalone softwares such as ClimateWNA for north America [5] and ClimateEU for Europe [6]. The main step of the dynamic lapse rate adjustment is an evaluation of the relationships between variability of climatic parameters (i.e. temperature or precipitation) and elevation in the surrounding of the location where climatic data needs to be downscaled to. To achieve this, after the geographic interpolation (i.e. DEM-independent), climatic and elevation data are firstly extracted from the 8 pixels surrounding the pixel where the spatial location is located plus the pixel itself (9 in total). All possible differences between the 9 climate variables (∆c) are calculated, obtaining a set of 36 unique pairs of differences. The same procedure is replicated for elevation (∆e), then a simple linear regression is built, based on the 36 pairs to reflect the local relationship between ∆c and ∆e. The slope of the regression line m represents the local (i.e. dynamic) lapse rate for the point of interest (i.e. the cell where the point of interest is located):

(1)
∆c = i + m ∆e

Finally, the elevation adjustment to be applied to the climactic value interpolated before, is predicted by this linear model using the difference between actual elevation of the location and the spatially-interpolated elevation of the point of interest:

(2)
Climate = SPTc + i + m (SPTe - Elev)

where SPTc is the spatial interpolation of climatic variable, i and m are the intercept and the slope (lapse rate) of the regression (1) respectively, SPTe is the elevation the system calculates from the 5 km Digital Elevation Model coupled with UKCP18 surfaces and Elev is the elevation provided by the user in the submitted file.

The whole calculation is performed in R environment and implements additional R packages which are still under development.

4. Description of the output

The system currently outputs three monthly variables (Minimum temperature, Maximum temperature and Precipitation) plus 39 climatic variables and indices (annual or monthly) over the whole time period (1901-2098). Among these parameters, the raw climatic variables with monthly resolution, the 19 bioclimatic parameters from the WorldClim portal [7], SPEI and SPI indices [8] and other drought and frost indices also available for other regions of the globe [9]. The full list of output variables is available here.

5. System performance

ClimateDT currently employs 3 CPUs on a virtual machine hosted at CNR-IBBR (Florence, Italy). The system currently needs approximately 15 minutes to process 1,000 locations for the full time coverage (1901-2098). The calculation is currently limited to 1,000 locations. However, more than one submission is allowed, each including a maximum of 1,000 locations.

6. Future Releases

The whole process is open-source and always under development. New indices, interpolation methods (IDW), base datasets and tools are planned to be added to the systems soon. Any feedback from users is welcome. New requests can be sent to the staff in order to implement new indices currently missing in the output files.

7. References

  1. Lowe JA, Bernie D, Bett PE, Bricheno LM, Brown SC, Calvert D, Clark R, Eagle K, Edwards T, Fosser G, Maisey P, McInnes RN, Mcsweeney C, Yamazaki K, Belcher S (2019). UKCP 18 Science Overview Report (version 2.0). November 2018 (Updated March 2019). Met Office Hadley Centre, Exeter, UK.
    URL: https://www.semanticscholar.org/paper/UKCP-18
  2. Harris I, Osborn TJ, Jones P, Lister D (2020.) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7: 109.
    doi: https://doi.org/10.1038/s41597-020-0453-3
  3. Moreno A, Hasenauer H (2016). Spatial downscaling of European climate data. International Journal of Climatology 36: 1444–1458.
    doi: https://doi.org/10.1002/joc.4436
  4. Fréjaville T, Benito Garzón M (2018). The EuMedClim Database: Yearly Climate Data (1901–2014) of 1 km Resolution Grids for Europe and the Mediterranean Basin. Frontiers in Ecology and Evolution 6: 1–5.
    doi: https://doi.org/10.3389/fevo.2018.00031
  5. Wang T, Hamann A, Spittlehouse DL, Murdock TQ (2012). ClimateWNA-high-resolution spatial climate data for western North America. Journal of Applied Meteorology and Climatology 51: 16–29.
    doi: https://doi.org/10.1175/JAMC-D-11-043.1
  6. Marchi M, Castellanos-Acuña D, Hamann A, Wang T, Ray D, Menzel A (2020). ClimateEU, scale-free climate normals, historical time series, and future projections for Europe. Scientific Data 7: 428.
    doi: https://doi.org/10.1038/s41597-020-00763-0
  7. Hijmans RJ, Cameron SE, Parra JL, Jones G, Jarvis A (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965–1978.
    doi: https://doi.org/10.1002/joc.1276
  8. Maca P, Pech P (2016). Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks. Computational Intelligence and Neuroscience 2016: 1–17.
    URL: http://downloads.hindawi.com/journals/cin/2016/3868519.pdf
  9. Wang T, Hamann A, Spittlehouse D, Carroll C (2016). Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One 11: e0156720.
    doi: https://doi.org/10.1371/journal.pone.0156720

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