Abstract:
Precipitation and near-surface temperature data from the Coupled Model Intercomparison Project phase 5 (CMIP5 models) are statistically downscaled to create these gridded datasets over the Rio Santa River Basin (in the Cordillera Blanca; d02) and the Vilcanota-Urubamba region (d03) at 4 km horizontal resolution, from 2019-2100. The bias-corrected WRF data found in the related dataset are used as the observational truth for the historical period 1980-2018, and the data are downscaled using an empirical quantile mapping technique. Two representative concentration pathways (RCP) have been downscaled, RCP 4.5 and RCP 8.5, from 30 CMIP5 models. The daily total precipitation and daily minimum and maximum temperature at 2 m are downscaled, and the daily average and monthly average temperatures are calculated using the hourly temperature (not archived due to space constraints). The potential evapotranspiration is estimated from the downscaled precipitation and temperature data, using the Hargreaves equation. These data were corrected as part of the PEGASUS (Producing EnerGy and preventing hAzards from SUrface water Storage in Peru) and Peru GROWS (Peruvian Glacier Retreat and its Impact on Water Security) projects. The datasets were created to assess future climate in the Peruvian Andes, as a basis to determine future climate in the region, and as an input for glaciological and hydrological models. The data were created on the JASMIN supercomputer.
The creation of this data was conducted under the Peru GROWS and PEGASUS projects, which were both funded by NERC (grants NE/S013296/1 and NE/S013318/1, respectively) and CONCYTEC through the Newton-Paulet Fund. The Peruvian part of the Peru GROWS project was conducted within the framework of the call E031-2018-01-NERC "Glacier Research Circles", through its executing unit FONDECYT (Contract No. 08-2019-FONDECYT).
Keywords:
Andes, CMIP, Peru, downscaling, future projections
Potter, E., Fyffe, C., Orr, A., Quincey, D., Ross, A., Rangecroft, S., Medina, K., Burns, H., Llacza, A., Jacome, G., Hellstrom, R., Castro, J., Cochachin, A., Montoya, N., Loarte, E., & Pellicciotti, F. (2023). Precipitation and temperature data from statistically downscaled CMIP5 models, Cordillera Blanca and Vilcanota-Urubamba regions, Peru, from 2019 to 2100 (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/67ceb7c8-218c-46e1-9927-cfef2dd95526
| Access Constraints: | No restrictions apply. |
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| Use Constraints: | Data supplied under Open Government Licence v3.0 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. |
| Creation Date: | 2023-04-11 |
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| Dataset Progress: | Complete |
| Dataset Language: | English |
| ISO Topic Categories: |
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| Parameters: |
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| Personnel: | |
| Name | UK Polar Data Centre |
| Role(s) | Metadata Author |
| Organisation | British Antarctic Survey |
| Name | Emily R Potter |
| Role(s) | Investigator, Technical Contact |
| Organisation | University of Leeds |
| Name | Catriona Fyffe |
| Role(s) | Investigator |
| Organisation | Northumbria University |
| Name | Andrew Orr |
| Role(s) | Investigator |
| Organisation | British Antarctic Survey |
| Name | Duncan Quincey |
| Role(s) | Investigator |
| Organisation | University of Leeds |
| Name | Andrew N Ross |
| Role(s) | Investigator |
| Organisation | University of Leeds |
| Name | Sally Rangecroft |
| Role(s) | Investigator |
| Organisation | University of Plymouth |
| Name | Katy Medina |
| Role(s) | Investigator |
| Organisation | Instituto Nacional de Investigacion en Glaciares y Ecosistemas de Montana |
| Name | Helen Burns |
| Role(s) | Investigator |
| Organisation | University of Leeds |
| Name | Alan Llacza |
| Role(s) | Investigator |
| Organisation | Servicio Nacional de Meteorologia e Hidrologia del Peru |
| Name | Gerardo Jacome |
| Role(s) | Investigator |
| Organisation | Servicio Nacional de Meteorologia e Hidrologia del Peru |
| Name | Robert Hellstrom |
| Role(s) | Investigator |
| Organisation | Bridgewater State University |
| Name | Joshua Castro |
| Role(s) | Investigator |
| Organisation | Universidad Nacional de San Antonio Abad del Cusco |
| Name | Alejo Cochachin |
| Role(s) | Investigator |
| Organisation | Autoridad Nacional del Agua |
| Name | Nilton Montoya |
| Role(s) | Investigator |
| Organisation | Universidad Nacional de San Antonio Abad del Cusco |
| Name | Edwin Loarte |
| Role(s) | Investigator |
| Organisation | Instituto Nacional de Investigacion en Glaciares y Ecosistemas de Montana |
| Name | Francesca Pellicciotti |
| Role(s) | Investigator |
| Organisation | Northumbria University |
| Parent Dataset: | N/A |
| Reference: | Emily R. Potter, Catriona Fyffe, Andrew Orr, Duncan Quincey, Andrew N. Ross, Sally Rangecroft, Katy Medina, Helen Burns, Alan Llacza, Gerardo Jacome, Robert A. Hellstrom, Joshua Castro, Alejo Cochachin, Nilton Montaya, Edwin Loarte, and Francesca Pellicciotti. (2023) Future projections of extreme precipitation and droughts in the Peruvian Andes. Under review at NPJ climate and atmospheric sciences. Fyffe, C. L., Potter, E., Fugger, S., Orr, A., Fatichi, S., Loarte, E., et al. (2021). The energy and mass balance of Peruvian glaciers. Journal of Geophysical Research: Atmospheres, 126, e2021JD034911. https://doi.org/10.1029/2021JD034911. Cannon, A. J., Sobie, S. R., and Murdock, T. Q. (2015). Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28, 6938-6959, https://doi.org/10.1175/JCLI-D-14-00754.1. |
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| Quality: | There are no known quality issues with the data. | |
| Lineage: | First, the CMIP5 models are regridded to the horizontal resolution of the WRF grid using bilinear interpolation. The statistical downscaling follows the empirical quantile mapping technique described in Cannon et al. (2015). This method preserves the large-scale trends from the CMIP5 models at each quantile (i.e. the trends in both the median and the extremes are preserved), while adjusting the number of wet days and the magnitude of precipitation and temperature based on the values from 1980-2018. The hourly temperature is calculated by scaling the raw WRF output to the bias-corrected minimum and maximum daily temperatures. Full details of the methodology can be found in Potter et al., (2023). | |
| Temporal Coverage: | |
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| Start Date | 2019-01-01 |
| End Date | 2100-12-31 |
| Spatial Coverage: | |
| Latitude | |
| Southernmost | -10.8 |
| Northernmost | -7.5 |
| Longitude | |
| Westernmost | -79.5 |
| Easternmost | -76.3 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Latitude | |
| Southernmost | -15 |
| Northernmost | -12.4 |
| Longitude | |
| Westernmost | -73.9 |
| Easternmost | -70.2 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Data Resolution: | |
| Latitude Resolution | N/A |
| Longitude Resolution | N/A |
| Horizontal Resolution Range | 1 km - < 10 km or approximately .01 degree - < .09 degree |
| Vertical Resolution | N/A |
| Vertical Resolution Range | N/A |
| Temporal Resolution | N/A |
| Temporal Resolution Range | N/A |
| Location: | |
| Location | Peru |
| Detailed Location | Cordillera Blanca including Rio Santa River Basin |
| Location | Peru |
| Detailed Location | Vilcanota-Urubamba River Basin |
| Data Collection: | See the reference materials for information on the CMIP5 models. |
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| Distribution: | |
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| Distribution Media | Online Internet (HTTP) |
| Distribution Size | 650 GB |
| Distribution Format | netCDF |
| Fees | N/A |
| Data Storage: | The directories Rio_Santa_d02 and Vilcanota_Urubamba_d03 contain the domain 2 and domain 3 data, respectively. Each directory contains subdirectories for RCP 4.5 (rcp45) and RCP 8.5 (rcp85), and within these are subdirectories for potential evapotranspiration (potential_evapotranspiration), daily precipitation (pr), minimum and maximum daily temperature at 2 m (tasmin and tasmax, respectively), average daily and monthly temperature at 2 m (temp_daily_av, temp_monthly_av, respectively). Note that the Time coordinate for each dataset relates to the averaging, for example 2019-01-31T00:00:00.000000000 in the directory temp_monthly_av refers to the monthly-averaged temperature for January 2019. Every individual file name contains the model name of the original CMIP5 data used for downscaling. |