Statistical Downscaling of Temperature and Precipitation for Mount Makulu, Zambia Using the Statistical Downscaling Portal for 2010-2099 | Chapter 3 | Emerging Issues in Environment, Geography and Earth Science Vol. 8

 Statistical downscaling of temperature and precipitation bridges the gap between the predictors and predictand and assist policymakers, researcher, and end users to assess the likely impacts of climate change on key sectors. This study simulated the future change in temperature and precipitation for Mount Makulu, Zambia using ERA-Interim reanalysis data within the Statistical Downscaling Portal (SDP). Climate scenarios were generated for temperature and precipitation for 2010-2039/1971-2000, 2040-2069/1971-2000 and 2070-2099/1971-2000 for RCP4.5 and RCP8.5. The Mann-Kendall test showed a significant positive trend for maximum and average temperature for the current and future climate scenarios at p<0.05. Results showed a higher variability and decreasing trend in annual precipitation under RCP 8.5 compared to RCP 4.5. The mean temperature change relative to the baseline would be 1.03oC, 1.21oC, 1.65oC, 1.87oC, 1.89oC and 2.23oC under RCP4.5 (2020), RCP8.5 (2020), RCP4.5 (2050), RCP8.5 (2050), RCP4.5 (2080) and RCP8.5 (2080), respectively. The use of PP improves the coarse spatial resolution of temperature and precipitation in GCMs and assists decision makers and end users in understanding the likely impacts of climate variability and change. The future climate scenarios under RCPs (RCP 4.5 and RCP 8.5) provides new insights in rainfall and temperature trends from 2010-2099. The findings provide useful information as inputs into the Sectors and National Adaptation Plans.

Author(s) Details:

Charles Bwalya Chisanga,
The Copperbelt University, SNR, PES, P. O. Box 21692, Kitwe, Copperbelt, Zambia.

Elijah Phiri,
University of Zambia, School of Agricultural Sciences, Department of Soil Science, P. O. Box 32379, Lusaka, Zambia.

Vernon R. N. Chinene,
University of Zambia, School of Agricultural Sciences, Department of Soil Science, P. O. Box 32379, Lusaka, Zambia.

Monday Chota,
Ministry of Education, P. O. Box 440045, Isoka, Muchinga, Zambia.

Edson Nkonde,
Zambia Meteorological Department, P. O. Box 30200, Lusaka, Zambia.

Please see the link here: https://stm.bookpi.org/EIEGES-V8/article/view/13615

Keywords: Scenarios, climate change, GCMs, perfect prognosis, statistical downscaling, SDP, predictand, predictor