Future precipitation in the Central Andes of Peru

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1 The International Conference on Regional Climate (ICRC)-CORDEX 2016 Future precipitation in the Central Andes of Peru Gustavo De la Cruz 1 Delia Acuña Azarte 1 1 National Meteorology and Hidrology Service of Peru Stockholm, 18th May 2016

2 1. STUDY AREA: The study area comprises four basins that provide water resources to Lima: Mantaro basin Chillon basin Lurin basin Rimac basin

3 Conventional Stations (stn)

4 Marcapomacocha station (4479 masl) Matucana station (2431 masl) Temperature Extreme Index (period: ) Warm days (Tx90p) Cold days (Tx10p) Cold nights (Tn10p) Warm nights (Tn90p)

Season Dry Season SENAMHI-SEDAPAL, 2014 +

5 Medium basin Upper basin Humidity trends in atmospheric column: Wet Season Dry Season Wet Season Dry Season SENAMHI-SEDAPAL, Positive trends ( ) O Negative trends ( )

(%) 2016:2045 1981:2005 Global models show uncertainty in spatial variability in precipitation; however, they

CMIP5 models projected positive changes in the maximum temperature and minimum temperature.

6 RCP 8.5 RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5 RCP 4.5 Projected climate change in the Central Andes of Peru according CMIP 5 models Mean precipitation Change (%) 2016: :2005 Global models show uncertainty in spatial variability in precipitation; however, they suggest in projecting a slight increase in rainfall in the middle and upper basins. CMIP5 models projected positive changes in the maximum temperature and minimum temperature. Spatial resolution of global models is too coarse, downscaling is necessary Mean maximum temperature change (%) 2016: :2005 Mean minimum temperature change (%) 2016: :2005 Downscaling SENAMHI-SEDAPAL, 2015

2. DATASET Observational dataset Daily precipitation from 53 rain gauge stations divided in 5 groups according to vector regional method Maximum/Minimum temperature

thermodynamics variables General Circulation Models dataset Rain gauge stations Temperature stations Three models GCM were used in this research.

CESM1-CAM5: model developed by National Science Foundation, Department of Energy, National Center for Atmospheric Research HadGEM2-ES: model developed by Met Office

7 2. DATASET Observational dataset Daily precipitation from 53 rain gauge stations divided in 5 groups according to vector regional method Maximum/Minimum temperature data from 10 stations Period: Reanalysis ERA-INTERIM dateset Centre: European Centre for Medium-Range Weather Forecasts Period Circulation and thermodynamics variables General Circulation Models dataset Rain gauge stations Temperature stations Three models GCM were used in this research. According to PACC (2012) this three models have good representations of athmospheric patterns in Andes of Peru. CESM1-CAM5: model developed by National Science Foundation, Department of Energy, National Center for Atmospheric Research HadGEM2-ES: model developed by Met Office Hadley Centre MPI-ESM-LR: Max Planck Institute for Meteorology Historical period: Projections period: Emissions scenarios: RCP 4.5 and RCP 8.5 Domain Ten different geographical domains in order to find Principal Components like predictor

GCMs validation Regridded information 0.5 x0.

Principal Components like predictor Predictor selection 33

precipitation Multiple linear regression for maximum/ minimum

data and Reanalysis ERA-interim data in 1981-2005 period:

correlation (temperature/precipitation) BIAS PDF-Score

predictor set and one geographic domain for each rain gauge

geographic domain for temperature stations(1 group)

8 GCMs validation Regridded information 0.5 x0.5 Different geographical domains (10) in order to find Principal Components like predictor Predictor selection 33 different predictor sets: P1: 2T P2: 2T, Q850 P3: 2T, T500, Q700, anothers 3.METHODOLOGY Transfer function Analogue method for precipitation Multiple linear regression for maximum/ minimum temperature Calibration statistical models with observations data and Reanalysis ERA-interim data in period: Local Scale Scenarios Validation Pearson/Spearmen coefficient correlation (temperature/precipitation) BIAS PDF-Score Optimums predictor set and geographical domain One optimum predictor set and one geographic domain for each rain gauge stations groups (5 groups) One optimum predictor set and one geographic domain for temperature stations(1 group) Projections for precipitation and minimum/maximum temperature with CMIP5 models: HADGEM-ES, CESM1-CAM5, MPI-ESM-LR Period: Emission scenarios: RCP 4.5 and RCP 8.5

4. RESULTS AND DISCUSSIONS Selection of predictors:

BIAS PDF-Score Best predictor set for each group Group

(2T, Z500, U500, Q500) Group 3: D5 and P32 (2T, Z500,

and P28 (2T, Q500, U250) Maximum temperature: D8 and

9 4. RESULTS AND DISCUSSIONS Selection of predictors: results for Group 1 of precipitation Correlation (rho) BIAS PDF-Score Best predictor set for each group Group 1: D5 and P26 ( 2T, Q500, U500 ) Group 2: D7 and P33 (2T, Z500, U500, Q500) Group 3: D5 and P32 (2T, Z500, Q500) Group 4: D6 and P28 (2T, Q500, U250) Group 5: D7 and P28 (2T, Q500, U250) Maximum temperature: D8 and P26 (2T, Q500, U500) Minimum temperature: D8 and P18 (2T, Z250)

10 Dry season (JJA) Rainy Season (DEF) Simulation results and validation Correlation Bias PDF-Score

11 Simulation results and calibration Rainy season (DEF) Dry Season (JJA) 4400 masl 2431 masl 3350 masl

12 Dry season (JJA) Rainy season (DEF) Climate change projections of precipitations Marcapomacocha Pampa Libre Picoy Junin

13 Climate change projections of precipitations Pampa Libre San Lazaro de Escomarca 1800 masl 3600 masl Junin Huarochiri 4120 masl 3146 masl

14 RCP 8.5 RCP 4.5 Precipitation change 2045: :2005 Model HadGEM-ES DEF JJA

15 Tmin Tmax Climate change projections of maximum/minimum temperature Marcapomacocha Picoy Tarma

16 RCP 4.5 RCP 8.5 Maximum/Minimum temperature change 2045: :2005 Model HadGEM-ES DEF Maximum temperature JJA DEF Minimum temperature JJA

17 5. CONCLUSIONS Results of statistical downscaling shows there is a mean increment for precipitation in between 0% and 100%, but there are some stations which show a mean decrease of precipitation in rainy season (DEF). The change of precipitation is stronger in RCP 8.5 scenarios than 4.5 scenarios. In rainy season, the change of precipitation for the period , is stronger in middle basin, whereas, in dry season is in upper basin. For the period , results shows a mean increment for maximum temperature, with respect to Some stations show decrease for minimum temperature.this change it is in between 0 and 4 C for maximum temperature and -1 and 3 for minimum temperature.

18 6. REFERENCES Gutierrez JM, San Martin D, Brands S, Manzanas R, Herrera S (2012) Reassesing statistical downsacling technique for their robust application under climate change conditions. Journal of Climate 26(1): , DOI /JCLI-D Benested RE (2010) Downscaling precipitation extremes. Theoretical and Applied Climatology 100(1-2):1-21, DOI /s Brands S, Gutierrez JM, Herrera S, Cofiño AS (2012) On the use of reanalysis dta for downscaling. Journal of Climatology 25(7): , DOI /JCLI-D Fealy R, Sweeney J (2007) Statistical downscaling of precipitation for a selection of sites in Irelan employing a generalised lineal modelling approach. International Journal of Climatology 27(15): DOI /joc.1506.

19 Thanks for your attention!

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