Assesment of fine resolution RegCM simulations over south-southeast - PowerPoint PPT Presentation

Assesment of fine resolution RegCM simulations over south-southeast Brazil Rosmeri P. da Rocha * , Michelle S Reboita, Marta Llopart *Departamento de Ciências Atmosféricas – Universidade de São Paulo, Brazil

Previous studies – south-southeast Brazil - da Rocha et al. (2015) – compared two (2003 and 2004) austral winter simulations (JJAS) using RegCM3 with 50 and 20 km grid spacing à local features of climate over São Paulo city are more realistic using 20 km. - 20 km simulation was used to characterize the mean conditions favoring fog events over São Paulo city à moist air is transported by an anticyclone located southward of the city

Previous studies – south-southeast Brazil - Application studies: thermal comfort in São Paulo megacity under climate change RCP8.5 scenario using RegCM4-50 km of grid Future (2065-2099) minus present (1975–2005) climate Temp RH Increase of air temperature is compensated by decrease of relative humidity; IIPET-Temp: São Paulo is in a transicion region of positive/ negative values; IPET IPET-Temp This study has shown the needing of fine resolution projections to better understand the climate change impacts (Batista et al., 2015)

Motivation and objectives As CORDEX-WCRP initiatives: South America Pilot Study Flagship (SESA-FPS) – “ Extreme precipitation events in Southeastern South America: a proposal for a better understanding and modeling ” – PI – M. Laura Berttolli. Objective is to evaluate the hability of fine resolution simulations with RegCM4 to reproduce regional and local features of climate over south-southeastern Brazil

Simulations set-up Model version: RegCM4.6.1 Simulation period: 01/12/2009 – 31/21/2010 (analysis – 2010) à Initial and boundary conditions: ERA-Interim (~ 75km) – Dee et al. (2011) à Convective scheme: Emanuel over all domain à Large scale precipitation: SUBEX à Number of vertical levels: 23 à Surface schemes: BATS and CLM4.5 à Hydrostract (H) or non-Hydrostact (NH) 3 large domains - LD - (ds= 100, 50, 25 km) 1 small domain – SD - (ds=5 km) Ds (km) 100 50 25 5 BATS H/LD H/LD H/LD NH/SD CLM H/LD H/LD H/LD NH/SD Number of grid 90x109 174x218 345x431 381x561 points Time step (s) 150 100 50 15

Domains (topography and landuse): Ds=100, 50, 25 km ds=5km Landuse: zoom over south-southeast Brazil 100 km 50 km 25 km 5 km

Data to evaluate the simulations: Various analysis/reanalysis are used to compare simulations with observations: Precipitatiion Temperature Data Description Resolution Data Description Resolution TRMM – 3B42 sattelite 25 km CFSR reanalysis 30 km product CPC daily 50 km ERA5 reanalysis 30 km raingauge analysis Local observations: Station data for São Paulo city: wind and air temperature at each 3 hours Annual cycles over 3 subdomains Big – SE Meteorological Medium – SU station: São Paulo Small - SP

Annual mean rainfall – 2010 OBS CLM BATS à Location of more intense rainfall over south Brasil/Paraguay: BATS has greater agreement with observations; positive impact of high resolution à Fine resolution: deficit of rainfall over part of southeast Brazil

Annual mean 2m air temperature – 2010 Topography OBS CLM BATS BATS - increase of resolution defines better areas of low/high temperatures à values/spatial pattern are similar to ERA5; CLM – a sistematic warm biases over NW domain (increase in fine grid simulation); It is necessary mesoscale analysis for validate fine resolution

Annual Cycle – 2010 - rainfall over SE (big subdomain) BATS Taylor Diagram 0.1 0.2 0.3 trmm bats50 bats5 0.4 ● bats100 bats25 0.5 Correlation 0.6 3 0.7 3 Phase of annual cycle is Standard Desviation 0.8 realistically captured by all 2 2 0.9 simulations à overperformce of 5 km 0.95 ● 1 1 experiments (CLM and ● 0.99 ● ● BATS) à + ● 0 0 1 2 3 CLM BATS – only 5km simulates Standard Desviation Taylor Diagram the observed low rainfall 0.1 0.2 0.3 trmm clm50 clm5 0.4 ● rate on dry season (MJJA) clm100 clm25 0.5 Correlation 0.6 3 0.7 CLM à dry season rainfall 3 Standard Desviation 0.8 is less dependent of the resolution (statiscal indices 2 2 0.9 are too closer) 0.95 ● 1 1 ● ● ● 0.99 ● 0 0 1 2 3 Standard Desviation

Annual Cycle of Rainfall – 2010 – SU subdomain (medium subdomain) BATS Taylor Diagram 0.1 0.2 0.3 ● trmm bats50 bats5 0.4 4 bats100 bats25 0.5 Correlation 0.6 4 Considerable differences 0.7 3 3 occur between Standard Desviation 0.8 ● BATS and CLM: ● 2 2 ● 0.9 ● BATS: phase of annual cycle 0.95 better captured by 1 1 0.99 100 and 5 km à smaller RMSE; however both ● 0 0 1 2 3 4 underestimate the rainfall CLM Standard Desviation Taylor Diagram rate mostly during April (more 0.1 0.2 0.3 rainy month) trmm clm50 clm5 5 0.4 ● clm100 clm25 0.5 Correlation 0.6 CLM – phase of annual cycle 4 0.7 4 (correlation) and intensity of Standard Desviation 0.8 3 3 rainfall are less sensitive to ● 0.9 ● the grid resolution; 2 2 ● 0.95 ● 1 1 0.99 ● 0 0 1 2 3 4 5 Standard Desviation

Rainfall Annual Cycle – 2010 – SP subdomain (local) BATS Taylor Diagram 0.1 0.2 0.3 trmm bats50 bats5 0.4 ● bats100 bats25 0.5 At local scale there is Correlation 6 0.6 greater disagreement 6 0.7 5 Standard Desviation among observations and 0.8 4 4 also simulations; 0.9 3 ● 0.95 Compared with TRMM: 2 2 ● ● ● BATS - No clear 0.99 1 improvement of the ● 0 simulated annual cycle as 0 1 2 3 4 5 6 CLM function of resolution; Standard Desviation Taylor Diagram 0.1 0.2 0.3 0.4 ● trmm clm50 clm5 clm100 clm25 0.5 Correlation 6 0.6 6 0.7 5 Standard Desviation 0.8 4 4 CLM – small overperform 0.9 3 of 50 km 0.95 ● 2 2 ● ● ● 0.99 1 ● 0 0 1 2 3 4 5 6 Standard Desviation

Daily rainfall – 2010 – synthesizing statistical indices (RMSE, SD and correlation) Taylor Diagram SE subdomain 0.1 0.2 0.3 0.4 ● trmm bats50 bats5 bats100 bats25 30 30 30 30 30 0.5 ● trmm bats50 bats5 300 bats100 bats25 trmm C 5 0.6 o bats100 r r e bats50 l a 25 25 25 25 25 t bats25 i 250 o 0.7 n bats5 4 20 20 20 20 20 Standard Desviation 200 0.8 rainfall(mm/day) rainfall(mm/day) rainfall(mm/day) rainfall(mm/day) rainfall(mm/day) 4 15 15 15 15 15 150 3 0.9 10 10 10 10 10 100 2 2 0.95 ● ● ● ● 50 5 5 5 5 5 1 0.99 0 0 0 0 0 0 2.5 7.5 12 18 22 28 0 0 0 0 0 100 100 100 100 100 200 200 200 200 200 300 300 300 300 300 ● 0 days days days days days 0 1 2 3 4 5 Standard Desviation Ds (km) 100 50 25 5 SDE CLM- CLM+ BATS - BATS+ SU CLM- CLM+ BATS- BATS+ SP CLM- CLM+ BATS- BATS+

Some improvemments of the annual cycle of rainfall and spatial pattern of simulated variables in high resolution experiments (CLM and BATS) à “Added Value” Next à Local features of climate in the 5km simulations

Mesoscale circulations over eastern southest Brazil: 5 km Tiete river basin Annual mean (2010): 10-m wind and rainfall CLM BATS Main patterns of mean circulation/rainfall are similar in CLM and BATS; São Local features à CLM simulates less rainfall in the main SP river basin Paulo (Tiete) and more rainfall over Sao Paulo city. city

Diurnal cycle: day (15-21 LT) minus nigth (03-09 LT) (as in da Rocha et al., 2009) CLM BATS Diurnal rainfall over mountains, along the shore and in São Paulo city; Nocturnal rainfall in Tiete river basin;

Day (15-21 LT) minus nigth (03-09 LT) – zoom CLM BATS During the day SE winds and along shore rainfall à sea breeze in CLM à greater amount of rainfall over São Paulo during the day (urban effect?)

Annual mean differences: CLM minus BATS 2-m air temperature 10-m wind/rainfall SE winds (sea breeze) and continental NW CLM simulates higher winds are stronger in CLM than in BATS à temperatures over São Paulo contributing to wind convergence over São (urban effect?) and along the Paulo à higher amount of rainfall over valey center-north of the city in CLM

Local validation: diurnal cycle of meridional wind over São Paulo Observation Dec Observation: wind changes from north to south in the Montlhy mean early afternoon (13-14 LT à diurnal cycle 16-17 UTC; OND 11-12 LT); More intense N winds JF/ 2010 Jan 0 2 4 6 8 10 12 14 16 18 20 UTC 0 2 4 6 8 10 12 14 16 18 20 UTC CLM BATS CLM has large hability to reproduce the observation (weaker winds and time of change from N to S) than BATS

Local validation: diurnal cycle of air temperature over São Paulo Observation Dec Montlhy mean diurnal cycle Jan 0 2 4 6 8 10 12 14 16 18 20 UTC CLM BATS Diurnal cycle of air temperature is realistically simulated by both CLM and BATS