SIMULATING GAS AND AEROSOL CONCENTRATIONS IN THE PARIS AREA USING DIFFERENT LAND SURFACE MODELS

Transcription

1 EGU SIMULATING GAS AND AEROSOL CONCENTRATIONS IN THE PARIS AREA USING DIFFERENT LAND SURFACE MODELS Dmitry Khvorostyanov, Laurent Menut, Jean Charles Dupont, Yohann Morille, Martial Haeffelin Laboratoire de Météorologie Dynamique IPSL Ecole Polytechnique, Palaiseau, France Session AS3.4, room 14 on Thursday, 06 May 2010, 11:00

2 The Modeling Tools: WRF and CHIMERE WRF (3.1.1): weather research and forecast model widely used in the World community CHIMERE is an off line chemistry transport model, a French CNRS national tool: Developed by IPSL/LMD and INERIS Used by more than 100 users for research or forecast Involved in projects such as GEMS, AMMA, GEOMON, CIRCE, MACC etc. Main processes: Anthropogenic emissions using EMEP, EDGAR or local emissions inventories Biogenic emissions using MEGAN Vertical mixing using a non local plume scheme Horizontal transport using the VanLeer scheme Chemistry with gaseous and aerosols species WRF and CHIMERE are used every day in experimental forecast for the COSY project:

3 WRF surface schemes and model configurations The sensitivity study is performed for the Paris area for summer 2008 With 4 different surface schemes: Diff: 5 layer Thermal Diffusion scheme (MM5) RUC: Rapid Update Cycle (Smirnova et al, 2000) Pleim and Xiu (Xiu and Pleim, 2001) Noah: Based on (Chen and Dudhia, 2001) model WRF Diff CHIMERE [c] Diff WRF RUC CHIMERE [c] RUC WRF PX CHIMERE [c] PX WRF Noah CHIMERE [c] Noah Comparisons to SIRTA meteorological data Comparison to AIRPARIF Surface concentrations

4 The Paris area, the SIRTA site and the studied period Studied period: summer 2008, June 1 September 3 A few precipitation events with max of 35 mm/h No pollution events exceeding warning thresholds

5 2m Temperature RUC shows a warm bias based on the studied period The other schemes yield relatively close scores with PX being slightly cold biased and Noah being slightly closer to observations

6 2m Relative Humidity RUC underestimates the 2m relative humidity The scores are close among the other LSMs

7 10m Wind Speed WRF overestimates 10m wind speed with all the LSMs, up to 2 times or more in daily maxima RUC yields the largest deviation towards stronger wind speeds

8 Main meteorological parameters for a good day and for a bad day A well simulated day: 21 August 2008 A poorly simulated day: 24 August 2008

9 Main meteorological parameters for a good day and for a bad day Small mid level clouds not visible by the models The cloud fraction is high (> 90%) Precipitation around noon not reproduced by the models Resulting large errors in main modeled meteo parameters

10 Surface ozone concentrations: urban and rural site The model simulates fairly well both afternoon peaks and low night time values The scores over the whole summer 2008 are very close among the LSMs: mean bias of ~13 g m 3, correlation depending on the LSM and on the site For individual days high variability among LSMs can occur, e.g., 20 g m 3 or 15% for August 1 in Rambouillet

11 Surface PM10 concentrations: urban and rural site The model overestimates PM10 concentration peaks The Noah scheme is more realistic: the mean bias for the whole summer 2008 is g m 3, the correlation is 0.35 for Paris High inter model variability for individual days, e.g., > 70 g m 3 for August 3 at Rambouillet One of the possible explanations is very high sensitivity of resuspension parameterization to surface humidity and thus to the LSM choice

12 Conclusions Four surface schemes were used for a sensitivity study at a regional scale for summer 2008 over the Paris area Simulating meteorological variables: The RUC scheme showed stronger wind, warmer, and drier biases For SHF and LHF, the Noah scheme shows the best results, followed by PX, Diff, RUC Precipitating clouds outside the PBL with high cloud cover can be difficult to simulate resulting in large errors in modeled variables Simulating pollutant concentrations: Both afternoon peaks and night time low values of O are well simulated. Mean bias ~ g m, correlation 0.8 Daily peaks of both ozone and PM10 concentrations are sensitive to LSMs (up to 20 g m 3 for O3 and more than 70 g m 3 for PM10) The Noah scheme showed the best results for simulated PM10: mean bias 13 g m 3, r = 0.35 for Paris The choice of right LSM to use with CTMs is thus important to give realistic warning threshold values