1 / 17

CL2.16 Urban climate, urban heat island and urban biometeorology

CL2.16 Urban climate, urban heat island and urban biometeorology. H ow to obtain atmospheric forcing fields for Surface Energy Balance models in climatic studies. Julia HIDALGO 1 , Bruno BUENO 1,2 and Valery MASSON 1 (1) CNRM/Meteo-France; (2) MIT, USA.

vkiefer
Télécharger la présentation

CL2.16 Urban climate, urban heat island and urban biometeorology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CL2.16 Urban climate, urban heat island and urban biometeorology How to obtain atmospheric forcing fields for Surface Energy Balance models in climatic studies Julia HIDALGO1, Bruno BUENO1,2 and Valery MASSON1(1) CNRM/Meteo-France; (2) MIT, USA This project is funded by the French Reserach Agency ANR (ANR-09-VILL-0003)

  2. Objective: • To provide representative atmospheric fields for a project related with climate change impact in future energy demand in Paris • to be used in long-term off-line simulations (2000-2100) • performed with the SEB model SURFEX (ISBA+TEB) • atmospheric fields are based on existing future projections of climate from Regional Climate Models: ENSEMBLES; MPI; CNRM • for a variety of IPCC emissions scenarios: Nakicenovic et al. (2000)

  3. Key aspects of the study: Related to the temporal frequency of RCM outputs 2. Related to the urban representation in RCMs

  4. Key aspects: Related to the temporal frequency SEB models need: • T; q; U,V; P; PP; +3 radiatif components (Lw, dir_sw, scat_sw) • z = canopy level. Hourly frenquency • Future climate projections currently available for Europe: • T mean,max,min; Hu mean,max,min; U, Vmean; Pmean ; PPmean ; RGmean RAmean • z = 2 m; time-period 1961- 2100 Methodology: - To classify hourly observational diurnal cycles to obtain a collection of clusters that represents the diversity of weather types affecting the site. - To use it to reconstruct the future projections at hourly frequency.

  5. 1. Clustering past observations 10years; 1h; ff, dd, T, HU, P, PP 30 years; 3h frequency; ff, dd, T, HU 1961 1990 1998 2008 • Statistic method: K-means • variables included as inputs: Tmax -Tmin, q, ff, dd, pp • Objective: (ΔT(h)mean_season)k 28070001. CHARTRES PARIS Shape: deviation to the mean value

  6. 1. Clustering past observations 10years; 1h; ff, dd, T, HU, P, PP 30 years; 3h frequency; ff, dd, T, HU 1961 1990 1998 2008 Wind rose observed for PARIS

  7. T(C) time 1. Clustering past observations: Validation & Reconstruction Specific Humidity Temperature Observations Reconstruction

  8. T(C) time 1. Clustering observations: Validation & Reconstruction Wind force Wind direction Observations Reconstruction

  9. r^2 T ( C ) q (kg/kg) ff (m/s) All points 0,975 0,911 0,701 Mean 1 0,999 0,844 B A Max 0,973 0,978 0,961 Min 0,94 0,917 0,806 C D 1. Clustering observations: Validation & Reconstruction 10years; 1h; ff, dd, T, HU, P, PP 30 years; 3h frequency; ff, dd, T, HU 1961 1990 1998 2008 Standard error r2 1998-2008 Scatter plot: Tall points (A), Tmean (B); Tmax (C) and Tmin (D)

  10. r^2 T ( C ) q (kg/kg) ff (m/s) All points 0,975 0,911 0,701 Mean 1 0,999 0,844 r^2 T ( C ) q (kg/kg) wind Max 0,973 0,978 0,961 All points 0,975 0,911 u Min 0,94 0,917 0,806 Mean 1 1 0,847 Max 0,996 0,873 v Min 0,993 0,987 0,8518 1. Clustering observations: Validation & Reconstruction 10years; 1h; ff, dd, T, HU, P, PP 30 years; 3h frequency; ff, dd, T, HU 1961 1990 1998 2008 • Cluster attribution: AT, AH, (T, Hu)mean, max and min (u-v)mean wind components and PPmean • Reconstruction • Validation 1961-1990: Standard error r2

  11. 1. Clustering: Future projections 10years; 1h; ff, dd, T, HU, P, PP 30 years; 3h frequency; ff, dd, T, HU Obs. 1961 1990 1998 2008 It is the degree of fit between the model and the observations. It should be evaluated before future projections analysis. RCM 1961 1990 2100 Bias correction: similar method than in Déqué, M. (2007) Cluster attribution: AT, AH, (T, Hu)mean, max and min (u-v)mean wind components and PPmean 3. Reconstruction 4. Validation 1961-1990

  12. 10th, Tmin, winter 10th, Tmin, winter 1. Clustering future projections: exemple ECHAM5-r3_RCA (SMHI center) Standard error r2 • 1961-1990 Control period + Model serie after bias correction * Hourly recontructed model serie

  13. Related to the temporal frequency of RCM outputs 2. Related to the urban representation in RCMs

  14. Key aspects: 2. Related to the urban representation in RCMs RCMs do not use urban parameterizations, so urban climate features are not included in these scenarios.

  15. Key aspects: 2. Related to the urban representation in RCMs RCMs do not use urban parameterizations, so urban climate features are not included in these scenarios. Methodology: To combine UHI scaling laws with the previous hourly regional atmospheric fields to reconstruct the themal spatial structure and day by day evolution.

  16. 2. UHI scalings: Night-time: Lu et al. 1997 Day-time: Hidalgo et al. 2008

  17. Thanks for your attention! julia.hidalgo@ymail.com

More Related