1 CL2.16 Urban climate, urban heat island and urban biometeorology H How 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 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 ( ) 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 1.Related to the temporal frequency of RCM outputs 2. Related to the urban representation in RCMs Key aspects of the study:

4 Key aspects: 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, V mean ; P mean ; PP mean ; RG mean RA mean z = 2 m; time-period Related to the temporal frequency 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.

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

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

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

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

r^2T ( C ) q (kg/kg)ff (m/s) All points0,9750,9110,701 Mean10,9990,844 Max0,9730,9780,961 Min0,940,9170, years; 3h frequency; ff, dd, T, HU 10years; 1h; ff, dd, T, HU, P, PP 1. Clustering observations: Validation & Reconstruction A B DC Scatter plot: Tall points (A), Tmean (B); Tmax (C) and Tmin (D) Standard error r 2

10 - Cluster attribution: AT, AH, (T, Hu) mean, max and min (u-v) mean wind components and PP mean - Reconstruction - Validation : r^2T ( C ) q (kg/kg)wind All points0,9750,911u Mean110,847 Max0,9960,873v Min0,9930,9870, r^2T ( C ) q (kg/kg)ff (m/s) All points0,9750,9110,701 Mean10,9990,844 Max0,9730,9780,961 Min0,940,9170, years; 3h frequency; ff, dd, T, HU 10years; 1h; ff, dd, T, HU, P, PP 1. Clustering observations: Validation & Reconstruction Standard error r 2

Bias correction: similar method than in Déqué, M. (2007) 2. Cluster attribution: AT, AH, (T, Hu) mean, max and min (u-v) mean wind components and PP mean 3. Reconstruction 4. Validation Obs. RCM 30 years; 3h frequency; ff, dd, T, HU 10years; 1h; ff, dd, T, HU, P, PP 1. Clustering: Future projections It is the degree of fit between the model and the observations. It should be evaluated before future projections analysis.

12 10th, Tmin, winter Standard error r 2 10th, Tmin, winter Control period + Model serie after bias correction * Hourly recontructed model serie 1. Clustering future projections: exemple ECHAM5-r3_RCA (SMHI center)

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

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

15 Key aspects: RCMs do not use urban parameterizations, so urban climate features are not included in these scenarios. 2. Related to the urban representation in RCMs 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 Day-time: Hidalgo et al. 2008

17 Thanks for your attention!