Extension of a fine mesh reanalysis over France on the entire XX ieth century Feasibility and prospects

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1 Extension of a fine mesh reanalysis over France on the entire XX ieth century Feasibility and prospects MINVIELLE M (1), CERON J-P (1), BESSON F (1) and PAGE C (2) (1) Climatology Department, (2) CERFACS Jean-Pierre.Ceron@meteo.fr Session UC4/SE3

2 Outline Preclide Project Objectives and goals Comparison of methods for Atmospheric parameters NSPA-S ws SD-WTA SD-WTA vs SD-WTAO Recap of Performances Application to the entire XX ieth century Precipitation trends Temperature Trends Conclusion & Perspectives

3 PRECLIDE (DEcadal CLImate PREdictability) Project Project funded by BNP PariBas fundation (~900k ) CERFACS coordination Project Objectives Expected Deliverables Main Partners Improve our scientific understanding of decadal climate variability and predictability over the next year period. - Assessment and understanding of climate variability and predictability at decadal time scales - Improvements of the different components (models, initialization, uncertainty assessment, regionalization) of decadal predictions systems - Development and application of methodologies to estimate climate change regional and local impacts at decadal time scales (next 30 to 50 years) - CERFACS - Météo-France, Climatology Department (DCLIM) - National Center for Atmospheric Science-Climate, University of Reading - Department of Physics, University of Toronto

4 Main DClim objectives PRECLIDE Project Improve our scientific understanding of decadal climate variability and predictability over France For Temperature and Rainfall Improve our scientific understanding of the impact of decadal climate variability onto Hydrology (Soil Wetness and River Flow). Extension of the Climsec Project Results (Gerbier Mumm Price 2011) Isolate the decadal variability contribution from the contribution of anthropogenically forced signal at a fine spatial resolution Main DClim deliverable "Extension" of the existing SAFRAN-ISBA-MODCOU (SIM) reanalysis over the entire 20 ieth century ; the current version covering the 1958-present period (Vidal et al., 2008) Provision of a reference dataset for model verification at decadal scales Provision of a new reanalysis specifically dedicated to the study of interannual to decadal variability of water resources.

5 Methodology The different methods tested Near Surface Parameter Analysis (NSPA-S) Safran module (same than the current SIM version) using available observations (T and Precip) Model guess given by NOAA 20 CR, Statistical Downscaling based on Weather Type classification and Analog Method (SD-WTA) Weather Types on NOA 20 CR Analog method using NOAA 20CR (several analogs), based on Weather Type classification and Analog Method (SD-WTAO) Same method Use of available observations (guidance of the analog choice among several) Based on Analog (SD-A) using NOAA 20CR fields as predictors (closest neighbouring)

6 Available Observations Assessment of the bias related to the (weak) network density in SAFRAN SIM experiment done 5 recent hydrological years (6364, 7576, 8283, 9495 et 0203) simulated with as SAFRAN input samples of observations artificially reduced to an equivalent network density of 1910, 1930 and Same years also reconstructed with DSCLIM (statistical donwscaling).

7 NSPA-S : Sensitivity to the network density Relative Difference for the annual precipitation ( ) with the current SIM version 1910 Density 1950 Density The lowest the density the largest the error, consistently with the geographical localisation of the observations.

8 SD-WTA performance Same Hydrological years reconstructed with SD-WTA to compare with the NSPA-S experiment done with a weak observation network density: Annual precipitation mean over the period (observations- black- and SD-WTA- Yellow) SD-WTA Results (comparison with SIM and NSPA-S) NSPA-S 1910, NSPA-S 1950, SD-WTA, SIM SD-WTA over performs NSPA-S Better Mean state, Interannual and seasonal correlations Comparable Seasonal Cycle (reasonnably good) Weaker daily correlation SD-WTA Results (comparison with SIM France averaged mean) Long term and interannual variability quite well reproduced

9 SD-WTA vs SD-WTAO Precipitation Daily Correlations period SD-WTA SD-WTAO Precipitation Seasonal/Yearly Correlations period SD-WTA SD-WTAO DJF JJA YEAR

10 SD-WTAO vs SD-A Precipitation Daily Correlations period SD-A SD-WTAO Precipitation Seasonal/Yearly Correlations period SD-A SD-WTAO DJF JJA YEAR

11 Performances of the Methods SD-WTA over performs NSPA-S Better Mean state, Interannual and seasonal correlations Comparable Seasonal Cycle (reasonnably good) Weaker daily correlation -> consistent with the method development (for interannual up to Climate Change time scales) Long term and Interannual variability quite well reproduced SD-WTAO over performs SD-WTA Mean state comparable Seasonal Cycle comparable Better Interannual and seasonal correlations Trend comparable Better daily correlation SD-A slightly better than SD-WTAO Mean state some improvment Seasonal Cycle comparable Interannual and seasonal correlations quite comparable Daily correlation comparable

12 Application to the entire XX ieth century DJF Trend PRECIP mm/dec SON Reanalysis JJA Observation MAM Reanalysis Observation 12

13 Application to the entire XX ieth century DJF Trend PRECIP mm/dec SON Reanalysis JJA Observation MAM Reanalysis Observation 13

14 Application to the entire XX ieth century Trend TEMPERATURE C/dec Year SD-A SD-WTAO Observation SD-A slightly better than SD-WTAO Trend comparable but over estimated over the period ( ) 14

15 Application to the entire XX ieth century DJF Trend TEMPERATURE C/dec SON Reanalysis JJA Observation MAM Reanalysis Observation 15

16 Conclusions Using NSPA-S with old observations Large impact of the network density (lower density = larger the error). Error consistent with the station location (lower where observation is available) Larger mean state bias with respect of SD methods. Reasonnably good daily correlation. Some additional concern with the sustainability of data quality on time and possible non homogeneous observations along the period. Using SD-WTAO Modification and improvment of the original version of SD-WTA. Significant improvment for interannual and daily correlations Better mean state (with respect of NSPA-S) Using SD-WTAO or SD-A over the entire XX ieth century Interannual and multidecadal variability quite well captured. Trends to be investigated further but very encouraging results. Weaknesses in Summer and in South-East of France. Tendency to over estimation of precipitation

17 Application to Hydrology Perspectives Use of SD-WTAO and SD-A reconstructed Temperatures and Precipitations to force ISBA (SWI) and MODCOU (River Flows) models over the period (2 set of hydrological reanalysis already available) Some windows of improvments for the reconstruction of both atmospheric and hydrological variables (nevertheless encouraging results for river flows when observed data available and for SWI) Investigation of the influence of the multi decadal variability over Temperature, Rainfall and Water Resources Investigation of the relative weight of the mutli decadal variability in the long term variations. Extension of the Climsec results over the entire XX ieth century.

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