Seasonal forecasting of climate anomalies for agriculture in Italy: the TEMPIO Project

Transcription

1 Seasonal forecasting of climate anomalies for agriculture in Italy: the TEMPIO Project M. Baldi(*), S. Esposito(**), E. Di Giuseppe (**), M. Pasqui(*), G. Maracchi(*) and D. Vento (**) * CNR IBIMET ** CRA CMA ECAC 2008 Climatological and meteorological information in the context of decision making 1

2 Motivations - The main aim of seasonal forecast is to predict the spatial and temporal distribution of atmospheric anomalies few months in advance - Even though the detailed dynamical evolution of the atmospheric system is not fully predictable at this time scale, some features can be predicted - In particular it is possible to deduce information on the climate over the next 1 to 3 months, and to estimate how much the probability distribution of such averages, differ from the "climatology". 2

3 Motivations - In a changing climate, characterized by a precipitation reduction, particularly during the cold season, by an increase of summer heat wave events, and by an extension of the growing season, the assessment of drought risk at seasonal time scale is fundamental in the agriculture sector for a better water management - Drought is the main issue and it depends on both large scale climatic anomalies and local environmental factors - Seasonal forecast systems are able to give information at large scale - For agricultural practices and crops management a sub-monthly to monthly forecast is needed to assess, among others, the risk of drought - It is fundamental to cover the gap and obtain information at local scale and sub monthly time scale 3

4 TEMPIO Project Overview Project Components Data Analysis Theoretical Modelling Pre operational System 4

5 TEMPIO Project Overview Project Components Data Analysis Theoretical Modelling Pre operational System DATA ANALYSIS STATION DATA analysis HISTORICAL MODEL DATA (NCEP/NCAR ECMWF) analysis CLIMATE VARIABILITY analysis WEATHER TYPE CIRCULATION analysis (COST733) HEAT and COOL WAVES analysis DRY and WET SPEELS analysis 5

6 Climatic Regions 6

7 Monthly mean of Tmax and Tmin in the homogeneous regions, and for Italy in the periods: , , ,

8 Occurrence of cyclonic and anticyclonic weather types in winter and summer 8

9 TEMPIO Project Overview Project Components Data Analysis Theoretical Modelling Number of observations Pre operational System THEORETICAL MODELLING Identification and addressing current changes in climate variability Identification of seasonal variability predictors Development of a conceptual model for thermal and rainfall regimes Model validation 9

10 TEMPIO Project Overview Project Compnents Data Analysis Theoretical Modelling Pre operational System PRE OPERATIONAL FORECAST SYSTEM Conceptual model set up Pre operational system for monthly and seasonal forecast 10

11 Method Overview The adopted methodology consists of a multi-regressive method based on physical atmospheric indices and sea surface anomalies. Using the atmospheric behavior knowledge, at monthly time scale for the Mediterranean basin, we select potential predictors among a list of monthly large scale circulation indexes (SV NAM, Modified Zi, NAO), sea surface temperature (Atlantic tripole, Guinea Gulf SST) and OLR anomalies. The indices and their coefficients are selected as those who pemit the maximization of the regression values between observed and forecasted rainfall and temperature anomalies, and are used in a multi regressive model. Therefore an adaptation is performed through the best choice of predictors which provides a maximum probability of detection for the selected anomalies. 11

12 Method Overview The multi-regressive method is based on physical atmospheric indices and sea surface temperature anomalies at monthly time scale using NCEP-NCAR Reanalysis2. Lead time are selected based on physical considerations and on the maximization of the regression values between observed and forecasted field anomalies thus performing an adaptation. Monthly large scale predictors indexes: Atmosphere Lead Time [months] SV NAM Mod. Zonal Index Multi ENSO Index Atlantic tripole Gulf of Guinea 1 st EOF SSTAs

13 Seasonal Forecast Overview Observed Temperature Matrix Temperature at location (x,y) Predictor Multivariate Linear Regression with Matrix Forecasted Temperature at (x,y) for time (t) expressed 13 as probability of occurrence

14 Predictors Matrix set - up Predictors with trend Predictors detrended Different Years Trend Analysis Predictors Matrix

15 Iteration over time and space Hindcasted Temperature at location (x,y) Hindcast Temperature Matrix

16 Winter Summer Predictions Summer variability could be associated to winter conditions (SV NAM, snow cover, etc.), and to Atlantic SST anomalies (Tripole and 1 st EOF in the Gulf of Guinea). 16

17 Fall Winter Predictions Fall to Winter variability could be associated to Spring SST anomalies through reemerging Atlantic SSTA mechanism (OUTLOOKS) plus the atmospheric fall winter variability (MONTHLY). 17

18 Web Products web.fi.ibimet.cnr.it/seasonal/ 18

19 Web Maps We compute monthly standardized anomaly maps, respect to the climatology, every month on 15 th. 850hPa Geopotential Height 850hPa Air Temperature Precipitation Field Name and Lead Time Issued time Standardized Anomaly 19

20 An example: Dec 2007 OBS - DATA 20

21 An example: Dec 2007 OBS - DATA 21

22 Standardized Precipitation Index SPI The SPI quantifies the precipitation deficit for multiple time scales (1, 3, 6, 12, 24, 48 months). A long-term precipitation record (30 years at least) is fitted to a probability distribution (gamma distribution), that is then transformed into a normal distribution, with a mean of zero and standard deviation of one. Pros: - Help to assess drought severity; - standardization permits a comparison between climatically different areas. Cons: - Long-term precipitation record (30 years at least). - Possible misleading interpretation with extreme values in areas with low seasonal rainfall amount for short time scales (1, 3 months). 22

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24 System Validation 6 months Standardized Precipitation Index for May Oct one leave out strategy base period SPI < 1 Probability Of Detection Better -> 1 False Alarm Rate Better -> 0 24

25 CONCLUSIONS The seasonal forecast system described shows encouraging results over the Mediterranean area: large scale features and their anomalies show a reliable skill level. The successful key being the selection of physical predictors with long range memory which is linked to the Winter North Hemisphere Snow Cover extent and Spring melting. In particular winter SV-NAM and the Spring Atl. Tripole & 1 st EOF Guinea, are able to describe the core Summer variability over that area. Further investigations is needed to assess the role of each predictor and to select the best statistical downscaling techniques. web.fi.ibimet.cnr.it/seasonal/ 25