Examples of using gridded observed climate datasets at the Finnish Environment Institute

Transcription

1 Examples of using gridded observed climate datasets at the Finnish Environment Institute Stefan Fronzek, Timothy Carter, Nina Pirttioja Finnish Environment Institute (SYKE) Workshop New gridded climate data set in Finland 27 August 2015, FMI, Helsinki

2 Outline Climate indicators related to the health sector Bioclimatic envelope modelling of plant and animal distributions Crop yield modelling Wish list and suggestions

3 Climate indicators for the health sector Simple indicators relevant for the health sector that quantify the exposure to climate change: Indicators of potential heat stress Number of high temperature days: future daily mean temperature TG > 99 th %-ile of baseline TG (TG99) Number of very warm days: maximum daily temp. (TX) > 25 C Number of heat waves: TG > TG99 for at least 6 consecutive days Indicators of potential cold stress Number of cold days: future TG < 1 st %-ile of baseline TG (TG01) Number of cold spell days: TG < TG01 for at least 6 cons. days Indicator of potential icy conditions Number of freezing point days: min temp (TN) < 0 C < max temp (TX) Regional averages for municipalities Changes between future and present

4 Change in number of days when max daily temperature >25 C, wrt Probabilistic projection, SRES A1B (Harris et. al 2010) 5th %-ile average 95th %-ile days/year

5 User-based Climate change Impacts, Adaptation and Vulnerability mapping tool Source:

6 Source: Ruuhela et al. Heat-related mortality estimated with a temperature function (1/3) Statistical relationships between: Daily mortality statistics from hospital districts ( ) Daily mean temperature, averaged from 10 km grid to the hospital districts Temperature dependence of mortality in Finland Hospital districts (source: Min. of Social Affairs and Health, Finland) Southern Finland, Simplified parameters, Excess mortality (1/100000) North All South Daily mean temperature (C)

7 Heat-related mortality estimated with a temperature function (2/3)

8 Heat-related mortality estimated with a temperature function (3/3) Source: Carter et al. 2014

9 Bioclimatic envelope modelling Climate indicators calculated from monthly mean temperature and precipitation, e.g.: mean temperature in April-June mean temperature of the coldest month annual daily temperature sum above 5 C mean precipitation in April-June mean annual precipitation simple water balance using precipitation and temperature Period-averages for different periods, e.g.: , (Heikkinen et al. 2009) (Virkkala et al. 2013) and annual averages (Mitikka et al. 2008)

10 Bioclimatic envelope modelling Invasive aquatic plant species: Observed (green and blue points, <1986 and ) and simulated distribution of Canadian waterweed (Elodea canadensis). Source: Heikkinen et al Future projections of range shifts Predicted probability (in %) of bird species distribution (C, E) and (E, F). Source: Virkkala et al Middle spotted woodpecker Recent range shifts Observed and simulated distribution of the map butterfly (Araschnia levana) for Source: Mitikka et al Observed Pine grosbeak Simulated

11 Crop yield simulations with gridded climate data Climate variables: daily mean temperature, precipitation, wind speed, relative humidity Barley yields simulated with WOFOST, Scarlett Source: Pirttioja et al., unpl.

12 Daily 10-m wind speed from ERA-interim linearly interpolated on the Finnish 10-km grid April 2000 daily wind speed in Jokioinen from station data, re-analysis and MARS interpolation Daily wind speed in 2008 April 2000 era40$wind ERA-40 grid cell ERA-interim grid cell Jokioinen MARS grid cell MARS 2nd cell Apr 01 era40$date

13 Wish list and suggestions (1/2) Quality control Comparison to other gridded products Quantified error estimate (e.g. E-OBS provides gridded daily standard error for each variable) Comparison between daily and monthly interpolations Consistency checks (TMIN<TMAX, RR>0 mm) Can you look at trends for a single grid cell? CRU/E-OBS approach to interpolate daily anomalies and add these to monthly means

14 Name E-OBS WATCH- WFDEI AgMIP JRC/MARS/ Agri4Cast EURO4M Observed daily climate datasets covering Europe Spatial extend +resol. Europe, 0.25 Global, 0.5 Global, 0.25 Europe, 25 km Europe, ~5 km Period Temp. resol Haylock et al. 2008, ensembles/download.php Weedon et al. 2011, /mars/aboutus/agri4cast/datadistribution/agri4cast- Interpolated-Meteorological- Data ets.html Daily Daily and 3- hourly Daily Daily Daily Variables 1 Method TG, TN, TX RR, PP TG, RR, PP, WS, GR, SH, SN TG, TN, TX, RR, WS, GR, RH Interpolated from station data Combining ERAinterim re-analysis with monthly CRU data Combining MERRA re-analysis with monthly CRU data and other observations TG, TN, TX, Interpolated from RR, WS, station data GR, RH, PE, SN TG, TN, TX, RR Downscaling ERAinterim with MESAN weather model Reference, web link Ruane et al. 2015, pacts/agmipcf/ 1 ) variable abbreviations: mean temperature (TG), minimum temperature (TN), maximum temperature (TX), precipitation sum (RR), sea level pressure (PP), wind speed at 10 m (WS), specic humidity (SH), relative humidity (RH), Penman potential evaporation (PE), global radiation (GR), snowfall rate or depth (SN)

15 Wish list and suggestions (1/2) Quality control Comparison to other gridded products Quantified error estimate (e.g. E-OBS provides gridded daily standard error for each variable) Comparison between daily and monthly interpolations Consistency checks (TMIN<TMAX, RR>0 mm) Can you look at trends for a single grid cell? CRU/E-OBS approach to interpolate daily anomalies and add these to monthly means

16 Example of a particularly cold year at Jokioinen, year 1987 Observed baseline weather data, Jokioinen, 1 Jun Dec 1987 Years: , location: Jokioinen Temp (C) Max temp Min temp Long-term max Long-term min Sowing Harv est Source: Pirttioja et al., unpl. Precipitation (mm) Precip Extreme precip Radiation Sowing Harv est Day nr Observed daily weather data at Nossen from 1 June 1986 to 31 December Vertical lines at the top figure depict temperature thresholds with potential relevance for phenology : +30, heat stress, Tmax for vernalisation, and +6 optimal temperature for vernalitastion, -1.3 base temperature for vernalisation and lethal limit (Porter & Gawith,1999). Black bars in the bottom figure mark temperature events when daily precipitation sum exceeds the 99th percentile of precipiation sums of that month during Radiation (MJ/m 2 d-1)

17 Wish list and suggestions (1/2) Quality control Comparison to other gridded products Quantified error estimate (e.g. E-OBS provides gridded daily standard error for each variable) Comparison between daily and monthly interpolations Consistency checks (TMIN<TMAX, RR>0 mm) Can you look at trends for a single grid cell? CRU/E-OBS approach to interpolate daily anomalies and add these to monthly means

18 Wish list and suggestions (2/2) Practical suggestions Version numbers Regular updates (maybe even more often than once a year?) Distribution through web page Consistent file format, netcdf? Interpolation to different grid systems (EUREF-FIN, KKJ, 0.25 ) Suggestions for additional gridded products Interpolation to minimum and maximum in addition to mean altitude Very high spatial resolution (~ metres) in urban areas for daily data; high resolution (~1 km) of monthly data and long-term averages for biodiversity research Additional variables: near-surface wind speed, relative humidity, vapour pressure, radiation