1. Modern and projected meteorological data for climate impact studies Vasily Kokorev

2. Meteorological data sources Weather stations Other ground observations Gridded products CRUTEM4 (5x5, averaging stations in the cell) CRU ts 3.22 (interpolation, 0.5x0.5) ERA-Int (reanalysis, T255, from 1979, assimilates 10^7 observations per day) NCEP2 (reanalysis, 2.5x2.5, from 1979) Satellite data Historical Future Model simulations Means (annual, regional, norms) Statistical (quintiles, extreme events) Spatial (South-North distribution) Rate of Change (trends) Parameters Climate projection GCM

3. Climate/Earth System Models Land physics and hydrology Ocean ecology, biogeochemistry Atmospheric circulation and radiation Atmospheric chemistry, aerosols Ocean circulation Plant ecology, land use Sea Ice Interactive CO 2 CMIP5 - Coupled Model Intercomparison Project Phase 5 A coordinated project by climate modeling community (Adopted from Jiping Liu) Promotes a standard set of model simulations in order to : evaluate how realistic the models are in simulating the recent past provide projections of future climate change on two time scales understand factors responsible for model differences

4. Observation network. Russia and neighboring countries Number of operational stations: Maximum 2297 in 1986, in 2014 - 1627 Mean areal of weather station: Russia– 10.5×10 3 km 2, Central region– 4.6×10 3 km 2, Russian Arctic – 25.1×10 3 km 2,

5. NCEP2 ERA Int CRUTEM4 CRU ts 3.22 Air Temperature, September 2013

6. MAAT trends for 1970-1999: selected GCMs vs observations

7. Observed regional temperature anomalies (Anisimov and Kokorev, 2012; Kokorev and Anisimov, 2013; Anisimov et al., 2013) EC

9. Temporal partitioning into climatic periods Global temperature anomaly, 1850 – 2010 http://www.cru.uea.ac.uk/cru/info/warming/ First year MAAT trends over NE calculated for periods of different length Distribution of stations by the beginning of modern climatic regime Map designating the beginning of modern climatic regime in NE regions

10. CMIP5: Difference between observed and modeled Trends

11. 1234567891011121314Ранг Наблюдения0.30.4 0.50.40.3 0.40.50.3 0.4 0 ERA-Interim0.3 0.40.50.40.3 0.40.30.40.50.30.40.05 CRUTEMP40.3 0.40.50.40.20.10.30.50.30.40.30.4 0.09 CRU TS 3.220.3 0.20.3 0.20.30.40.10.3 0.14 MIROC-ESM0.40.6 0.50.40.30.40.3 0.50.30.60.30.20.14 EC-EARTH0.30.4 0.70.5 0.80.50.60.80.40.15 IPSL-CM5A-LR0.3 0.5 0.70.40.70.50.4 0.30.50.40.16 CCSM40.30.50.6 0.70.60.50.70.4 0.5 0.40.18 HadGEM2-ES0.30.4 0.50.3 0.40.60.70.60.81.10.60.70.18 GISS-E2-H-CC0.20.40.30.2 0.30.20.60.40.50.6 0.40.50.19 NCEP20.2 0.30.2 0.3 0.50.40.30.80.4 0.2 MPI-ESM-MR0.20.50.60.5 0.60.40.80.60.50.70.80.3 0.23 HadCM30.70.5 0.40.6 0.50.60.50.410.90.7 0.24 bcc-csm1-10.80.60.7 0.6 0.80.60.50.70.50.3 0.50.25 CSIRO-Mk3-6-00.70.50.60.5 1.10.80.60.90.60.4 0.20.26 GISS-E2-R0.50.6 0.7 0.8 0.50.90.70.80.3 0.27 IPSL-CM5A-MR1.10.60.4 0.70.810.60.40.80.50.3 0.27 NorESM1-M110.60.40.80.70.50.70.60.50.70.90.40.50.27 NorESM1-ME10.50.40.20.6 0.90.50.20.30.10.50.2 0.27 CESM1-BGC0.80.7 0.50.80.9 0.5 0.70.8 0.40.50.28 GISS-E2-H0.60.50.4 0.7 0.40.20.10.40.50.60.30.10.28 bcc-csm1-1-m00.30.60.50.20.1 0.4 0.50.60.5 0.20.29 CESM1-WACCM0.60.30.2 0.50.60.80.5 110.90.60.40.29 ACCESS1.30.40.20.1 0.20.3 0.20.10.40.5 0.30.20.3 BNU-ESM0.40.10.30.40.30.50.60.910.80.7 0.8 0.3 CanCM41.30.90.80.60.90.810.50.40.60.50.70.50.40.3 ACCESS1-00.911.110.90.70.60.40.50.60.710.5 0.32 MPI-ESM-P0.60.81 0.70.80.9 10.60.3 0.50.33 CMCC-CMS0.10.50.60.40.30.1 0.2 0.10.2 0.40.34 CNRM-CM50.70.60.40.10.5 0.80.70.80.90.81.10.70.50.34 CanESM20.91 0.710.8 0.6 0.71.10.6 0.35 MIROC-ESM-CHEM0.70.80.7 0.50.60.40.10.40.30.500.10.36 MRI-ESM11.10.90.80.70.8 1 0.71.210.7 0.60.38 CMCC-CM1.10.90.80.60.8 0.9 110.600.50.90.41 HadGEM2-AO0.71.2 0.70.90.811.211.31.10.80.60.30.42 HadGEM2-CC0.80.200.100.20.50.1 0.50.70.5 0.20.44 MIROC4h-0.10.2 0.10.20.1 0.40.50.30.2 0.10.30.44 CNRM-CM5-20.70.60.5 0.70.60.20.10-0.10.10.70.10.20.46 FIO-ESM0000.1 0.20.5 0.40.70.30.2 0.48 MIROC521.61.51.21.41.31.71.30.61.41.20.70.50.30.48 CESM1-FASTCHEM0.60.50.40.7 0.60.80.20.10.90.5-0.10.2-0.10.51 FGOALS_g21.11.51.20.40.91 0.50.40.50.4-0.20.50.80.51 GFDL-ESM2G0.10-0.20.20.3 0.2 0.40.81.50.60.20.54 GISS-E2-R-CC0.30.2 0.10-0.10.1-0.10.1 0.50.60.40.20.65 GFDL-CM2p1-0.10.40.50.60.30.1-0.20.30.20.10.510.40.10.78 CMCC-CESM0.60.30.8 0.70.60.50.30.10.30.1-0.2 0.82 inmcm40-0.20.2 0.100.3-0.1 0.30.2 -0.20.11.06 GFDL-CM3-0.40.10.20.4 0.3-0.10.50.4-0.1 0.90.20.11.5 CESM1-CAM5-0.20.20.4 0.1-0.1-0.5-0.20-0.10.4 001.68 MRI-CGCM30.1-0.1-0.3 -0.2-0.10-0.2 0.1 -0.1 2.42 IPSL-CM5B-LR0.90.3-0.2-0.30.1 0.2-0.4-0.3-0.100.3-0.1 2.62 GFDL-ESM2M-0.2-0.5-0.3-0.2-0.8-0.1-0.40.10.20.60.90.404.06 MPI-ESM-LR-0.7-0.4-0.10.1-0.2-0.3 -0.200.10.50.80.50.376.2 1234567891011121314Rank Observations0.30.4 0.50.40.3 0.40.50.3 0.4 0 ERA-Interim0.3 0.40.50.40.3 0.40.30.40.50.30.40.05 CRUTEMP40.3 0.40.50.40.20.10.30.50.30.40.30.4 0.09 CRU TS 3.220.3 0.20.3 0.20.30.40.10.3 0.14 MIROC-ESM0.40.6 0.50.40.30.40.3 0.50.30.60.30.20.14 EC-EARTH0.30.4 0.70.5 0.80.50.60.80.40.15 IPSL-CM5A-LR0.3 0.5 0.70.40.70.50.4 0.30.50.40.16 CCSM40.30.50.6 0.70.60.50.70.4 0.5 0.40.18 HadGEM2-ES0.30.4 0.50.3 0.40.60.70.60.81.10.60.70.18 GISS-E2-H-CC0.20.40.30.2 0.30.20.60.40.50.6 0.40.50.19 NCEP20.2 0.30.2 0.3 0.50.40.30.80.4 0.2 MPI-ESM-MR0.20.50.60.5 0.60.40.80.60.50.70.80.3 0.23 HadCM30.70.5 0.40.6 0.50.60.50.410.90.7 0.24 bcc-csm1-10.80.60.7 0.6 0.80.60.50.70.50.3 0.50.25 Annual Temperature Trend 1981-2005, °C/10y

12. Projected changes, Air temperature anomaly, 2035-2064

13. Thank you for your attention! “Alice asked the Cheshire Cat, who was sitting in a tree, “What road do I take?” The cat asked, “Where do you want to go?” “I don’t know,” Alice answered. “Then,” said the cat, “it really doesn’t matter, does it?”

14. GCMs evaluation web interface (work in progress)

15. CMIP5 models. Uncertainty in mean annual temperature trend Export as Excel table

16. Regions 1234567891011121314 Winter Observations-13.0-7.8-6.3-2.2-10.6-13.6-19.6-15.6-21.5-27.3-34.1-22.8-22.1-13.9 CRU ts 3.22-14.9-8.3-6.4-3.5-11.1-14.1-19.8-16.4-23.8-29.5-37.4-25.4-26.0-14.1 CRUTEMP4-14.5-7.6-7.1-3.0-11.2-13.8-19.8-16.5-24.1-27.5-36.8-23.5-20.9 Era-Interim-14.0-8.2-6.5-3.2-11.0-13.5-20.3-15.6-21.2-28.2-34.0-25.5-25.0-15.2 NCEP2-13.3-9.0-7.7-3.9-11.2-13.0-17.7-14.4-18.9-24.0-28.9-22.4-23.1-15.2 Spring Observations-1.14.86.88.54.02.4-5.21.0-1.1-8.9-9.6-8.9-0.74.2 CRU ts 3.22-1.64.87.18.74.02.3-5.60.7-2.8-12.7-12.6-12.7-4.02.3 CRUTEMP4-1.65.56.58.34.12.6-5.70.6-3.0-10.8-12.5-11.4-2.1 Era-Interim-1.64.76.78.83.72.4-6.50.7-1.8-12.4-11.2-10.7-2.33.3 NCEP2-2.93.66.38.62.71.4-6.6-0.3-3.7-12.1-11.5-11.4-4.53.9 Summer Observations12.716.318.520.618.317.613.816.614.712.312.710.415.918.4 CRU ts 3.2213.116.519.221.518.617.713.017.013.910.211.27.913.616.1 CRUTEMP412.917.018.921.918.917.912.816.813.810.9 7.913.6 Era-Interim12.816.318.521.518.017.313.116.314.29.911.39.914.917.8 NCEP212.616.518.921.918.218.112.517.114.19.210.78.514.718.5 Fall Observations0.54.35.99.43.31.5-3.20.6-2.1-7.8-11.2-5.2-0.85.6 CRU ts 3.22-0.34.36.39.33.31.3-3.20.6-3.5-10.2-13.6-8.1-4.14.4 CRUTEMP4-0.14.85.79.53.41.4-3.30.5-3.8-8.6-13.3-6.7-1.1 Era-Interim-0.13.95.69.12.91.3-3.60.2-3.0-9.5-12.0-7.6-4.04.0 NCEP2-0.53.55.69.42.71.2-3.40.4-3.0-9.0-11.2-8.2-3.95.1 Year Observations-0.24.46.29.03.82.0-3.60.6-2.5-7.9-10.6-6.7-1.93.6 CRU ts 3.22-0.94.46.59.03.71.8-3.90.5-4.0-10.6-13.1-9.6-5.22.2 CRUTEMP4-0.84.96.09.23.82.0-4.00.3-4.2-9.0-12.9-8.5-2.6 Era-Interim-0.74.26.19.03.41.9-4.40.4-2.9-10.1-11.5-8.5-4.12.5 NCEP23.65.79.03.11.9-3.80.7-2.9-9.0-10.2-8.4-4.23.1

17. Temperature anomaly Winter (DJF) 2006-2015 CRUTEMP4 CMIP5 Models

18. MATT

19. Changes in the heating costs(%) comparing to ba 1981-1990. (Хлебникова и Саль, 2013) 2001-2010 2050 Региональные изменения дефицита тепла и продолжительности отопительного периода, проекция CMIP5 (Anisimov and Kokorev, 2013)

20. C Прогнозируемые изменения температуры в период вскрытия рек по отношению к 1961-1990. (Anisimov and Kokorev, 2014) 2006-20152016-2025 Схема образования заторных наводнений Разности ожидаемых изменений температуры в устье реки и на различном расстоянии от него для крупных Сибирских рек (SWIPA, 2011) 20502100 Лена ОбьЕнисей 200 600 100 1400 1800200 600 1000 1400 1800 6 4 2 0 -2 6 4 2 0 -2 6 4 2 0 -2 Расстояние от устья, км ΔT, °C

21. 2 5 25 50 75 95 105 125 250 500 Изменения годового стока рек при потеплении на 2°C IPCC WG-2, 2014 Количество людей (млн. чел.), подверженных риску наводнений Частота повторения наводнений к 2080, случавшихся раз в 100 лет в 20 веке. Повторяемость (кол-во лет) Увеличение частотыУменьшение