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Researches on climate change scenarios in Romania

Researches on climate change scenarios in Romania Aristita Busuioc, Alexandru Dumitrescu, Madalina Baciu National Meteorological Administration, Sos. Bucuresti-Ploiesti 97, e-mail: busuioc@meteoromania.ro. Research objective:

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Researches on climate change scenarios in Romania

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  1. Researches on climate change scenarios in Romania Aristita Busuioc, Alexandru Dumitrescu, Madalina Baciu National Meteorological Administration, Sos. Bucuresti-Ploiesti 97, e-mail: busuioc@meteoromania.ro

  2. Research objective: • Development of statistical downscaling models for projection of the global climate change scenarios on Romanian scale (stations) through: - national projects; - EU projects: ENSEMBLES (2004-2009), CECILIA (2006-2009), CC-WaterS (2009-2012); • Adaptation of a high resolution regional climate model (RegCM3,developed by ICTP) centered over Romania: - CECILIA project (2006-2009)

  3. Statistical downscaling models (SDM): • SDM based on canonical correlation analysis (CCA) (Busuioc et al. 1999, 2001, 2006) developed for monthly anomalies: • -temperature: 94 stations (May-October, November-April); • -precipitation : 16 stations in southeastern Romania; • Conditional stochastic model(Busuioc and von Storch, 2003) to construct daily precipitation projections for stations in Romania, generated in 1000 runs– allowing calculation of the ensemble mean for 10 precipitation indices (including 6 extremes) and associated 90% confidence intervals. • SDMs have been applied to ensembles of GCM runs to produce probabilistic projections for the 2030s and 2080s.

  4. Climate change scenarios derived from 8 ENSEMBLES GCMs (stream1:BCM2, FUB(3), ECHAM5(3), INGV) and ARPEGE: 1961-1990, 2001-2099 (A1B); Comparison of the SDM results with those obtained directly from 8 RCMs-25 km (ENSEMBLES):CNRM-ARPEGE, DMI-ARPEGE, DMI-ECHAM5, HadRM3Q0-HadCM3Q0, MPI-REMO-ECHAM5, SMHIRCA-BCM2, SMHIRCA-ECHAM5, KNMI-RACMO2-ECHAM5, RegCM3-ECHAM5

  5. Predictors: NCEP reanalysis over the period 1961-2007 : • Precipitation: SLP-sea level pressure, H500-geopotential heights at 500mb, SH850- specific humidity at 850 mb; • Temperature : temperature at 850 mb : • Main statistical downscalingissues • -the stability of the SDM skill over 3 independent data sets (1961-1980, 1981- 1999, 1991-2007); optimum SDM as average over the 3 SDM versions identified as optimum within the validation process over the 3 independent data sets. • - the SDM capability to reproduce the extreme events after 2000 and changes in the monthly mean over the period 1991-2007 against 1961-1990, with the SDM fitted over 1961-1990; credibility of the SDM for the future scenarios; • - local changes are derived as ensemble average over the outputs of the optimum SDMs applied to more GCM outputs;  estimation of the uncertainties.

  6. SDM performance-temperature: -high and stable skill for the both seasons but higher values for the warm season and for mountain stations (see figure 1); -SDM reproduces well the extreme events: for example, the highest temperature anomalies recorded in summer 2007 (see figure 2) Fig. 1. SDM skill over independent data sets: 81-99: SDM fitted over 1961-1980 and validate over 1981-99; 61-80: SD fitted over 1981-1999 and validated over 1961-1980; 91-07: SDM fitted over 1991-1990, validated over 1991- 2007

  7. SDM skill (explained variance) for monthly temperature (May-October)over the independent interval 1961-1980).

  8. The SDM reproduces very well the temperature trend and extreme events Figure 2: Observed and estimated (through the SDM) temperature anomalies for July over the period 1991-2007, with the SDM fitted over the 1961-1990, at 2 stations: Turnu Magurele (79 % explained variance) and Sinaia 93% explained variance)

  9. PRECIPITATION

  10. Fig. 3. Standardised anomalies of the monthly precipitation anomalies over the independent period 1991-2007, derived directly from observations (black) and indirectly through the SDM (red) for the stations Targoviste (winter) and Ploiesti (summer). The SDM was developed for each season (winter, spring, summer and autumn) considering the standardized monthly anomalies together. The SDM was fitted over the period 1961-1990. Summer months (JJA) Winter months (DJF) The extreme events are generally well captured by the SDM but sometimes they are underestimated; for example, the monthly summer precipitation anomalies in 2007 were very well estimated by the SDM at the Ploiesti station

  11. Tab 2-1. Changes in the monthly precipitation (%) over the period 1991-2007 (XII-V) against 1961-1990, derived directly from observations and indirectly through the SDM fitted over the period 1961-1990. The shaded values in grey show the similar observed and SDM signals). These SDMs are then used in the construction of the scenarios for the future precipitation changes Changes (decreases) for January, February and May are well estimated by the SDMs; these SDMs are more credible when they are applied for the future scenarios.

  12. Tab 2-1. Changes in the monthly precipitation (%) over the period 1991-2007 against 1961-1990, derived directly from observations and indirectly through the SDM fitted over the period 1961-1990. These SDMs are then used in the construction of the scenarios for future precipitation changes: months VI-XI Decreases in June (for almost stations), increases in September and October (all stations, except for Tr. Magurele in October) , as well as some signals in July and august are well estimated by the SDMs; these SDMs are more credible when they are applied for the future scenarios

  13. Winter temperature change, A1B scenario: (2021-2050)-(1961-1990),average over SDMs applied to 8 GCMs

  14. Summer temperature:(2020-2050)-(1961-1990), average of the SDMs applied to 8 GCMs

  15. Winter temperature change: 2070-2099, A1B, average over SDMs applied to 8 GCMs

  16. Summer temperature change: 2070-2099, A1B, average over SDMs applied to 8 GCMs

  17. Changes in monthly temperature at 16 Romanian stations derived through the SDM applied to the ARPEGE simulations, A1B scenario -Highest changes during summer months: around 4oC in July and 5oC in August; -Lowest change in December: around 1.7oC

  18. Summer temp. (2020-2050)-average over 8 ENSEMBLES RCMs

  19. Seasonal temperature change: average over 7 ENSEMBLES RCMs, 2070-2099, A1B Winter Summer

  20. Changes in monthly precipitation (%) derived as average over SDMs applied to 5 ENSEMBLES GCMs (stream 1): 2070-2099, A1B scenario

  21. Seasonal precipitation changes: 2021-2050, A1B, average over 8 ENSEMBLES RCMs Winter Spring Autumn Summer

  22. Seasonal precipitation changes: 2070-2099, A1B, average over 8 ENSEMBLES RCMs

  23. Changes in the frequency of daily precipitation exceeding 15mm/day at the Calarasi station for the period 2070-2099, derived indirectly through the conditional stochastic model applied to various ENSEMBLES GCMs (blue) and direct from observation (red). Red bars-90% confidence intervals.

  24. Conclusions 1) Under A1B emission scenario, the temperature in Romania is projected to increase in the next decades, compared to 1961-1990, with the highest values over the northwestern regions in winter and southern-southwestern regions in summer. The most likely values are: - 2021-2050: about 1-1.5oC in winter and 1.2-1.9 oC in summer - 2070-2099 : about 1.8-3.7 oC in winter and 2.4-4.1 in summer 2) The precipitation over the southeastern analysed region, for the period 2070-2099, is the most likely to decrease in almost all cold months. In October and May is most likely a slightly increase while during summer time the change is not significant. For precipitation the uncertainty is higher. • I

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