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Transcript
M. Trnka (1,2), M. Dubrovský (1,2), P. Hlavinka (1), D. Semerádová (1,2), P. Štěpánek (3), J.Eitzinger (4), H. Formayer (4), Z. Žalud (1,2)
POSTER:
XY 0229
1) Institute of Agrosystems and Bioclimatology, Mendel University of Agriculture and Forestry, Brno, Czech Republic, [email protected]
(2) Institute of Atmospheric Physics, Czech Academy of Sciences, Prague
(3) Czech Hydrometeorological Institute, Czech Republic
(4) Institute for Meteorology, University of Natural Resources and Applied Life Sciences (BOKU), Vienna, Austria
AIMS
Figure 1:. Overview
of the area
orography and
location of weather
stations used in the
study.
CROP MODEL EVALUATION
Figure 2:. Results of the CERES-Wheat and CERES-Barley simulations
compared with the observed values.
SRES & SENSITIVITY
SPATIAL ANALYSIS
Figure 3. Methodology of the spatial analysis showing applied digital
elevation model, weather stations, soil and land-use maps.
GCM SELECTION
Figure 6: Mean sowing date of winter wheat under present climate compared with the expected sowing dates by 2050 (SRES A2 – high climate
sensitivity). The Figure documents high uncertainty caused by the differences between three used GCMs. The weather conditions based on the
HadCM are characterized by much higher soil temperature during August and September while NCAR model estimated significantly lower
precipitation during September and October (Fig 5). Both projections are conducive to latter sowing dates.
Figure 7: Mean maturity date of wheat under the present climate compared with the expected maturity date by 2050 (SRES A2 – high climate
sensitivity). The maps document high uncertainty caused by the differences between three used GCMs. The differences between the GCMs are even
more obvious when we compare the intra-model variability in the sowing date. The weather patterns in HadCM are characterized by high temperature
increases leading to shorter developmental time. The NCAR model estimates higher precipitation during spring combined with decrease of solar
radiation, which results into event longer developmental time than under the present climate.
Indirect effect of climate change – CO2 influence not accounted for
Comined effect of climate change – CO2 influence accounted for
Figure 8: Estimated impact of climate change on the winter wheat yields in the Czech Republic. The figures show two main sources of uncertainties in the analysis i.e. differences between individual GCM and also
the effect of CO2 which is responsible for the largest portion of the uncertainty. In the CERES models increase of the ambient CO2 concentration leads to 9-10% yield increase per 100 ppm increase of CO2 (under
the Central European climate conditions). However the magnitude of the increase remains to be confirmed by ongoing FACE experiments.
Cold region Warm region
Figure 10: Estimated impact of climate change on the crop yields in 77 administrative
districts (see Fig. 3. Landuse and stat. Data ) and the whole territory as a function of
different SRES scenarios (top) and GCM (bottom). The regions are ordered according
to the mean air temperature in the district under the present climate.
Changes of the overall agroclimatic condtions
The study region is centered in the Czech Republic but part
of the analysis included also north-east part of Austria. In
total 129 weather stations were available together with
detail soil and land-use information.
Uncertainty of the scale
STUDY AREA
STEPS OF THE STUDY
Changes in the phenology
1) To analyze sources of uncertainty in the estimates of future cereal productivity in the Central Europe with
the focus on role of the emission scenarios (SRES), climate sensitivity and global circulation models (GCM).
2) To take into account the effects of the carbon dioxide concentration on the crop productivity as well as the
factor of the scale for which the results are integrated.
3) To estimate the expected impact of the climate change on the future cereal productivity as well as on the
overall agroclimatic conditions in the area.
This study was conducted with support of the Czech National
Agency for the Agricultural Research (project QG60051), 6th
FP EU project Adagio (Adaptation of Agriculture in European
Regions at Environmental Risk under Climate Change)
SSPE-CT-2006-044210 and the research plan No.
MSM6215648905 “Biological and technological aspects of
sustainability of controlled ecosystems...“.
References: with the authors
RESULTS
Changes in the yield
Czech Republic
Projection of uncertainties of the climate change scenarios into the estimates of future
agrometeorological conditions and crop yields?
Figure :9 Uncertainty in the climate change impacts on the example of wheat yields for time
frames centered around 2020 and 2050. The uncertainty is given by the differences in the used
SRES scenario and climate sensitivity. The top maps represent model runs for SRES A2 and
high climate sensitivity. The bottom maps represent simulation for SRES B1 and low climate
sensitivity. Maps depict yield deviation from 1961-1990 means.
PRESENT
Figure 11: Distribution of the production region
under the present climate (above) based on
1961-1990 climate conditions. On the right:
Estimated area of the production regions
according under expected climate conditions for
the time slice centered around 2020 (a,c,e) and
2050 (b,d,f). The Figures a) and b) are based on
the HadCM SRES B1 with low climate sensitivity;
the figures c) and d) on the HadCM SRES A2
with high climate sensitivity and the figures e)
and f) are based on the NCAR SRES A2 with
high climate sensitivity
CONCLUSIONS
QUESTIONS?
The range of uncertainty caused by the different projections within the set of used GCM is relatively large and is most pronounced in case of A2 SRES
scenario in combination with the high climate system sensitivity.
Overall uncertainty of the estimates of the future cereal productivity is rather high (larger than 20% between individual GCM for 2050) and becomes
even higher in case of spring cereals (compared to winter cereals)..
The effect of uncertainty within the available set of GCM-SRES-CS on the future national production levels is of the same magnitude as the effect of
sub-regional differences. The overall uncertainty decreases with the level of integration.
Figure 4:. Dynamics of CO2 increase and sensitivity of the
climate system to this change.
Figure 5:. Expected changes of global radiation (SRAD), mean tempearture (TAVG) and
precipitation (PREC) according to SRES A2 and SRES B1 at 2050 for the Czech Rep.
MIROSLAV TRNKA
The sensitivity of agrometeorological indicators to SRES-GCM-CS combination showed similar patterns as crop yields and some aspects of these
[email protected]
changes especially their speed should be a reason for concern.