Extension of CCI-HYDR climate change scenarios for temperature and wind speed Programme SSD « Science for a Sustainable Development » Based on results CCI-HYDR project for: TECHNICAL REPORT, JANUARY 2009 Project “Klimaatscenario’s voor Vlaanderen” for Instituut voor Natuur- en Bosonderzoek (INBO) Royal Meteorological Institute of Belgium Meteorological Research and Development Department Risk Analysis and Sustainable Development Section CCI-HYDR project Faculty of Engineering Department of Civil Engineering Hydraulics Division
Transcript
Extension of CCI-HYDR climate changescenarios for temperature and wind speed
Programme SSD « Science for a Sustainable Development »
Based on results CCI-HYDR project for:
TECHNICAL REPORT, JANUARY 2009
Project “Klimaatscenario’s voor Vlaanderen” for Instituut voor Natuur- en Bosonderzoek (INBO)
Royal Meteorological Institute of Belgium
Meteorological Research and Development Department
Risk Analysis and Sustainable Development SectionCCI-HYDR project
Faculty of EngineeringDepartment of Civil Engineering
Hydraulics Division
Royal Meteorological Institute of BelgiumMeteorological Research and Development DepartmentRisk Analysis and Sustainable Development SectionAvenue Circulaire, 3BE-1180 Brussels, Belgiumtel. +32 2 3730554fax +32 2 [email protected] www.meteo.be
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without indicating the reference :
Baguis P., Ntegeka V., Willems P., Roulin E., 2009. “Extension of CCI-HYDR climate change scenarios for INBO”, Instituut voor Natuur- en Bosonderzoek (INBO) & Belgian Science Policy – SSD Research Programme, Technical report by K.U.Leuven – Hydraulics Section & Royal Meteorological Institute of Belgium, January 2009, 31 p.
Meteorological Research and Development Department
CCI-HYDR IIb. Climate change scenarios extension for INBO
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1 Introduction During the period from January till December 2008 the CCI-HYDR team was involved in the project on “Climate change scenarios for Flanders” for the Institute for Nature and Forest Research (INBO) of the Authorities of Flanders in Belgium. In that project the CCI-HYDR methodology has been applied to extend the CCI-HYDR climate change scenarios for rainfall and ETo, to scenarios for temperature and wind speed. During that project also a comparison was made with the KNMI’06 climate change scenarios for the Netherlands. This report describes the extensions made in that INBO project of the CCI-HYDR climate change scenarios for temperature and wind speed. It also repeats the CCI-HYDR results for rainfall and ETo using the same style of presentation as the additional temperature and wind speed results.
2 Climate model data Climate change assessment is tightly bound to the modelling of the climate system. Predicting changes of the climate in the future involves in particular the inclusion in the modelling procedure of plausible scenarios about the economic, social and technological evolution of the human society. The reason is that the human activity can have a major impact in the climate system equilibrium through the emission of Green House Gases (GHG’s), which can directly affect the mean air temperature; this in turn may trigger a series of other changes in the climate system that can lead to a new climate state.
The data used in this work come form Regional Climate Model (RCM) and Global Climate Model (GCM) simulations. The corresponding databases used are the PRUDENCE project database, http://prudence.dmi.dk (RCM), and the IPCC AR4 database http://www.ipcc-data.org/ (GCM). The scenarios considered in these climate change experiments are based on the SRES scenarios from IPCC. In Table 1 we provide a brief description of the scenario fundamentals.
Table 1: SRES scenarios. Scenario Description A1
Fast growing economy, new/efficient technologies, population peak around mid-century and decline thereafter. Three storyline subgroups: fossil intensive (A1FI), fossil energy sources (A1T) and balanced use of all sources (A1B).
A2 Heterogeneous world, preservation of local identities, continuous population growth. Economic/technological progress is more fragmented and slower than in other scenarios.
B1 Global population as in A1, services and information society, clean and resource efficient technologies.
B2 Global population as in A2 but slower evolution, intermediate economic development, more diverse evolution in technology than in the A1 and B1 storylines.
Although there are many in common between the two databases, there are some important differences as well, as we can see below.
• RCM database: - High-resolution simulations (at 50 km) from 10 RCM’s: Arpège, HIRHAM,
- Medium/coarse resolution simulations (150-450 km) from 22 GSM’s: BCM2.0, CGCM3 (T47 and T63 resolutions), CNRM/CM3, Mk3.0, ECHAM5-OM, ECHO-G, FGOALS-g1.0, CM2.0, CM2.1, AOM, E-H, E-R, INM/CM3.0, CM4, MIROC3.2 (medium and high resolution), CGCM2.3.2, PCM, CCSM3, HadCM3, HadGEM1.
- More than one run from each model: at least one control and one SRES scenario (A1B, A2, B1) simulation, other scenarios too (1% - 2x and 1% - 4x CO2 concentration).
- Generally much longer time series than in the RCM case, common periods only retained.
The data processed correspond to the following meteorological variables: (1) precipitation and (2) potential evapotranspiration (PET) related variables, which are: long wave net radiation, short wave downward radiation, mean sea level pressure, cloud cover, 2-meter temperature, 10-meter wind speed and humidity of the air.
From the RCM database we analyzed all the variables previously mentioned. The GCM data analyzed concern precipitation only. Furthermore the analysis is local in character, which means that the data processed from each model simulation correspond to the grid point which is the closest to a fixed station (in this case the Ukkel station of the RMI). Only the RCM precipitation data are analyzed at regional scale over Belgium.
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3 Methods used The monthly data curves that will be presented in this report are calculated from the original RCM time series by taking monthly mean values (or monthly aggregated values for precipitation) over the complete control or scenario period. These curves correspond to point data, namely to the model grid point the closest to Ukkel. In the diagrams, the observations will be represented by a think gray continuous line and the simulations by thin blue (for control) or orange (for scenario) continuous lines, and dashed ones as well with other colours to distinguish between the driving GCM’s. The main techniques involving the definition of three scenario levels and the calculation of PET are described below.
3.1. Low, mean and high climate change scenarios As we explained previously, we have many model simulations based on the SRES scenarios, so we can calculate at a given time (month or season in our case) the model perturbations as differences or quotients between the scenario and control values. Differences are calculated in the case of temperature and quotients in all other cases. This leads to a sample of possible perturbations and we can proceed to outlier elimination using standard statistical techniques. Based on the set of the remaining perturbations, we can define the low, mean and high scenario as the lowest, mean and highest value of this set. The outlier filtering method proceeds as follows.
For each month or season, we have a certain number of perturbations, which can be viewed as a “two dimensional” sample: one dimension corresponds to time (month or season) and the other one to the different perturbation values provided by the scenario/control simulation combinations. For a fixed value of the first dimension (time), we look at the sample obtained while the other dimension runs over the available perturbations corresponding to each scenario/control simulation combination. In this sample we calculate the lower and upper quartiles (75th and 25th percentiles) 1Q and 2Q respectively. Then the
inter-quartile range is 12 QQIQ −= . In terms of the above sample variables one defines the lower and
upper outer fences as IQQ ⋅− 31 and IQQ ⋅+ 32 respectively. The dataset extreme outliers are those
values that lie outside of the interval )3,3( 21 IQQIQQ ⋅+⋅− . The low, mean and high scenarios are calculated as the lowest, mean and highest perturbation values inside the interval.
3.2. PET calculation In order to calculate PET we use the Penman formula for the potential evaporation (Penman, 1948; Bultot et al., 1983). This formula makes use of many meteorological variables and is therefore heavier in calculations than other, simpler PET formulas. However, for this reason it is considered as a more accurate way to calculate PET under climate change conditions.
We proceed as follows. Let 0E (mm/day) be the evaporation of a free water surface. Then the Penman equation reads:
,) - )( ( /* 0
0 δγ
εβαγδ
+
⋅++=
euLQE
where:
p
dT
d
LKQ
T
000662.0
)(
-s)-1( *0
*0
=
=
=
γ
εδ
α
The variables in the equation have the following meaning:
Methods used 4
*0Q : total radiation balance (J/(cm
2day))
SK : global solar radiation (J/(cm2day))
*L : net terrestrial radiation (J/(cm2day))
L : vaporization latent heat of water (10-4
J/kg)
γ : psychrometric coefficient (mb/K)
p : mean annual atmospheric pressure (mbar)
u : mean daily wind speed at 2m (km/h)
e−ε : saturation deficit (mbar)
0α : free water surface albedo
The parameters α and β can be determined with evaporation measurements and their values are known for 11 Belgian stations (Bultot et al., 1983).
Now the free water surface albedo can be calculated using the formula: 4.0
0 )07.0-(07.0 IrA+=α
where A is the albedo of the free water surface under clear sky and Ir is the relative insulation, while the net terrestrial radiation *L is given by the Monteith formula (Monteith, 1973):
)))-1( 1()(-1( 24* IrcebaTL ++= σ
In this equation, σ is the Stefan-Botzmann constant, e the water vapour pressure and T the air temperature in degrees Kelvin. The parameters a , b and c can be determined by measurements on radiation variables (Bultot et al., 1983). We can use the Monteith formula in two ways: either with daily mean values of the variables, or with mean values for the sunlight period of the day.
There are other alternatives to calculate the net terrestrial radiation *L appearing in the Penman formula for potential evaporation. For the needs of the project we opted for two more approaches: the Idso formula for the effective clear sky emissivity (Idso, 1981; Roulin et al., 1996) and the model output for the net terrestrial radiation. The Idso formula is considered more robust under a different climate and with it the formula for the net terrestrial radiation becomes:
( )clTL ⋅−−−= )1(1 * 004 εεσ
with
+=T
ceba 1
110 expε
The parameters in this equation have the values: 70.01 =a , 51 1095.5 −×=b and 15001 =c . The
variable cl represents the cloud amount and can take the following values: 0.0 for clear sky, 0.79 for overcast conditions/low clouds and 0.21 for intermediate cloud covering conditions (Roulin et al 1996).
With the previous setup, the potential evapotranspiration iPET (mm/day) of a natural cover i is given by:
.0ii EfPET =
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In this equation, f is a transfer coefficient given by:
gie
giei
QLK
QLKf
--s)-1(
--s)-1(*
0
*
α
α=
where iα denotes the albedo of the natural cover surface and giQ is the amount of heat exchanged
between the natural cover and the ground. Also, *eL is the same as *L , but calculated over the
sunshine period.
From the previous analysis it becomes evident that the calculation of evapotranspiration from the output of climatic model simulations, involves the following variables (in parentheses are the code names in the PRUDENCE database hosted by DMI):
• Mean Sea Level Pressure (MSLP) • Total radiation balance (Swdown) • Cloud covering (clcov) • 2-meter temperature (t2m) • 10-meter wind (w10m) • Humidity
Using the RCM data for all the variables mentioned above, we have calculated the PET for all control and scenario simulations in the PRUDENCE database with the four methods outlined previously (based on two variants of the Monteith formula, the Idso formula and the model output for the terrestrial radiation).
Wind 6
4 Wind The wind speed variable provided by the models output is calculated at 10 meters above the ground. The PET formula though requires the wind speed at 2 meters, so a transformation has been performed to produce the appropriate values.
Because of this fact and the observation conditions of wind at the Ukkel station (many physical obstacles around), one should not expect any meaningful comparison between the model output and observations. Indeed, this can be seen very clearly in Figure 1, where the RCM simulation results are plotted against the wind measurements at Ukkel.
Figure 1: Wind speed curves from RCM simulations (at 10 m) and observations at Ukkel (at 2 m).
Although there is no comparison between the two from a bias and mean error point of view, we observe that for the most part the models reproduce the general observed wind speed pattern throughout the year (decrease in summer). So, no model validation is possible for this variable. However it is still possible to explore the climate change signal for the wind speed by calculating the corresponding perturbations. The results are presented in Figure 2 and Figure 3, and in Table 2. A quick look at the corresponding graphs reveals rather small changes in the wind speed for the scenario period. In particular, from Table 2 we see that when all the SRES scenarios are taken into account, the projected wind speed perturbations are generally between 0.8 and 1.2, depending on the scenario level. This means that there is no clear climate change signal for the wind speed.
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Figure 2: SRES scenarios for the wind speed.
Figure 3: Wind speed perturbations from RCM data.
Wind 8
Table 2: Wind perturbations from RCM data. Wind perturbations (A2
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5 Temperature Model control curves together with temperature observations at Ukkel for the period 1961-1990 are shown in Figure 4. The observations are represented by the thick gray line while the colored lines correspond to RCM control simulations classified by driving GCM. We observe a good agreement between models and observations, despite some bias during May, October and the winter months. We can see the corresponding curves under climate change conditions in Figure 5; in the same figure, the mean of all scenario simulations is plotted against the observations and the mean of all control simulations. The scenarios A2 and B2 are shown separately and as we can see in the corresponding figures, a significant temperature increase is expected for the end of the century, stronger in the A2 case than in the B2 one. The climate change signal obtained as temperature perturbation from these data, is graphically represented in Figure 6. Every season is affected by the temperature increase which appears however to be stronger during the summer months.
Figure 4: Observed and modeled temperature curves organized according to driving GCM.
It is obvious from Figure 5, Figure 6 and Table 3 that the temperature increase in the scenario period 2071-2100 is expected to be quite large according to the model simulations. Increase is predicted even in the low scenario of the B2 family of simulations, which produces the mildest changes of all (between 0.726 and 3.062 degrees). The worst-case scenario (high scenario) on the other hand predicts more than 8 degrees increase in August. However, the uncertainty in the predictions varies greatly from one month to the other. For example, in January the temperature perturbations lie between 1.508 and 4.233 degrees (range 2.725 degrees), while in August between 2.81 and 8.892 (range 6.082 degrees). This complicates even further the details of the assessment, however the climate change signal for the mean temperature is quite clear and it is summarized to a significant increase in every month of the year.
Temperature 10
Figure 5: SRES scenarios for temperature.
Figure 6: Temperature perturbations from RCM data.
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Table 3: Temperature perturbations from RCM data. Temp. perturbations (A2
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Another interesting aspect of temperature change under a different climate is how certain temperature percentiles change too. In particular we calculated the projected changes in the 10th and 90th temperature percentiles as seasonal low, mean and high perturbations like in the case of the other variables considered in the project. These percentile changes will provide estimation of what kind of change one should expect near the minimum and maximum of the temperatures time series. The results are presented in Figure 7 and Figure 8 and explicitly in Table 4. Again, like in the case of mean temperature we analyzed previously, an increase is expected at every scenario level in all cases. For the 10th percentile, the most significant increase is expected in winter and autumn. The consequence of such a change would be a drastic decrease in the number of days with temperatures below zero. Similarly, for the 90th percentile, the biggest increase is expected in summer. A direct consequence of such an increase would be summers much hotter than today, from the point of view of the maximum daily temperatures.
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6 Potential evapotranspiration As we explained in the beginning of the report, we have four ways to produce output for the net terrestrial radiation and consequently equal number of ways to compute PET. In particular we have four groups of control results for PET, as in Figure 9.
Figure 9: Control curves for PET from RCM data organized according to calculation method and driving GCM.
It is worth noting that there is a noticeable positive bias in the results of every PET calculation method. However, the correlation between the curve calculated from observations and the control curves is very high. We also observe that the highest bias in almost every month and every calculation method is consistently produced by the ECHAM5-driven simulation. Only one PET curve is lying below the observations-based curve, thus producing a negative bias, as we can see in Figure 9. Same kind of PET diagrams under the conditions of SRES scenarios we see in Figure 10, while the corresponding perturbation diagrams are presented in Figure 11.
We observe that in all cases of PET calculation techniques, a shift to higher values is expected throughout the year. This is clearly seen in Figure 11, which further shows graphically the uncertainty due to the method of calculation. Indeed, the two Monteith variants closely follow each other and produce higher perturbations during the autumn and winter months than the two other methods. Accordingly, the PET perturbations produced by the use of the Idso formula and the model output for the net terrestrial radiation are also quite similar. This grouping of the four possible PET perturbations by two according to their agreement in every month is explicitly presented in Table 5, in terms of their precise numerical values. According to these results, the effect of the climate change in the end of the century on PET can be summarized by an increase even in the low scenario. The increase is clearer in the mean scenario, in which case every perturbation is equal or exceeds the value 1, while
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the higher perturbation values are produced by the two Monteith variants during winter. The best agreement between the four methods is observed in spring and summer for each scenario level.
Figure 10: Scenario curves for the four PET calculation methods.
Figure 11: Perturbations for all the PET calculation methods used.
Potential evapotranspiration 16
Table 5: Monthly PET perturbations from RCM data and for each calculation method.
PET perturbations from RCM data
Monteith 24h Monteith daylight Idso LWnet
Month Low Mean High Low Mean High Low Mean High Low Mean High
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7 Precipitation Precipitation data have been analyzed more extensively than the other variables in two ways: first by including in the analysis both RCM and GCM data and second, by extending the RCM data processing over the whole Belgium. We start with the control curves by both RCM and GCM data plotted against observations in Figure 12.
Figure 12: Control precipitation curves from RCM and GCM simulations.
There are two main characteristics of the precipitation simulation results that are visible immediately in Figure 12: (1) all the simulations (in both RCM and GCM cases) deviate significantly from the observations, while the GCM simulations exhibit a larger dispersion than the RCM ones; and (2) while we observe negative and positive bias throughout the year, almost all RCM and GCM simulations exhibit a positive bias during the winter months. Also there are two “outliers”, one from the RCM and one from the GCM set, which are candidates for rejection.
As far as the SRES scenarios are now concerned, the only in common between the RCM and GCM sets is the A2 one, so in what follows we will present results for the A2 scenario alone or all scenarios considered together.
Figure 13: Scenario precipitation curves from RCM and GCM simulations.
The scenario curves in Figure 13 reveal a significant shift of the precipitation pattern at Ukkel towards drier summers and rainier winters. This observation holds for both RCM and GCM simulations. However, inspecting more carefully Figure 13 we realize that there is an important difference between the RCM and the GCM simulation sets: while in the RCM case almost all the simulations lie below the baseline (observations at Ukkel for the period 1961-1990) during summer, in the GCM case there are many of them above it. This could have an impact in the perturbations one can calculate based on these data, and this is indeed the case as we see in Figure 14. In the RCM case, even the high perturbation lies below the value 1 in summer (A2 scenario or all SRES scenarios together), while in the GCM case the high scenario clearly exceeds the value 1, meaning a significant
Precipitation 18
increase in precipitation (see also Table 6). In summary, the inclusion of more GCM simulations and SRES scenarios may lead to a significantly different picture in what concerns the precipitation regime over central Belgium in a future climate.
Figure 14: Precipitation perturbations from RCM and GCM data.
Table 6: Monthly precipitation perturbations from RCM and GCM data.
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Concerning know the RCM precipitation data processing at regional scale over Belgium, we originally processed the model data individually using the intersection of the grid of each model with the Belgian territory. Since there are many different model grids, this kind of data processing has been proved inefficient in estimating the climate change signal over Belgium. An example is presented in Figure 15.
Figure 15: Sample of regional perturbations with 3 different RCMs.
We immediately observe that there are two difficulties: (1) staying within the same RCM, it is not clear in which way the whole country is affected, and (2) the regional pattern of the climate change can be quite different as we change the combination of the control/scenario simulations used to calculate the perturbations. The approach adopted to solve these problems was to project all the RCM data on a common and high-resolution grid (7 km) and then calculate the perturbations based on the data collected over each cell of the new grid. We present here the results for the two hydrological seasons.
Figure 17: Precipitation perturbations: hydrological winter. Summarizing the content of Figure 16 and Figure 17, we observe that the regional climate change pattern is generally similar to the signal obtained in point data processing (Figure 14 – RCM results); however it is combined with some regional differentiation. In particular, according to the RCM simulations, a significant precipitation decrease is expected during summer almost everywhere in Belgium for the period 2071-2100. However, if the high scenario holds true, then we should expect unchanged or even slightly increased precipitation in the coast. Similarly for the winter, an increase of precipitation is predicted almost everywhere in Belgium. Again, in every scenario level the increase is expected to be more significant along the coast.
High Mean Low
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8 Hydrological extremes The perturbations in the previous results are given mainly for the mean monthly values. The perturbations, however, might depend on the time scale (aggregation time) and return period (mean time between two successive exceedances of a given perturbation). In the CCI-HYDR project, this dependency on time scale and return period has been investigated for precipitation and potential evapotranspiration (ETo). The dependence on return period is studied because of the project’s focus on hydrological extremes. Detailed investigations were primarily carried out for the two key hydrological variables: ETo and precipitation. However, temperature and wind speed were also studied as elaborated in the previous sections.
Percentage changes were calculated by comparing the control (1961-1990) runs with the scenarios (2071-2100).Table 7 gives an overview of the average seasonal changes of the extremes for 2085 (2071-2100) relative to 1975 (1961-1990). For clarity, the percentages were calculated by averaging the changes for extreme events for a return periods ranging from 0.1 to 30 years. Indeed, each return period has a specific impact; however initial studies revealed that those changes are somewhat constant for a range of return periods higher than a particular threshold. The PRUDENCE runs are available for 30 years hence the highest return period that can be directly estimated is 30 years. The changes are also based on half-year seasons: winter (October to March) and summer (May to September). The high, mean and low changes represent the expected changes based on the several PRUDENCE runs. Thus, they can be interpreted as measures for the uncertainty. In addition to the PRUDENCE A2 and B2, the precipitation changes include the A1B and B1 scenarios from the AR4 GCMs while the ETo changes only include the A2 and B2 scenarios.
Table 7: CCI-HYDR Precipitation and ETo seasonal percentage changes for all quantiles above 0.1 years to 30 years return period.
The ranges provided in Table 7 for the seasonal precipitation means imply that the winter season will generally get wetter while the summer season will get drier. The number of wet days will probably reduce in summer because there is a change in impact from positive to negative; the mean daily changes are positive while the mean seasonal changes are negative. This indicates a reduced seasonal volume due to an increase in the frequency of dry days. During winter there is no significant change but the slight increase signals a slight increase in frequency of wet days. The potential evapotranspiration will increase in both seasons as both seasons show positive changes. The increase is observed for both seasonal means, but the change is higher during winter from the daily to seasonal mean changes. This suggests that the future winters will get warmer compared to the summers.
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Table 8 lists the wet day frequency percentage changes calculated based on only the PRUDENCE A2 and B2 daily data. Similar seasonal conclusions can be made by observing the wet day frequencies. The winter season (DJF) changes indicate that future winters around 2085 will be wetter while the summer (JJA) will get drier; the mean seasonal changes are more positive.
Table 8. Wet day frequency changes for Belgium based on the PRUDENCE RCMs.
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9 High, mean and low impact scenarios The CCI-HYDR project studied the available climate scenarios for Belgium to understand better the climate change characteristics for the future. However without tailored scenarios, the use of the scenarios would be limited to rough estimates of the possible changes. Even so, the large set of models implied that the interpretations would be difficult for the impact assessments. Therefore, three scenarios were proposed which would simplify the interpretation and at the same time account for the overall uncertainty from the selected models. The three scenarios were derived through a statistical downscaling method that involved the transfer of the changes estimated from the climate models to an observed time series. The changes included the number of wet days and the intensities of the wet days for precipitation and the intensity changes for ETo. Based on the entire set of the models the high, mean and low scenario cases were derived to represent the overall expected range. The high scenario can be interpreted as being a wet scenario with high peaks while the low scenario is particularly relevant for low flows. Table 9 shows the derived combinations for ETo and precipitation. The high, mean and low represent the maximum, average and minimum changes that were estimated from the PRUDENCE RCMs. The scenarios were developed after a seasonal correlation analysis of the changes for ETo and precipitation. More details about the correlations can be found in the CCI-HYDR Technical Report II “Study of climate change scenarios” (see project website: http://www.kuleuven.be/hydr/CCI-HYDR.htm). It was also assumed that the highest flood impact would result from the highest positive changes for the winter season; hence the high scenario has high changes during the winter season. A possible confusion that is worth noting is to assume that the high impact scenario means high precipitation amounts for all the seasons. This is not the case; the high impact scenario has low precipitation amounts (changes) in summer with autumn and winter having mean precipitation changes. Another case in point is that the high Impact scenario and the low impact scenario have the same low precipitation changes during summer. Therefore, to avoid misleading conclusions, the scenarios should be interpreted as hydrological impact scenarios which are an end result of the combination of both ETo and precipitation. For clarity, the scenarios could be denoted as wet, mild and dry instead of high, mean and low as illustrated in Figure 18. This is particularly necessary in case of other applications. For instance, for water quality studies, the low scenario (dry) would have the highest impact; thus the low CCI-HYDR scenario would instead be high scenario.
Table 9: CCI-HYDR impact scenarios and the related changes in precipitation, temperature, ETo and wind. Season ETo/Temperature Precipitation/Wind Scenario Winter high high High/Wet Spring mean mean Summer high low Autumn mean mean Winter mean mean Mean/Mild Spring mean mean Summer mean mean Autumn mean mean Winter Low Low Low/Dry Spring Low Mean Summer Low Low Autumn Low Mean
Figure 18: Relevance and interpretation of the CCI-HYDR scenarios. The common belief that the mean scenario is the best guess for future changes is misleading. It is desirable to use all the three scenarios as they explain the overall uncertainty range of the models. Based on the statistically probed scenarios, the CCI-HYDR project provides scenarios which are constructed specifically for the Belgian climate. Other variables like the temperature and wind were derived through a correlation analysis with precipitation. More information about the CCI-HYDR project can be found on the project website: http://www.kuleuven.be/hydr/CCI-HYDR.htm. It is interesting to note that a climate change tool (CCI-HYDR Perturbation Tool) is available from the website. The tool enables end users to construct their own scenarios from observational data.
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10 CCI-HYDR and the KNMI’06 scenarios The KNMI’06 scenarios are mainly used in the Netherlands for estimating the future changes in precipitation and temperature. Since the Netherlands is in the vicinity of Belgium, it is practical to ascertain that the scenarios are within the range of CCI-HYDR Belgium scenarios. The main outputs for the CCI-HYDR scenarios were ETo and precipitation while the KNMI’06 scenarios provided temperature and precipitation. The KNMI’06 scenarios considered changes in circulation and temperature while the CCI-HYDR scenarios focused on the ranges of hydrological impacts from the possible precipitation and ETo correlations. To elaborate further, the precipitation scenarios in the CCI-HYDR scenarios were derived from a hydrological impact perspective while the precipitation in the KNMI’06 scenarios was derived from a climatological perspective. Figure 19 provides an overview of the CCI-HYDR and KNMI scenarios. From an impact perspective, the W KNMI’06 scenario would be a high scenario because of high precipitation amounts for both winter and summer although the W+ would also need to be checked especially due to high precipitation amounts during winter. The plus and minus signs in Figure 19 are only meant for a general comparison.
G+ Win -,Sum-
W+ Win++,Sum--
G Win--,Sum +
W Win +, Sum ++
+ 2ºC + 1ºC
Stro
ng
Wea
k
Air circulation High/Wet Win++,Sum--
Mean/Mild Win,Sum
Low/Dry Win--,Sum--
Hydrological Im
pact Floods
Low flows
CCI-HYDR KNMI’06
Figure 19: CCI-HYDR scenarios (left) and the KNMI’06 scenarios (right). The winter (Win) and summer (Sum) changes (+,-, ++, --) show the wet (+) or dry conditions (-). To examine the differences, it was assumed that the ETo changes are similar to the temperature changes. Figure 20 shows the estimated changes for the precipitation and temperature for the period around 2085 (2071-2100) relative to 1975 (1961-1990). Both scenarios are climatically consistent; they indicate a positive correlation between precipitation and temperature during winter and a negative correlation during summer. During summer, the CCI-HYDR changes show reductions while the KNMI’06 scenario changes show both an increase and a decrease in seasonal values. During winter, however, all scenarios show a positive change for winter with the exception of the low CCI-HYDR scenario which shows a slight reduction. The W+ scenario high precipitation changes during winter are similar to the CCI-HYDR high changes. The W+ summer changes are also the lowest which is comparable to low CCI-HYDR changes. The summer differences may arise from the fact that there exists a north to south temperature gradient during summer with the south experiencing warmer conditions than the north. An increase in temperature during the summer season signifies a reduction in precipitation. The low CCI-HYDR scenario during summer may also be explained by the excessive drying models; KNMI used a weighting method to reduce the influence of the drying in the models. Figure 21 and Figure 22 show the temperature for two PRUDENCE RCMs (HIRHAM, RCAO) and a GCM (HadAM3H). For both RCMs, the north to south temperature gradient is clear
CCI-HYDR and KNMI’06 scenarios 26
during summer. Belgium appears to be warmer than the Netherlands. During winter, the differences in temperature between Belgium and the Netherlands are not clear from Figure 22. Thus, the differences in the precipitation range during winter may be explained by other factors. The differences in scenario construction may partly explain the range. The weighting and discarding of models is a possible difference that needs further investigations.
CCI-HYDR and KNMI winter
W+
WG+
G
High
Mean
Low
-10
0
10
20
30
40
50
1 2 3 4 5
Temperature change [ºC]
Prec
ipita
tion
chan
ge [%
]
KNMI CCI-HYDR
CCI-HYDR and KNMI summer
G
G+
W
W+
Low
Mean
High
-50
-40
-30
-20
-10
0
10
20
1 2 3 4 5 6 7 8
Temperature change [ºC]
Prec
ipita
tion
chan
ge [%
]
KNMI CCI-HYDR
Figure 20: The CCI-HYDR and KNMI’06 seasonal changes for precipitation and temperature (from the reference period 1961-1990 till the future period 2071-2100; the 3 CCI-HYDR scenarios are based on Uccle; the 4 KNMI’06 scenario’s are based on 13 stations in The Netherlands) for winter (DJF) and summer (JJA).
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Figure 21: Europe temperature changes for 2071-2100 during summer. Belgium (bottom of encircled region) and the Netherlands (top of the region). Source: PRUDENCE project.
Figure 22: Europe temperature changes for 2071-2100 during winter. Belgium (bottom of encircled region) and the Netherlands (top of the region). Source: PRUDENCE project.
Hydrological impact case example 28
11 Hydrological impact case example The comparison of other precipitation statistics sheds some light on the possible hydrological impacts of using the two scenarios. This is necessary as the CCI-HYDR scenarios were tailored for Belgian catchments. It’s also crucial to stress that there is a difference between a high change and a high scenario from the CCI-HYDR perspective. For instance, Figure 20 looks at the changes and not scenarios. A high scenario for the CCI-HYDR implies that the flood impact generated from using both precipitation and ETo will be high even though some of the precipitation changes in some months may be low. Table 10 and Figure 23 illustrate this point because the summer changes for the high scenario and the low scenario are similar. Table 10 gives an overview of the daily precipitation differences for the KNMI’06 and CCI-HYDR scenarios between 2050 (2036-2065) and 1990 (1976-2005) periods. It is observed that the CCI-HYDR scenarios are drier than the KNMI’06 scenarios during summer (mean precipitation). All KNMI’06 scenarios project an increase in precipitation during winter while the low CCI-HYDR scenario projects a possible reduction in precipitation. The high and low CCI-HYDR scenario precipitations are more extreme probably due to the weighting of the similar models in the KNMI’06 scenarios.
Table 10: Daily precipitation changes for the KNMI’06 and CCI-HYDR scenarios between 2050 (2036-2065) and 1990 (1976-2005) periods.
KNMI, G
KNMI, G+
KNMI, W
KNMI, W+
CCI-HYDR, High
CCI-HYDR, Mean
CCI-HYDR, Low
Winter (%) Mean precipitation 3.5 6.7 7.1 14.1 44.2 9.9 -13.8 Wet day frequency 0.1 0.9 0.3 1.9 8.0 1.7 -8.7 Mean precipitation on a wet day 3.4 5.8 6.9 12.0 33.5 8.1 -5.7 Precipitation on 1% wettest days 4.3 5.6 8.5 11.3 42.0 5.6 -20.1 Summer (%) Mean precipitation 3.4 -9.1 6.2 -18.8 -43.6 -5.2 -43.6 Wet day frequency -1.7 -9.5 -3.3 -19.2 -30.0 -13.9 -30.0 Mean precipitation on a wet day 5.1 0.5 9.9 0.5 -19.6 10.1 -19.6 Precipitation on 1% wettest days 5.7 -7.1 17.4 -13.4 -46.4 6.1 -46.4
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Figure 23: Mean and extreme statistics for the CCI-HYDR and KNMI scenarios for winter (top) and summer (bottom) seasons.
To check whether the impact derived from the scenarios would differ significantly a hydrological case was investigated. Using a previously calibrated lumped conceptual rainfall-runoff model (VHM model) for the Molenbeek Erpe-Mere catchment, discharges were estimated for around 1990 to 2100. The VHM model which requires ETo and precipitation as input. The KNMI’06 and CCI-HYDR scenarios were derived for the Beitem belgian station and used as input to the hydrological model. Since the KNMI’06 scenarios do not provide estimates for ETo, the CCI-HYDR ETo changes for 2100 were used. ETo was estimated from the observed uccle historical ETo series. Figure 24 shows the impact generated from the scenarios. The high CCI-HYDR scenario impact is higher than the highest KNMI’06 scenario while the low CCI-HYDR scenario is lower than the lowest KNMI’06 scenario. The high difference is explained by the high precipitation amounts for the CCI-HYDR scenario especially during winter while the low impacts are explained by the low precipitations during summer for the CCI-HYDR scenarios. It would have been concluded a priori that the CCI-HYDR high impact would have been much higher if one had only looked at the mean rainfall statistics in Figure 23. There is a need to be cautious when looking at mean statistics to predict the impact on extremes. Mean statistics provide a general assessment of a given time series but fail to account for the relative changes of the quantiles within the time series. It is notable that both the CCI-HYDR and KNMI’06 used the same pool of models: PRUDENCE RCMs and AR4 GCMs, albeit with varying statistical methods. The CCI-HYDR scenarios were based on a quantile-perturbation technique where each quantile was explicitly changed by multiplying it by a unique factor. For the KNMI’06 scenarios, the quantiles changes were not explicitly applied.
Figure 24: Observed (1990) and future (2100) discharges for the KNMI’06 and CCI-HYDR scenarios for the Molenbeek Erpe-Mere catchment.
The CCI-HYDR and KNMI’06 comparison has demonstrated that the range of expected change is not certain and that it depends on the assessment and selection of the climate models. It is not clear which of the two scenarios is better as there is no possibility of ascertaining the realism of the two scenarios. However, similar conclusions may be made about the seasonal changes in Belgium. In winter the mean precipitation will increase while the summer mean precipitation will decrease. The complementarity of the two scenarios has been a positive outcome of the comparison. The KNMI’06 scenarios provide a climatological explanation of the wet and dry atmospheric conditions while the CCI-HYDR scenarios provide other possible scenarios based on the impact perspective. Future KNMI and Belgian scenarios would find the results useful.
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12 References CCI-HYDR project reports and papers: Ntegeka V., Willems P., Baguis P., Roulin E., 2008. “Climate change impact on hydrological extremes along rivers and urban drainage systems. Summary report Phase 1: Literature review and development of climate change scenarios”, K.U.Leuven – Hydraulics Section & Royal Meteorological Institute of Belgium, April 2008, 64 p. Baguis P., Boukhris O., Ntegeka V., Roulin E., Willems P., Demarée G., 2008. “Climate change impact on hydrological extremes along rivers and urban drainage systems. I. Literature review”, Technical report, K.U.Leuven – Hydraulics Section & Royal Meteorological Institute of Belgium, May 2008, 57 p. Ntegeka V., Baguis P., Boukhris O., Willems P., Roulin E., 2008. “Climate change impact on hydrological extremes along rivers and urban drainage systems. II. Study of rainfall and ETo climate change scenarios”, Technical report, K.U.Leuven – Hydraulics Section & Royal Meteorological Institute of Belgium, May 2008, 112 p. Ntegeka V., Willems P., 2008. “Climate change impact on hydrological extremes along rivers and urban drainage systems. III. Statistical analysis of historical rainfall, ETo and river flow series trends and cycles”, Technical report, K.U.Leuven – Hydraulics Section & Royal Meteorological Institute of Belgium, May 2008, 37 p. Ntegeka V., Willems P., 2009. “Climate change impact on hydrological extremes along rivers and urban drainage systems. Perturbation tool”, Manual version January 2009, K.U.Leuven – Hydraulics Section & Royal Meteorological Institute of Belgium, 7 p. Ntegeka, V., Willems P., 2008. “Trends and multidecadal oscillations in rainfall extremes, based on a more than 100-year time series of 10 min rainfall intensities at Uccle, Belgium”, Water Resour. Res., 44, W07402, doi:10.1029/2007WR006471. Other references: Bultot F., Coppens A., Dupriez G., 1983. “Estimation de l’évapotranspiration potentielle en Belgique”. Publications/publicaties série/serie A No 112 Royal Meteorol Inst Belg.
Idso S.B., 1981. “A set of equations for full spectrum and 8- to 14 m and 10.5- to 12.5 m thermal radiation from cloudless skies”. Water Resour. Res. 17, 295-304.
Monteith J.L., 1973. “Principles of Environmental Physics”. Contemp Biol Ser, Edward Arnold Ltd, London.
Penman H.L., 1948. “Natural evaporation from open water, bare soil and grass”. Proc R Soc Lond A193, 120-146.
Roulin E., Gellens-Meulenberghs F., Gosset J., 1996. “Operational assessment of surface radiative fluxes over Belgium by means of Meteosat PDUS and meteorological data”. In: Parlow E. (Ed.) Progress in Environmental Remote Sensing Research and Applications, Balkema, Rotterdam, 409-416.
CCI-HYDR Perturbation ToolA climate change tool for generating perturbed time series for the Belgian climate
Programme SSD « Science for a Sustainable Development »
MANUAL, JANUARY 2009
CCI-HYDR project (contract SD/CP/03A) for:
Royal Meteorological Institute of Belgium
Meteorological Research and Development Department
Risk Analysis and Sustainable Development SectionCCI-HYDR project
Faculty of EngineeringDepartment of Civil Engineering
Hydraulics Division
Royal Meteorological Institute of BelgiumMeteorological Research and Development DepartmentRisk Analysis and Sustainable Development SectionAvenue Circulaire, 3BE-1180 Brussels, Belgiumtel. +32 2 3730554fax +32 2 [email protected] www.meteo.be
