Sensitivity of runoff and projected changes in runoff ...

Open Access Article

Meteorologische Zeitschrift, Vol. 19, No. 3, 225-236 (June 2010) c by Gebrüder Borntraeger 2010

Sensitivity of runoff and projected changes in runoff over Quebec to the update interval of lateral boundary conditions in the Canadian RCM A NNE F RIGON1 ∗ , B ILJANA M USIC1 and M ICHEL S LIVITZKY1,2 1 Ouranos

Consortium on Regional Climate and Adaptation to Climate Change, Montreal, Canada Montreal, Canada

2 Ouranos/INRS-ETE,

(Manuscript received October 23, 2009 revised form May 2, 2010, accepted May 2, 2010)

Abstract An analysis was carried out of the sensitivity of runoff simulations from version 4.2 of the Canadian Regional Climate Model (CRCM) to the frequency of lateral boundary condition (LBC) forcing at 6 and 12-hour intervals. The motivation for this study was that some climate model output may only be available at a 12hour interval and it is important to know if CRCM runs with these outputs are comparable to runs made with 6-hourly forcing. The LBC sensitivity was assessed over two different regional domains (North America and Quebec) for annual runoff simulated over 21 river basins located in the Quebec/Labrador peninsula. The sensitivity results were compared with the CRCM’s internal variability and natural climate variability to reach conclusions about the relative importance of LBC update frequency. The results show that LBC frequency can have a significant influence on mean annual runoff over the investigated basins when the simulation domain is relatively small, as in the case of the Quebec, but not for the larger North American (AMNO) domain runs. The entire ensemble of five members of the Canadian Coupled Global Climate Model (CGCM3) can therefore be safely used to generate dynamically downscaled projections over the basins, even though three of the members were archived at a 12-hourly interval. Climate projections for the 2041–2070 horizon (with SRES-A2), from a five-member ensemble of CRCM 45-km runs performed over the AMNO domain (driven by each of the five CGCM3 members), project an increase of annual runoff over all investigated river basins with the largest changes towards the north. This ensemble also provides an estimate of uncertainty of projected basin runoff change related to natural variability, but there remains a need to consider additional projections (more RCMs, more driving GCMs) to produce a more complete assessment of uncertainty. Zusammenfassung Es wurde eine Analyse u¨ ber die Sensitivität von Abfluss-Simulationen des Canadian Regional Climate Model (CRCM) auf die Frequenz, 6 bzw. 12 stündlich, der antreibenden Randbedingungen erstellt. Die Motivation zu dieser Studie war, dass einige Randbedingungen nur alle 12 Stunden vorhanden sind. Es ist daher wichtig zu wissen, ob die Ausgaben von CRCM Läufen mit diesen Randbedingungen vergleichbar sind mit Läufen, die mit 6 stündlichen Randbedingungen angetrieben wurden. Die Sensitivität auf die Randbedingungen wurde für zwei regionale Gebiete (Nordamerika und Quebec) für den jährlichen Abfluss untersucht, der für 21 Flusseinzugsgebiete der Quebec/Labrador Halbinsel simuliert wurde. Die Sensitivitätsergebnisse wurden mit der internen Variabilität des CRCMs und natürlichen Klimaschwankungen verglichen, um einen Eindruck u¨ ber die Wichtigkeit des Zeitintervalls der Grenzbedingungen zu bekommen. Die Resultate zeigen, dass die Frequenz der Grenzbedingungen einen wichtigen Einfluss auf den mittleren jährlichen Abfluss der untersuchten Einzugsgebiete haben, wenn das Modellgebiet relativ klein ist, wie das Gebiet u¨ ber Quebec. Für das größere Gebiet u¨ ber Nordamerika (AMNO) konnte allerdings keine beachtenswerte Sensitivität festgestellt werden. Daher kann das gesamte Ensemble von fünf Mitgliedern des Canadian Global Climate Model (CGCM3) benutzt werden, um dynamisch herunter skalierte Projektionen für die Einzugsgebiete zu erzeugen, obwohl drei der Mitglieder im 12 Stunden-Intervall archiviert wurden. Klimaprojektionen für den 2041–2070 Horizont (mit SRES-A2) eines aus fünf Mitgliedern bestehenden Ensembles von CRCM Läufen, ausgeführt u¨ ber dem AMNO Gebiet (angetrieben von fünf CGCM3 Mitgliedern), projektieren einen ¨ Anstieg des jährlichen Abflusses von allen untersuchten Flusseinzugsgebieten, mit grössten Anderungen im Norden. Dieses Ensemble liefert außerdem einen Eindruck u¨ ber die Unsicherheit von projektierten Abfluss ¨ Anderungen, die durch natürliche Schwankungen verursacht werden. Allerdings sollte in Betracht gezogen werden, zusätzliche Projektionen zu erstellen (mehr RCMs, mehr antreibende GCMs), um eine komplettere Beurteilung der Unsicherheiten erstellen zu können.

1 Introduction Regional Climate Models (RCMs) are high-resolution limited-area models requiring nesting information at ∗ Corresponding

author: Anne Frigon, Ouranos, 550 Sherbrooke West, 19th floor, West Tower, Montreal (Quebec), Canada, H3A 1B9, e-mail: [email protected]

DOI 10.1127/0941-2948/2010/0453

their lateral boundaries. A RCM can be nested within a Global Climate Model (GCM), or within reanalyses of atmospheric observations. Most RCM simulations are performed using 6-hourly updates of lateral boundary conditions (LBC). Some earlier studies (D ENIS et al., 2003; A NTIC et al., 2004) showed improved downscaling performance with LBC update frequencies higher

0941-2948/2010/0453 $ 5.40 c Gebrüder Borntraeger, Stuttgart 2010

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than twice a day. These studies concluded that 12hourly LBC updates would produce reasonable results, although 6-hourly updates were preferable. They also showed that RCM simulations are more sensitive to the horizontal resolution jump between the driving and driven models than LBC update interval. However, these conclusions must be treated with some caution as they are based on a perfect-model approach (the idealized “Big-Brother Experiment”), without consideration for both driving model and driving data deficiencies. It is also important to stress that these results are strongly linked to the regional domain characteristics (location, size and resolution), as well as to the variables and the time scale considered. Finally, these studies were based on only a few summer (D ENIS et al., 2003) and winter months (A NTIC et al., 2004) of RCM simulations. The main objective of this study was to evaluate the sensitivity of basin-scale annual runoff in Québec and Labrador simulated by the Canadian Regional Climate Model (CRCM) to the LBC update interval. This evaluation was initially motivated by the fact that some of the recently produced Canadian Coupled Global Climate Model (CGCM3/T47) climate change runs, intended to be used for climate impact studies at the regional scale, were archived either at a 6-hourly or at a 12-hourly interval. Annual runoff for 21 drainage basins in Québec and Labrador was selected to evaluate the LBC sensitivity as runoff is a spatial and temporal integrator of weather events, is a basic measure of water availability, and has a high priority in Québec for water resource studies in general and for hydroelectricity in particular. As discussed by M URPHY et al. (2009), the physical significance of results from any sensitivity analysis must be assessed against the internal variability arising from the chaotic nature of the climate system. Quantitative estimates of internal variability can be obtained by running climate models several times with different initial conditions (M URPHY et al., 2009). Such simulations will result in different sequences of weather events and might even have somewhat different climate statistics, although the differences in the climate means are expected to decrease as the length of the simulation increases (DE E LIA et al., 2008). In this study, “internal variability” refers to the minimal noise level assessed from the CRCM simulations differing only in their initial conditions; it serves as a lower threshold to help evaluate the significance of the LBC update interval sensitivity results. As LBCs for RCM climate change projections are taken from Global Climate Models (GCMs), the effect of the driving GCM’s internal variability on the RCM simulation must also be considered in a sensitivity analysis; this will provide an estimate of the irreducible uncertainty related to the natural variability of the climate system (M URPHY et al., 2009; C HRIS TENSEN et al., 2001). CRCM simulations driven by different CGCM members will be used to estimate what is hereafter referred to as “natural climate variability”.

Meteorol. Z., 19, 2010

Natural climate variability generally surpasses internal variability of the RCM since RCMs are constrained at their lateral boundaries (C HRISTENSEN et al., 2001; B RAUN et al., submitted; F RIGON et al., 2008). Sensitivity to LBC update interval was considered in terms of its effect on the simulated 30-year climate over both the recent past and future periods, and also for its effect on the climate change signal. D E E LIA and ˆ E´ (2010) present a similar analysis with a focus on C OT seasonal temperature and precipitation over various climatic regions of North America (much larger than the basins investigated here), exploring various types of sensitivities in model configuration (driving data interval, driving GCM, GCM member, CRCM version, internal variability, etc.). We also used the results of the current analysis to provide new insights into the significance of projected changes in runoff over Québec and Labrador compared to the uncertainties related to natural climate variability. Sensitivity to LBC update interval was considered in terms of its effect on the simulated 30-year climate over both the recent past and future periods, and also for its effect on the climate change signal. D E E LIA and ˆ E´ (2010) present a similar analysis with a focus on C OT seasonal temperature and precipitation over various climatic regions of North America (much larger than the basins investigated here), exploring a series of sensitivities in model configuration (driving data interval, driving GCM, GCM member, CRCM version, internal variability, etc.). We also used the results of the current analysis to provide new insights into the significance of projected changes in runoff over Québec and Labrador compared to the uncertainties related to natural climate variability. A similar type of analysis was performed over the United Kingdom by ROWELL (2006), on climate change of seasonal temperature and precipitation (with regards to natural variability). Moreover, with access to multiinstitutional runs performed within the project PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects; C HRISTENSEN et al. (2002)), ROWELL (2006) was also able to consider additional uncertainties in the climate change signal, such as those related to RCMs, GCMs, and emissions scenarios. The document is organized as follows: the next section describes the CRCM and the experimental setup used for the present analysis. Section 3 presents the results of the sensitivity analysis to LBC update interval over the 21 basins of interest, both in terms of the effect on simulated climate and on simulated climate change; this section closes with CRCM projections of water budget components obtained from an ensemble of five runs. Finally, section 4 summarizes the results of this study.

2 The CRCM and experimental setup In this section, we first present a description of the CRCM and then describe in more detail the configura-



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tion of the experiments used in our sensitivity analysis.

2.1 Model description The model used in this study represents the current version (4.2) of CRCM (M USIC and C AYA, 2007), which is the product of the evolution from previous versions (C AYA and L APRISE, 1999; L APRISE et al., 2003; P LUMMER et al., 2006). The CRCM lateral boundary conditions are provided through a one-way nesting method inspired by DAVIES (1976) and refined by ROBERT and YAKIMIW (1986). That is, the regional model receives information from its driving atmospheric data but does not influence the driving data in return. CRCM is driven at its lateral boundaries by the time evolution of vertical profiles from the driving data’s winds, air temperature, humidity and pressure. Linear interpolation of the driving data is performed to provide CRCM input at its own internal time steps. The regional model’s horizontal winds are gradually blended with those of the driving data over a margin covering some 9 grid points along the edges of the lateral boundaries (called the sponge zone). The interactive mixed-layer/thermodynamic-ice lake model developed by G OYETTE et al. (2000) simulates the evolution of surface water temperature and ice cover over the North American Great Lakes, and comes into play only when the CRCM is driven by a GCM. Surface hydrology in CRCM V4.2 is treated in version 2.7 of the multi-layer Canadian LAnd Surface Scheme (CLASS, V ERSEGHY, 1991; V ERSEGHY et al., 1993). CLASS receives precipitation and atmospheric radiation inputs and returns its resulting turbulent (momentum, sensible and latent heat) and radiative fluxes back to the atmosphere. CLASS includes prognostic equations of energy and water conservation for three soil layers (a 0.10 m surface, a 0.25 m vegetation root zone and a 3.75 m deeper layer), a thermally and hydrologically distinct snowpack where applicable (treated as a fourth variable-depth “soil” layer), and the treatment of a separate vegetation canopy. When water input in the soil exceeds the rate of infiltration, water is allowed to pond on the surface until it either evaporates or infiltrates. Any excess ponded water (when water depth exceeds the allowable ponding depth) contributes to surface runoff and is assumed to enter the drainage system. Soil moisture will decrease if drainage and soil evaporation are greater than water input into the soil. Total runoff comes from surface runoff and from drainage from the deep soil column (subsurface runoff). No surface water routing is performed and baseflow from lateral groundwater flow is not considered.

2.2 Experimental setup This analysis is based on simulations performed inhouse by the Climate Simulation Team at Ouranos with version 4.2 of CRCM. Figure 1 presents the two regional

Figure 1: CRCM’s large AMNO (200 x 192) and smaller QC (111 x 87) regional domains at 45-km resolution with topography in grey shades [meters].

domains investigated, where the larger North American domain (AMNO) comprises 200 x 192 grid points covering the whole of North America (Canada, the United States and most of Mexico) and adjoining oceans, while the smaller Québec domain (QC), centered over the province of Québec (in Eastern North America), contains 111 x 87 grid points. Simulations over both domains were performed with a horizontal grid-point spacing of 45 km (true at 60 degrees North), 15-minute time steps, and with a total of 29 vertical Gal-Chen terrainfollowing levels (G AL -C HEN and S OMERVILLE, 1975), ranging from the surface to the rigid lid (top) of the model near 29 km. A three-year spin up was performed for each 30-year simulation to allow the simulated climate system to reach equilibrium (PAQUIN et al., 2006). Spectral nudging was applied to large-scale winds within the regional domain for all the CRCM simulations considered in this analysis to keep the RCM’s large-scale flow close to that of the driving data. Such a technique can be useful when RCM simulations are performed over very large regional domains, such as AMNO. As a comparison, the size of the AMNO domain is at least twice that of the ENSEMBLES domain (C HRISTENSEN et al., 2008). Even though there is still some debate as to whether spectral nudging is beneficial or not, recent results point to more positive than negative impacts (A LEXANDRU et al., 2009). Our spectral nudging technique, developed by R IETTE and C AYA (2002), uses the spectral decomposition from D ENIS et al. (2002); the basic approach originates from the work of VON S TORCH et al. (2000). The spectral nudging applied here is considered relatively weak, where horizontal winds with wavelengths greater than 1400 km are nudged with varying intensity at the vertical, starting just above 500 hPa and reaching a characteristic relaxation time of 10 hours at the model top (near 10 hPa). At the

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Table 1: List and characteristics of the pairs of CRCM V4.2 simulations used in the analysis. Each pair, presented in the first column, consists in a reference run followed by its perturbed run used to perform the sensitivity analysis to LBC interval (with 6-hourly versus re-sampled 12-hourly data). Also listed are the experiments for internal variability (with RCM “twin” runs differing only in their initial conditions) and for natural climate variability (with RCM runs driven by different GCM members differing only in their initial conditions) that serve as basic comparison thresholds. The third column specifies the regional domain of the experiment, either North America (AMNO) or Quebec (QC).

CRCM runs compared adj/adt adl/adv adk/adu adm/adw afx/agb afy/agc acw/acx adj/aeb adk/aec acy/acz acu/adz acv/adr adj/adl adk/adm acu/adc acv/add

Analysis

Domain

Driving data

LBC update interval LBC update interval LBC update interval LBC update interval LBC update interval LBC update interval Internal Variability Internal Variability Internal Variability Internal Variability Internal Variability Internal Variability Natural Clim. Variability Natural Clim. Variability Natural Clim. Variability Natural Clim. Variability

AMNO AMNO AMNO AMNO QC QC AMNO AMNO AMNO QC QC QC AMNO AMNO QC QC

CGCM3#4 CGCM3#5 CGCM3#4 (A2) CGCM3#5 (A2) CGCM3#4 CGCM3#4 (A2) ERA-40 CGCM3#4 CGCM3#4 (A2) ERA-40 CGCM3#4 CGCM3#4 (A2) CGCM3#4 / #5 CGCM3#4 / #5 (A2) CGCM3#4 / #5 CGCM3#4 / #5 (A2)

top of the model, only 5 % of the CRCM’s large scale is replaced by that of its driving data. A complete list of the pairs of CRCM V4.2 simulations used in this analysis is presented in Table 1. These include the basic sensitivity runs to LBC update interval, as well as the RCM’s internal variability and the natural climate variability experiments for evaluating the physical significance of the sensitivity results. In our analysis, we consider sensitivity to LBC update interval to be significant if it is greater than the RCM’s internal variability (the minimum noise level). The pairs of CRCM runs used to evaluate sensitivity to driving data update frequency consist of a simulation driven by 6-hourly driving data (the “reference” run) and one driven by resampled 12-hourly driving data (the “perturbed” run). Over the AMNO domain, a total of four pairs of 30year CRCM V4.2 simulations were performed, driven by members #4 and #5 of CGCM3 (S CINOCCA et al., 2008; F LATO and B OER, 2001), over the recent past (1961–1990) and future (2041–2070) periods. All future climate runs were based on the Special Report on Emissions Scenarios (SRES) A2 scenario from NAKI CENOVIC et al. (2000), for both the driving CGCM3 and the driven CRCM. Over the QC domain, only two pairs of 30-year simulations of CRCM V4.2, driven by CGCM3’s member #4, were available over the same past and future time windows. As shown in Table 1, the RCM’s internal variability at basin scale is evaluated from three pairs of socalled “twin” 30-year CRCM V4.2 runs performed over each regional domain (AMNO and QC), differing only in their initial conditions. Some internal variability runs

Analyzed period 1961-1990 1961-1990 2041-2070 2041-2070 1961-1990 2041-2070 1961-1990 1961-1990 2041-2070 1961-1990 1961-1990 2041-2070 1961-1990 2041-2070 1961-1990 2041-2070

were driven by 6-hourly re-analyses (ERA 40 from European Center for Medium-Range Weather Forecasts, freely available on a regular 2.5◦ x 2.5◦ latitudelongitude global grid, degraded from its original spectral T159 horizontal resolution; U PPALA et al., 2005) over the recent past, and others were driven by the 6-hourly CGCM3 (member #4) over both the recent past and future periods. Additionally, natural climate variability was estimated at basin scale from two pairs of 30-year CRCM V4.2 simulations run over each domain. Each pair consists of CRCM runs driven by a different member of the CGCM3; here we used the 6-hourly CGCM3 members #4 and #5, differing only in their initial conditions. Finally, it is worth mentioning that in the case of both the internal variability and the natural climate variability experiments, the climate simulated by one simulation in a pair is as probable as the other. However, as an indication, the first and second runs refer respectively to the chosen reference and perturbed runs for each pair presented in Table 1. This analysis is based on more than 550 years of simulations performed with the CRCM. The use of additional simulations would certainly ensure a better coverage of the sensitivities explored but we estimate that the ensemble sizes used here are sufficient for our analysis. Access to limited computer resources represents an important constraint and the strategic operational choices and priorities do not always comply with those of all axes of research. In summary, over each domain (AMNO and QC), the analysis of internal variability is based on three pairs of 30-year runs, while natural climate variability is evaluated from two pairs. As mentioned previously,


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internal variability is known to decrease as the averaging period considered lengthens (DE E LIA et al., 2008) and also as the area of interest becomes larger. At the grid-point scale, within a relatively large regional domain (193 x 145 versus our 200 x 192 AMNO), L UCAS P ICHER et al. (2008) find that a minimal ensemble size would consist of two 10-year members. At the basin scale, the ensemble size used here can be considered sufficient to enable a reliable evaluation of internal variability. Evaluating natural climate variability from two 30year pairs could be considered a random glance, but we find in fact that these two pairs provide results comparable to those found from an ensemble of five simulations (see section 3.3). Finally, on one hand, the sensitivity analysis to LBC update interval on the AMNO domain used four pairs of CRCM runs, and this can be considered as a relatively good sample size. On the other hand, over the QC domain, the smaller ensemble of only two pairs of 30-year runs might seem small but the results show such a clear sensitivity effect that we do not consider that a larger ensemble would change the outcome (see section 3.1). The surface fluxes investigated in this study (runoff, precipitation and evapotranspiration) are cumulated as time integrals between 6-hourly archivals in CRCM’s outputs. As mentioned above, results of the sensitivity analysis are presented here only for runoff (in mm/day), which provides an integrated picture of a basin’s hydrological regime; this choice of units allows comparisons regardless of basin drainage area. The CRCM annual runoff values (January-December), integrated at the basin scale, were obtained by aggregating data from all CRCM 45-km grid-size points located within each basin’s boundaries. Analysis of 30-year annual series was performed over the 21 basins of interest shown in Figure 2; Table 2 provides the complete basin names and drainage areas. Investigated basins span most of the province of Québec and part of Labrador (in Newfoundland Province), with drainage areas ranging from 13 000 to 177 000 km2 (corresponding to nine to ninety 45-km CRCM grid points). The basins are located in the center of the smaller domain (QC) and in the northeastern section of the larger domain (AMNO) (cf. Figure 1).

3 Results This section presents sensitivity results from the experiments discussed in section 2.2 and listed in Table 1. First, section 3.1 presents climate sensitivity of CRCM’s simulated annual basin runoff to the change in driving data update interval (12-hourly versus 6-hourly). Section 3.2 then discusses the sensitivity of the climate change signal to the LBC update interval, providing a more complete sensitivity picture. In both sections, sensitivity is explored according to domain characteristics (AMNO and QC) and is compared to the CRCM’s internal variability as well as to natural climate variability.

Figure 2: Basins of interest with CRCM’s 45-km grid. Table 2 provides the complete name for each of the 21 basins.

Finally, section 3.3 shows projected changes of annual runoff obtained from a subset of CRCM regional climate projections (2041-2070-A2 versus 1961–1990) over the 21 basins of interest, providing an idea of the future water resources availability over the Québec territory.

3.1 Climate sensitivity experiments Figure 3 summarizes climate sensitivity results for annual runoff over the basins of interest from 30-year CRCM simulations performed over the AMNO domain; each symbol (diamond, star and square) represents runoff statistics computed from a specific pair of CRCM simulations over one particular basin, where each pair consists of a perturbed and a reference run (cf. Table 1). On the abscissa, the symbol presents the difference in the 30-year mean runoff from each pair of runs, while the ordinate shows the centered (or unbiased) Root Mean Square Difference (C RMSD) obtained in equation (3.1) below by comparing the 30-year time series of basin annual runoff in each pair of runs. v u n u1 X C RM SD = t [(xi − x ¯) − (yi − y¯)]2 (3.1) n i=1

where xi and yi represent the elements from each of the two time series, n being the total number of elements i,


Drainage area (km2) Rivière Arnaud (ARN) 26 900 29 000 Rivière à la Baleine (BAL) 22 200 Rivière Bell (BEL) 87 000 Bersimis-Outardes-Manic (BOM) 37 870 Réservoir Caniapiscau (CAN) 69 300 Réservoir Churchill Falls (CHU) Rivière aux Feuilles (FEU) 41 700 Rivière Georges (GEO) 24 200 Grande rivière de la Baleine (GRB) 36 300 La Grande Rivière (LGR) -includes CAN177 000 29 240 Réservoir Manic5 (MAN) 42 700 Rivière aux Mélèzes (MEL) 19 000 Rivière Moisie (MOI) 15 600 Rivière Natashquan (NAT) 48 500 Rivière Caniapiscau (Pyrite) (PYR) 143 000 Rivière des Outaouais (RDO) Rivière Romaine (ROM) 13 000 Rivière Rupert (RUP) 40 900 Lac Saint-Jean (SAG) 73 000 41 340 Rivière Saint-Maurice (STM) 31 900 Rivière Waswanipi (WAS) Basin name (3-letter abbreviation)

and with x ¯ and y¯ as the time mean values. This statistic was used previously in analyses of the internal variability of the CRCM hydrology over the Québec/Labrador basins (M USIC et al., 2009; B RAUN et al., submitted) and provides a measure of the degree of synchronicity of the interannual variability of two time series. In the results, C RMSD is expressed as a percentage of the 30year reference run mean which will vary depending on the pairs of simulations considered. The LBC sensitivity experiment (black diamonds) used the 6-hour runs as the reference (and the re-sampled 12-hourly driven as the perturbed runs). In the case of the internal variability (grey stars) and natural variability (grey squares) experiments, the chosen reference runs have been identified for each pair in Table 1 (as the first run). In Figure 3, the black diamonds encompass the sensitivity of basin annual runoff to driving data update interval (12-hour versus 6-hour), while grey stars represent the CRCM’s internal variability at the basin scale. The Figure shows that for the AMNO domain runs, the CRCM’s sensitivity to driving data frequency has an effect on basin annual runoff that is comparable to internal variability i.e. differences in the 30-year mean runoff are ±5–6 % with C RMSD ranging from 5 to 18 %. These results indicate that LBC sensitivity is not significant for the given variable and experimental conditions. This lack of sensitivity is likely related to the fact that the basins investigated are located far away from the western inflow boundary of the AMNO domain (ap-


30 C RMSD of 30−year annual runoff [%]

Table 2: Drainage area for each of the 21 basins of interest, presented in square kilometers. Figure 2 shows the location of the basins.

25 20 15 10 5 0 −10

−5 0 5 10 Difference of 30−year mean annual runoff [%]

Figure 3: Relative sensitivity [percent, relative to the 30-year mean of the reference run] of annual runoff over the 21 investigated basins from pairs of 30-year CRCM runs performed on the AMNO domain. The effect of driving data interval (12-hourly versus 6-hourly; black diamonds) is compared to that of CRCM’s internal variability (grey stars) and to natural variability (driving GCM’s internal variability on CRCM runs; grey squares).

30 C RMSD of 30−year annual runoff [%]

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25 20 15 10 5 0 −20

−15 −10 −5 0 5 10 15 Difference of 30−year mean annual runoff [%]

Figure 4: As in Figure 3, but from CRCM runs performed over the smaller QC domain [percent, relative to the 30-year mean of the reference run].

proximately 6000 km), allowing sufficient “spatial spin up” (L EDUC and L APRISE, 2009); thus the CRCM can regenerate its own climate at the annual scale whether driven by the 6-hourly or 12-hourly GCM data. The so-called “spatial spin up” refers to the distance over which the small-scale features will develop from the driving large-scale flow, being generated through various forcings such as orography, subgrid-scale physical processes and non-linear interactions (L EDUC and L APRISE, 2009).


Figure 3 also shows that differences in the 30-year mean runoff from natural variability are ±10 % (grey squares), while C RMSD values are much larger (17 to 27 %). The large C RMSD values reflect the important differences in year-to-year runoff generated by natural climate variability. These results are consistent with previous studies (C HRISTENSEN et al., 2001; B RAUN et al., submitted; F RIGON et al., 2008) that show natural variability is more important than internal variability of the RCM, which is constrained at its lateral boundaries. For both internal and natural variability, an almost symmetrical spread around zero (the y axis) of the 30-year climate mean is apparent related to the chaotic nature of this phenomenon, still present at the basin scale. Figure 4 presents the sensitivity analysis results for the QC domain runs. The CRCM’s internal variability at the basin scale (grey stars) is only ±1 % of the 30-year mean runoff, with much smaller C RMSD values (less than 4 %) than from the AMNO domain runs. These results are consistent with previous studies (B RAUN et al., submitted; A LEXANDRU et al., 2009; A LEXANDRU et al., 2007; R INKE and D ETHLOFF, 2000) that show internal variability becomes smaller with a smaller domain due to more constraint from driving data. Over the QC domain, the response of basin annual runoff to the change of driving data frequency (black diamonds) is clearly higher than internal variability and is therefore significant. This sensitivity is not only more important than internal variability but it shows a large systematic difference in the 30-year climate mean at basin scale (–3 to –18 %), so that there is always less runoff produced when the CRCM is driven by 12-hourly CGCM3 data (compared to 6-hourly). This implies that changing the driving data frequency may alter the simulated climate if the regional domain is relatively small, not allowing enough “spatial spin up” for weather systems to be fully achieved. For the QC domain, a distance of only approximately 2500 km is available from the western inflow boundary to the investigated basins, compared to 6000 km for the AMNO domain. This is consistent with results found by L EDUC and L APRISE (2009). Closer examination of CRCM outputs over the QC domain (6-hourly outputs, monthly means, seasonal means, etc.) showed that with 12-hour driving data, the low pressure systems do not become as intense (less cyclogenesis) and thus, generate less precipitation and runoff (than with 6-hourly LBCs). In fact, we find that by generating less runoff over the basins, the CRCM gets even farther from the observed values (not shown; M USIC et al., 2009; F RIGON et al., 2007). Thus, over a small domain such as QC domain, the use of 12-hourly LBC is not suitable. Even though we find that driving data frequency has a systematic influence on basin annual runoff for the QC domain runs, comparison to natural variability (grey squares) in Figure 4 allows us to complete the picture. It is noteworthy that differences from natural variabil-

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40 Runoff change from perturbed run [%]


35 30 25 20 15 10 5 0 −5 0

10 20 30 40 Runoff change from reference run [%]

50

Figure 5: Sensitivity of annual runoff change signal between 2041– 2070 (SRES-A2) and 1961–1990 periods [percent, relative to 1961– 1990 present climate] over the 21 investigated basins, from CRCM runs performed on the AMNO domain. The effect of driving data interval (12-hourly versus 6-hourly; black diamonds) is compared to that of CRCM’s internal variability (grey stars) and to natural variability (driving GCM’s internal variability on CRCM runs; grey squares).

ity are generally within ±10 % for 30-year mean runoff and within 17–28 % for C RMSD for CRCM simulations performed either on the AMNO or on the smaller QC domain. This provides very useful information as it shows that natural variability (of annual runoff), estimated by dynamically downscaling GCM projections, does not depend on the RCM’s regional domain characteristics; this contributes to increased confidence in the RCM projections. Similar sensitivity behavior was found for basin annual precipitation and evapotranspiration.

3.2 Climate change sensitivity experiments The second part of the analysis consisted of examining the effect of driving data frequency (12-hour versus 6hour) on the climate change signal from CRCM simulated basin annual runoff through comparison of runs made for the future 2041–2070 period (SRES-A2 emissions scenario) with the recent 1961–1990 period. Since one of the main objectives of RCMs is to perform regional climate change projections, such an analysis will complete the picture in terms of the sensitivity to driving data frequency. Here, the climate change signal is given in percent relative to the present climate (1961–1990). Figure 5 summarizes sensitivity of projected runoff change over the basins of interest from CRCM simulations performed over the AMNO domain; each symbol (diamond, star and square) represents runoff change computed over one particular basin (from a specific pair of CRCM past and future simulations). The abscissa presents the climate change signal obtained from

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Runoff change from perturbed run [%]

40 35 30 25 20 15 10 5 0 −5 −10

0

10

20

30

40

Runoff change from reference run [%]

Figure 6: As in Figure 5, but from CRCM runs performed over the smaller QC domain [changes in percent, relative to 1961–1990 present climate].

CRCM reference runs, while the ordinate shows the climate change signal from CRCM perturbed runs (cf. Table 1). As mentioned previously, sensitivity to driving data update interval (black diamonds) is evaluated from the CRCM simulations driven by 6-hour LBC data as the reference runs, while the perturbed runs are driven by the re-sampled 12-hour driving data (cf. Table 1). For the internal variability (grey stars) and natural variability (grey squares) experiments, the chosen reference runs have also been identified for each pair in Table 1 (as the first run). The sloped line identifies the 1:1 ratio line between the climate change from the reference and the perturbed CRCM runs. From Figure 5 we can see that the projected runoff change at the basin scale for the AMNO domain runs is only slightly more affected by the change in driving data frequency (black diamonds within ±8 % maximum departure from the 1:1 ratio line) than by CRCM’s internal variability (grey stars within ±6 % maximum departure from the 1:1 ratio line). To put this in perspective, natural variability has a much more important effect on projected runoff change (grey squares within ±13 % maximum departure from the 1:1 ratio line). We can therefore conclude that the response of projected runoff change to a change in driving data interval is not really significant (same order of magnitude than CRCM’s internal variability). From these results, we find that the resulting climate change sensitivity of basin runoff (sensitivity of future minus present climates) to LBC update interval is just slightly enhanced compared to the effect found on (or sensitivity of) 30-year annual runoff over the recent past (due to the change in LBC update interval) and over the future periods separately; this means that 30-year sensitivities from past and future climates partially cancel each other out for runoff at basin scale for the AMNO


ˆ E´ (2010) present a simdomain runs. D E E LIA and C OT ilar analysis for seasonal temperature and precipitation over the AMNO domain and also find that sensitivity of the climate change signal to a modification in model configuration (driving data interval, driving GCM, GCM member, CRCM version, internal variability, etc.) can be partially enhanced (or even reduced) due to discrepancies in recent past and future climate sensitivities separately. Figure 6 presents sensitivity of the climate change signal of runoff from the CRCM runs performed over the QC domain in the same form as Figure 5. Basin runoff change on the QC domain is slightly more sensitive to driving data interval (black diamonds within ±4 % maximum departure from the 1:1 ratio line) than to CRCM’s internal variability (grey stars within ±2 % maximum departure from the 1:1 ratio line). As was the case from the AMNO domain runs, sensitivity of the runoff change signal to the driving data interval remains much smaller than the effect of natural variability (grey squares within ±15 % maximum departure from the 1:1 ratio line). These results are a bit surprising since sensitivity of 30-year mean runoff to driving data frequency was found to be quite significant on QC domain runs, producing a different climate (cf. Figure 4 with a “drier” climate when CRCM is driven by 12-hourly versus 6hourly output from CGCM3). We find that comparable sensitivities of basin runoff to driving data interval from past and from future periods (not shown) cancel each other out, resulting in a negligible climate change sensitivity (sensitivity of future minus present climates). In addition, it is interesting to note that the effect of natural variability on runoff change simulated by the CRCM (by using CGCM3 members #4 and #5) over both regional domains (AMNO and QC) is quite similar (around ±13–15 %). This provides an additional element to increase our confidence in the RCMs as a downscaling tool of GCM projections.

3.3 CRCM projected changes of basin annual water budget components Results from previous sections suggest that the sensitivity of the 30-year basin annual runoff and its projected change to the driving data interval (12-hourly versus 6-hourly) is within the range of internal variability when CRCM simulations are performed over the large AMNO domain (and much smaller than natural variability). The entire ensemble of five CGCM3 members can therefore be safely used to drive the CRCM over such a large domain, even though three of the members were archived at a 12-hourly interval (#1, #2 and #3). In this section, we address changes of the main water budget components (precipitation, evapotranspiration and runoff) from 1961–1990 to 2041–2070 (with SRES-A2 emissions scenario), projected by the AMNO domain CRCM simulations, which are driven by each of the five CGCM3 members.



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Figure 7: Projected changes of basin annual means of water budget components [mm/day] between 2041–2070 (SRES-A2) and 1961– 1990 from CRCM runs performed over the AMNO domain. Each of the five adjacent bins shows results from a CRCM run driven by each of the five CGCM3 members. Histograms over each basin present changes in precipitation (entire bar, red and blue), in evapotranspiration (red part) and in runoff (blue part). All histograms are based on identical scales, ranging linearly from 0 to 0.5 mm/day.

Figure 8: Projected relative changes of annual basin runoff between 2041–2070 (SRES-A2) and 1961–1990 [in percent, relative to 1961– 1990] from five CRCM runs performed over the AMNO domain, driven by each of the five CGCM3 members. The numbers over each basin indicate intervals of projected changes as “∆R ±SPREAD”, where “∆R” is the median from the five CRCM projections and “SPREAD” is defined as the maximum deviation from the median value.

Figure 7 presents a map of the investigated basins with projected changes in water budget components (in mm/day) from all five CRCM simulations. A histogram over each basin summarizes the balance of precipitation, evapotranspiration and runoff change as a response to global warming. In other words, it indicates what portion of precipitation change (entire bar) goes to evapotranspiration change (red part) and to runoff change (blue part). According to all simulations, the hydrological cycle intensifies over all the investigated basins in the future climate: increases are projected for precipitation, as well as for evapotranspiration and runoff. Figure 7 clearly shows that the most important changes in evapotranspiration (red part) are projected for the southwestern basins, with a gradual decrease of these changes towards the north. As a consequence, the geographical pattern of runoff change follows the opposite direction. Note also that projected changes in precipitation are in general more important over the central parts of the Québec/Labrador region. The variations in the bars’ heights in Figure 7 illustrate the effects of internal vari-

ability of the driving GCM, providing thus an idea of natural variability related to the chaotic nature of the climate system: the differences in the changes between the five CRCM projections vary from 0.06 to 0.18 mm/day for precipitation, from 0.01 to 0.04 mm/day for evapotranspiration, and from 0.08 to 0.21 mm/day for runoff. To clarify the runoff change picture, the projected relative changes (in percent relative to the recent past) are shown in Figure 8. The numbers over each basin present the median from the five CRCM projections, accompanied by a spread derived from the ensemble that can be used as an estimate of uncertainty induced by natural variability present in the climate system. For each basin, the spread is defined as the maximum deviation of all projections from the basin median value. As can be seen from Figure 8, projected increases in runoff range from 9 % to 33 % with the largest changes projected over the northernmost basins, with a gradual decrease towards the southeast and southwest. The spread (uncertainty) of basin runoff change related to natural variability is generally within ±10 %, except for a few basins that exceed

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this range (ARN with ±14 %, GRB with ±13 %). This ±10 % spread (linked to natural variability obtained from the climate change of five CRCM runs) agrees well with the results of the sensitivity analysis presented in section 3.2, which was based on only two pairs of 30year CRCM climate change runs (driven by members #4 and #5 of the CGCM3; see grey squares on Figure 5). The use of two pairs of runs only provides a very limited sample of natural climate variability, but it seems that this was (by chance) sufficient in this case. In fact, closer analysis of Figure 7 shows that for many basins, it is the fourth and fifth members of CRCM (driven by CGCM3 members #4 and #5) that show the largest differences in their climate change values of runoff (blue bar). Thus by chance, we appear to have used two very different CRCM pairs of runs to perform our analysis of natural variability. Finally, it should be emphasized that a comprehensive assessment of uncertainty associated with hydrological projections requires an approach that covers a wide spectrum of modeling choices. An important question concerns the design of model experiments in order to cover a wide range of fundamental uncertainty. Combination of models differing in their structure, in their physical parameterizations and in the processes they include (or neglect), as well as the interpretation of their results is an important challenge. For instance, it is not clear yet how to take model performance into account when constructing future climate projections (W EIGEL et al., 2010). Intuitively, when a model has large biases in its representation of historical climate, the confidence in its projections becomes lower. M USIC et al. (2009) show that when CRCM is run over the AMNO domain it tends to systematically underestimate observed annual 30-year mean runoff over most of the investigated basins, with biases ranging from 10 % up to 40 %.

4 Summary and conclusions In this study, we evaluated the sensitivity of basin scale annual runoff simulated by the CRCM to the LBC update interval (6-hourly versus re-sampled 12-hourly). In order to take into account the irreducible variability related to the chaotic nature of the climate system, sensitivity was evaluated with regard to both the RCM’s internal variability (evaluated by changing the RCM’s initial conditions) and to natural variability (evaluated by driving the RCM with different GCM members – differing only in their initial conditions). This work extends previous studies on the influence of driving data update frequency from D ENIS et al. (2003) and A NTIC et al. (2004) that were limited to only a few summer and winter months. The analysis focused on a set of 21 river basins located in the Québec/Labrador peninsula, and was based on a large ensemble of CRCM V4.2 30-year simulations (1961–1990 and 2041–2070 with SRES-A2) performed


over two regional domains at a 45-km resolution: a large AMNO domain and a smaller QC domain. Results from the sensitivity analysis shows that changes in driving data update frequency may alter the 30-year annual mean of basin runoff if the simulation domain is relatively small, such as is the case for the QC domain runs. For the AMNO domain runs, annual runoff at the basin scale is not significantly affected by using re-sampled 12-hourly instead of 6-hourly driving data. This confirms earlier findings of L EDUC and L APRISE (2009) showing that in order to obtain physically correct results, sufficient “spatial spin up” is required from the (western) inflow boundary of the regional domain to allow for the small scales to develop properly. Related ˆ E´ (2010) find that sensitivito this, DE E LIA and C OT ties of temperature and precipitation to various configuration changes (such as driving GCM, driving data interval) will differ depending on the distance from the regional domain’s inflow boundary, on the season considered, as well as on the spatial scale at which analysis is performed. The present study also shows that sensitivity of the climate change signal to LBC update interval may be partially enhanced (or even reduced) compared to the sensitivity found separately for present and future cliˆ E´ (2010). mates, as is also shown by DE E LIA and C OT It is worth mentioning that the effect of natural variability on both CRCM’s simulated 30-year runoff and on projected runoff change at the basin scale did not depend on the regional domain characteristics (AMNO or QC); this contributes to build confidence in the RCM as a downscaling tool to produce climate projections at a scale more appropriate (than GCMs) for water resources management. Taking into account the above sensitivity results, we safely used all five available members of the CGCM3/T47 to drive the CRCM V4.2 over the AMNO domain, allowing us to project the annual runoff change at the basin scale in the future 2041–2070 (with SRESA2) relative to the 1961–1990 period. The results show an increase in the three water budget components (precipitation, runoff, evapotranspiration) over the 21 investigated basins, with the largest runoff increases projected over the northernmost basins. The projected increases in runoff were found to be smaller moving towards the southwestern basins with the opposite spatial pattern observed for projected changes in evapotranspiration. The CRCM ensemble of simulations used also provided information on the spread (uncertainty) of projected basin runoff change related to natural variability, which is found to be generally within ±10 %. A more complete assessment of uncertainty of hydrological climate projections would require RCM simulations driven by different GCMs, and should even make use of different RCMs. This is the motivation behind the multi-institutional multi-RCM/multi-GCM projects



such as the North American Regional Climate Change Assessment Program (NARCCAP; M EARNS, 2004). In closing, while this analysis was motivated by a practical need to generate an ensemble of regional climate projections at a 45-km resolution from GCM output at 6-hour and 12-hour intervals, it raises an important question: what will be the acceptable spatial resolution and driving data time interval for RCM simulations that will be performed at finer spatial resolutions in the future? In other words, will the current 6-hourly driving data be even sufficient to drive future higher resolution RCM simulations?

Acknowledgments The authors would like to thank the Ouranos Climate Simulation Team for generating and supplying output from the numerous CRCM climate change simulations analyzed here; Mourad L ABASSI who maintains an efficient and user-friendly local computing environment; the Canadian Center for Climate Modelling and Analysis (CCCma) for kindly providing the CGCM3 archives. We also thank Dr. Ramon DE E LIA for stimulating discussions and for his useful comments on the original manuscript. We are grateful to Ross B ROWN for his careful revision of the manuscript and “Danke sehr!” to Katja W INGER who kindly accepted to translate our abstract into German. In addition, we thank the anonymous reviewers, whose constructive comments contributed to improve the manuscript.

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