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Hindawi Advances in Meteorology Volume 2018, Article ID 4984827, 10 pages https://doi.org/10.1155/2018/4984827 Research Article Links between Temperature Biases and Flow Anomalies in an Ensemble of CNRM-CM5.1 Global Climate Model Historical Simulations 1,2 1 O. Lhotka and A. Farda Global Change Research Institute, Czech Academy of Sciences, Brno, Czech Republic Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic Correspondence should be addressed to O. Lhotka; lhotka.o@czechglobe.cz Received 14 February 2018; Revised 6 June 2018; Accepted 24 June 2018; Published 19 July 2018 Academic Editor: Anthony R. Lupo Copyright © 2018 O. Lhotka and A. Farda. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. )e aim of this study was to evaluate temperature and sea-level pressure (SLP) fields and to analyse a related anomalous flow over midlatitudes simulated by the CNRM-CM5.1 global climate model (GCM). Simulated flow over midlatitudes of the Northern Hemisphere was assessed through flow indices, classified into 11 circulation types. Reference data were taken from the NOAA- CIRES 20th Century Reanalysis, version 2c. CNRM-CM5.1 exhibited analogous temperature biases to those reported for the mean of the CMIP5 GCMs’ ensemble. )e most prominent features were an erroneous temperature dipole pattern in the Atlantic Ocean and a warm bias over regions of deep water upwelling (locally exceeding 5 C). )e latter feature was associated with negative SLP biases in those regions. Too low pressure was found over midlatitudes of the Northern Hemisphere, and CNRM-CM5.1 simulated too frequent zonal flow in these latitudes. )e usage of three ensemble members with different initial conditions did not improve model’s outputs because the bias is found to be considerably larger compared to the ensemble members’ spread. )e study showed that temperature and SLP biases are connected in certain regions, suggesting that improvement of GCMs and development of bias correction methods should be carried out with a complex insight. CMIP5 GCMs [6]. In addition, CMIP5 GCMs are not able 1. Introduction to reproduce the changes of a sea ice around the Antarctic Projections of a possible future climate and advanced un- properly [7]. derstanding of the climate system largely depend on global Possible connections between these large-scale oceanic climate models (GCMs). Although considerable effort factors and climate in distant regions have been widely has been made on their improvement over past decades, analysed. Many studies assessed possible links between the current generation of GCMs still suffers from errors that observed rapid Arctic sea ice loss and modified atmo- lower the credibility of their simulations and bring con- spheric circulation in the Northern midlatitudes [8–10], siderable uncertainties in future climate projections [1, 2]. with consequent impacts on wintertime temperatures Wang et al. [3] reported that CMIP5 GCMs tend to un- [11, 12]. European temperatures seem to be also related to derestimate an intensity of Atlantic meridional over- sea surface temperature (SST) anomalies in the North turning circulation (AMOC) [4] and linked this deficiency Atlantic Ocean [13], and even a more distant connection to cold biases in the Northern Hemisphere and warm was found by Zhou and Wu [14] who concluded that heat biases over the Southern Ocean. Majahan [5] suggested waves over Eurasia are influenced also by El Niño-Southern that the decreased AMOC is related to an increased sea Oscillation. ice extent in the Arctic and may have contributed to a too Differences in SST among individual GCMs can explain slow decline of the Arctic sea ice since 1979 simulated by the majority of the intermodel variability in intensity 2 Advances in Meteorology changes of Walker and Hadley circulation cells [15]. )is the lack of cloudiness over the Northern Hemisphere uncertainty propagates to projections of atmospheric cir- continents [2]. In addition, Huszar et al. [21] revealed too large Arctic sea ice extent in this GCM. More detailed culation and other meteorological variables, such as pre- cipitation and temperature. Moreover, Shepherd [16] information about CNRM-CM5.1 is available in the study concluded that the effect of increased global temperature by Voldoire et al. [2]. on atmospheric circulation is not clear, suggesting that climate change in certain regions may possibly be domi- 2.2. NOAA-CIRES 20th Century Reanalysis. Model simula- nated rather by circulation changes than background tions were evaluated against the NOAA-CIRES 20th Century warming. Reanalysis V2c. It provides a global analysis of the most Besides these models’ drawbacks, climate projections are likely state of the atmosphere since 1851 at 6-hourly tem- affected by uncertainties originating from the choice of the poral resolution, and it is available in the 192 × 94 Gaussian emission scenario and from the internal variability of climate grid [22]. Identical variables (temperature 2 meters above [17]. Emission scenarios represent possible ways of human the surface and SLP) were taken in order to evaluate the society’s development that alter the radiative forcing on CNRM-CM5.1 simulations. Seasonal and annual averages of climate [18], mainly through a modification of atmospheric SLP and temperature from CNRM-CM5.1 were interpolated chemistry and land-cover changes. In midlatitudes, the to the aforementioned 192 × 94 Gaussian grid using the uncertainty related to the internal climate variability is ordinary kriging method, allowing a direct comparison with primarily associated with atmospheric dynamics, dominated the reanalysis. by teleconnection modes [19], such as the North Atlantic Oscillation [20]. )e aim of this study was to evaluate global patterns 2.3. Assessment of Model Biases and Circulation Types. of sea-level pressure (SLP) in relation to a simulated tem- We analysed both annual and seasonal global patterns of SLP perature bias. In order to better understand possible dy- and temperature bias for the 50-year-long 1956–2005 pe- namic causes of biases in temperature over Northern riod. Moreover, globally averaged temperature biases for midlatitudes, we also analysed differences between observed various time periods (since 1851) were also calculated. and simulated flow indices over three regions there. His- Besides the biases, differences among individual ensemble torical simulations of the CNRM-CM5.1 GCM [2] were members were also visualised. used, and spatial characteristics of SLP and temperature SLP biases were associated with anomalous flows in the fields are investigated in three ensemble members (initial simulated data, which were quantified through flow indices condition ensemble), which allows analysing the natural (flow strength, direction, and vorticity) [23, 24]. )e flow variability simulated by the model. Simulated flow and indices were calculated identically for both CNRM-CM5.1 temperature characteristics are evaluated against the NOAA- and NOAA-CIRES 20th Century Reanalysis V2c using SLP CIRES 20th Century Reanalysis, version 2c, which is available in 16 points, evenly distributed over analysed regions (North since 1851. America, Europe, and East Asia; Figure 1). )e flow strength (STR) is a vector sum of zonal (w) and 2. Data and Methods meridional (s) flow components (1), and f represents the latitude of the centre (50 N). SLP values (hPa) in individual 2.1. CNRM-CM5.1 Global Climate Model. CNRM-CM5.1 points from Figure 1 are indicated by p1–p16 in the fol- GCM simulations performed by the Czech Hydrometeoro- lowing equations: logical Institute were used. )e simulations are available for √������ the extended 1740–2005 period at 6-hour temporal resolu- 2 2 STR � w + s , tion. We used daily means of SLP (averaged 4 daily values) and monthly means of air temperature 2 meters above the w � 0.5 × (p4 + p5)− 0.5 × (p12 + p13), surface. )e CNRM-CM5.1 GCM was developed by CNRM- GAME (Centre National de Recherches Me´te´orologiques- 1 s � × (0.25 × (p13 + 2 × p9 + p5) Groupe d’e´tudes de l’Atmosphe`re Me´te´orologique) and cos(φ × (π 180)) Cerfacs (Centre Europeen de Recherche et de Formation Avance´e). )e GCM contains the ARPEGE-Climat atmo- − 0.25 × (p12 + 2 × p8 + p4)). sphere component, the NEMO oceanic model, and the (1) GELATO sea-ice component. )ree ensemble members of the ARPEGE-Climat atmosphere model with different initial )e flow direction (DIR) is calculated using an conditions are used. )eir horizontal spatial resolution arctg2 (w, s) function (2). It is similar to the inverse tangent, is roughly 150 km (T127) with global coverage, and the except that the signs of both arguments (w, s) are used to model has 31 vertical levels. Although the model performs determine the quadrant of the result: considerably better compared to its predecessor, sub- DIR � arctg2 . (2) stantial errors in seasonal precipitation and cloud radiative forcing are still present, including the double intertropical convergence zone, the critical underestimation of low Finally, the total vorticity (VORT) is the sum of its zonal clouds on the eastern side of the tropical ocean basins, or (zw) and meridional (zs) components of vorticity (3): Advances in Meteorology 3 70° 60° 50° 40° 30° 20° 10° –180° –160° –140° –120° –100° –80° –60° –40° –20° 0° 20° 40° 60° 80° 100° 120° 140° 160° 180° Figure 1: Location of grid points used for the calculation of flow indices over North America, Europe, and East Asia. Table 1: Annual global temperature ( C) in the NOAA-CIRES 20th VORT � zw + zs, Century Reanalysis (V2c), in the CNRM-CM5.1 ensemble mean, and in its individual ensemble members (E1–E3) during four time sin(φ × (π 180)) periods. zw � × (0.5 × (p1 + p2) sin((φ− 5) × (π/180)) 20th Century CNRM-CM E1 E2 E3 Period Reanalysis sin(φ × (π/180)) ° ° ° ° ( C) ( C) ( C) ( C) − 0.5 × (p8 + p9))− ° V2c ( C) sin((φ + 5) × (π 180)) 1980–2005 14.7 12.0 (−2.7) 11.9 12.0 12.0 1956–2005 14.6 11.8 (−2.8) 11.8 11.8 11.8 × (0.5 × (p8 + p9)− 0.5 × (p15 + p16)), 1906–2005 14.4 11.6 (−2.8) 11.6 11.6 11.6 1851–2005 14.3 11.5 (−2.8) 11.5 11.5 11.5 zs � Temperature biases in the CNRM-CM5.1 ensemble mean are shown in 2 × ( cos(φ × (π/180))) brackets. × (0.25 × (p14 + 2 × p10 + p6)− 0.25 Compared to the NOAA-CIRES 20th Century Reanalysis, × (p13 + 2 × p9 + p5)− 0.25 the CNRM-CM5.1 ensemble mean captured main features reasonably well, but biases were too pronounced in certain × (p12 + 2 × p5 + p4) + 0.25 regions (Figure 2(b)). Over land, a negative bias prevails, especially over polar regions (both the Arctic and Antarctic) × (p11 + 2 × p7 + p3)). and high-altitude areas. )e magnitude of this bias was ° ° (3) roughly 5 C, but it reached 10 C in several grid points over the Tibetan Plateau and the Antarctic. Larger terrestrial areas Based on these indices, 11 circulation types were cal- with a positive bias (1−3 C) were present only over eastern culated. If the STR (flow strength) and the absolute value of parts of North America. Oceans generally exhibit a cold bias VORT (total vorticity) are lower than 3, a pressure pattern is in the Northern Hemisphere and a warm bias in the south. unclassified (U). When the absolute value of VORT is at least ° ° )e largest positive bias (around 3 C, locally exceeding 5 C) four times larger compared to STR, a pressure pattern is was found over the eastern Pacific Ocean, the southeastern classified as cyclonic (C, if the vorticity is positive) or an- Atlantic Ocean, and the Southern Ocean and in the Baffin and ticyclonic (A, if the vorticity is negative). When the afore- Hudson Bays. A bias with a similar magnitude but opposite mentioned conditions are not met, the pressure pattern is sign was located over northern Pacific Ocean, northern classified based on DIR (the flow direction) into one of the 8 Atlantic Ocean, and Arctic Ocean. )e described overall directional types (N, NE, E, SE, S, SW, W, and NW). temperature biases were similar for all ensemble members, especially in the Tropics, where the difference between the “warmest” and “coldest” ensemble members was only 0.1– 3. Global Patterns of Temperature and 0.3 C (Figure 2(c)). )e agreement among the ensemble Sea-Level Pressure Biases members was somewhat weaker in the midlatitudes, but )e negative bias of annual global temperature in CNRM- differences larger than 1 C were still quite rare. )e largest CM5.1 (approximately 2.8 C) was found during all examined variability was found over polar regions, mainly in the Arctic time periods (Table 1), and its magnitude was almost where the difference between the “warmest” and “coldest” identical among the individual ensemble members. ensemble members exceeded 3 C in the Barents Sea. Spatial patterns of annual temperature in CNRM-CM5.1 Turning to SLP, CNRM-CM5.1 was able to simulate for the 1956−2005 period are shown in Figure 2(a). locations of atmospheric action centres (Figure 3(a)) quite 4 Advances in Meteorology Temperature (°C) ≥ 25 20.0 to 24.9 15.0 to 19.9 10.0 to 14.9 5.0 to 9.9 0.0 to 4.9 –5.0 to –0.1 –10.0 to –5.1 –15.0 to –10.1 –20.0 to –15.1 –25.0 to –20.1 < –25.0 (a) Temperature bias (°C) ≥ 5 4.0 to 4.9 3.0 to 3.9 2.0 to 2.9 1.0 to 1.9 0.0 to 0.9 –1.0 to –0.1 –2.0 to –1.1 –3.0 to –2.1 –4.0 to –3.1 –5.0 to –4.1 < –5.0 (b) Max-min difference (°C) ≥ 1.50 1.00 to 1.49 0.90 to 0.99 0.80 to 0.89 0.70 to 0.79 0.60 to 0.69 0.50 to 0.59 0.40 to 0.49 0.30 to 0.39 0.20 to 0.29 0.10 to 0.19 < 0.10 (c) Figure 2: (a) Average annual 2 m temperature in the CNRM-CM5.1 ensemble mean, (b) its bias against the NOAA-CIRES 20th Century Reanalysis, version 2c, and (c) diŠerences between “warmest” and “coldest” ensemble members in individual grid points. All maps represent the 1956−2005 period. well; however, some notable biases were found. Over land, was found in the Tropics (especially over the Indian Ocean), negative SLP bias was located in midlatitudes of the while the largest diŠerences were over polar regions and in Northern Hemisphere (analysed in more detail in Section 4) Azores and North Paci c anticyclones. and in the Antarctic (Figure 3(b)). �e most distinctive Focusing on individual seasons, the smallest global negative SLP biases over oceans were found in the eastern temperature bias was found during the boreal summer (−2.4 Paci c Ocean and the southeastern Atlantic Ocean, and the C in the 1980–2005 period; Table 2). In the remaining biases were related to overestimated temperature in these seasons, the biases were somewhat larger (roughly −2.9 C) regions. By contrast, positive SLP bias was found over and quite similar. Analogously to the annual bias, seasonal western parts of the United States, Mexico, the Tibetan biases were stable during diŠerent time periods and com- Plateau, the Arctic Ocean, and the Southern Ocean. Dif- parable among individual ensemble members. ferences between ensemble members with highest/lowest �e global pattern of temperature bias varied across SLP (in individual grid points; Figure 3(c)) are analogous individual seasons (Figure 4). �e substantial negative bias to those obtained from temperature output (Figure 2(c)). in Greenland and Arctic, which was especially pronounced �e best agreement between individual ensemble members in boreal spring and winter (Figures 4(a) and 4(d)), almost Advances in Meteorology 5 Pressure (hPa) ≥ 1020.0 1018.0 to 1019.9 1016.0 to 1017.9 1014.0 to 1015.9 1012.0 to 1013.9 1010.0 to 1011.9 1008.0 to 1009.9 1005.0 to 1007.9 1000.0 to 1004.9 995.0 to 999.9 990.0 to 949.9 < 990.0 (a) Pressure bias (hPa) ≥ 5.0 4.0 to 4.9 3.0 to 3.9 2.0 to 2.9 1.0 to 1.9 0.0 to 0.9 –1.0 to –0.1 –2.0 to –1.1 –3.0 to –2.1 –4.0 to –3.1 –5.0 to –4.1 < –5.0 (b) Max-min difference (hPa) ≥ 10.0 9.0 to 9.9 8.0 to 8.9 7.0 to 7.9 6.0 to 6.9 5.0 to 5.9 4.0 to 4.9 3.0 to 3.9 2.0 to 2.9 1.0 to 1.9 0.5 to 0.9 < 0.5 (c) Figure 3: (a) Average annual sea-level pressure in the CNRM-CM5.1 ensemble mean, (b) its bias against the NOAA-CIRES 20th Century Reanalysis, version 2c, and (c) diŠerences between ensemble members with the highest/lowest sea-level pressure value in individual grid points. All maps represent the 1956−2005 period. Table 2: Seasonal temperature biases ( C) in the CNRM-CM5.1 was most pronounced during the boreal autumn, when its ensemble mean during four time periods. values exceeded 5 C in some grid points. �e oceanic temperature bias dipole was least distinctive during boreal ° ° ° ° Period MAM ( C) JJA ( C) SON ( C) DJF ( C) autumn. On the contrary, the eastern Paci c Ocean and the 1980–2005 −2.9 −2.4 −2.8 −2.8 southeastern Atlantic Ocean regions with warm biases were 1956–2005 −2.9 −2.6 −2.9 −2.9 relatively stable during all seasons. 1906–2005 −2.9 −2.5 −2.9 −2.9 No considerable diŠerences in the magnitude of the SLP 1851–2005 −2.9 −2.6 −3.0 −2.9 bias were found among individual seasons, except for the MAM: March 1–May 31; JJA: June 1–July 31; SON: September 1–November Antarctic Ocean and the Southern Ocean (Figure 5). �e 30; DJF: December 1–February 28. underestimated SLP in Northern midlatitudes and over disappeared during boreal summer (Figure 4(b)). Analo- the upwelling regions in the eastern Paci c Ocean and the gously to the Arctic, the strong negative bias over the southeastern Atlantic Ocean was also fairly stable during Antarctic was reduced in austral summer (Figure 4(d)). �e the year. �e positive SLP bias over the western parts of aforementioned positive bias over eastern North America the United States, Mexico, and the Tibetan Plateau was 6 Advances in Meteorology Temperature bias (°C) Temperature bias (°C) –5 –4 –3 –2 –1 012345 –5 –4 –3 –2 –1 012345 (a) (b) Temperature bias (°C) Temperature bias (°C) –5 –4 –3 –2 –1 0 1 2 3 45 –5 –4 –3 –2 –1 0 1 2 3 4 5 (c) (d) Figure 4: Average seasonal 2 m temperature bias in the CNRM-CM5.1 ensemble mean for the 1956−2005 period: (a) boreal spring (March–May), (b) boreal summer (June–August), (c) boreal autumn (September–November), and (d) boreal winter (December–February). a year-round feature; however, it was least distinctive during (Figure 4(d)), since the types that are associated with the the boreal summer. advection of relatively cold Arctic air or the development of cold continental stagnant air occur with lower frequency in the model outputs. �e underestimation of northerly types 4. Northern Hemispheric Wintertime was found also during summer and autumn. By contrast, Circulation Types over Midlatitudes CNRM-CM5.1 simulated too frequent easterly, south- In this section, we evaluated the capability of CNRM-CM5.1 easterly, and southerly advection in these seasons (Figure to simulate seasonal frequencies of ¢ow types over three 6(a)). �e largest temperature anomaly during autumn was midlatitude regions (North America, Europe, and East Asia). not clearly related to biases in large-scale ¢ow, and this error Over North America during winter and spring, the most would be possibly associated with land-atmosphere frequent northerly advection (occurring on about one- fth interactions. of days) was underestimated in all three model integra- Too frequent westerly advection was also found over tions, in which the frequency of the northern circulation Europe (except during the summer season; Figure 6(b)). type varied between 15% and 17% (Figure 6(a)). �e oc- Its observed frequency during winter (20%) was consider- ably overestimated in CNRM-CM5.1, ranging from 25% currence of the types with advection from northeast was also suppressed in the simulations. By contrast, advection to 27%. �is overestimation is less pronounced in the transient seasons (spring and autumn), and the summertime from west and northwest and the frequency of the cyclonic type were overestimated, indicating a too frequent zonal frequency of the westerly ¢ow type is underestimated in ¢ow in simulated data during these seasons. �ese synoptic- CNRM-CM5.1. �e model also simulated too frequent scale anomalies can be clearly linked to biases in the mean south-westerly advection, while the frequency of easterly and SLP gradient over North America, that is, to the positive south-easterly circulation types was underestimated (except SLP bias over western parts of the United States and Mexico in summer). Interestingly, these errors did not result in and the negative bias over Alaska and Canada (Figures 5(a) a notable wintertime positive temperature bias over Europe, and 5(d)). Furthermore, these errors in atmospheric dy- probably due to underestimated temperature in the North namics can be linked to positive winter temperature biases Atlantic Ocean in the model simulations. Advances in Meteorology 7 Pressure bias (hPa) Pressure bias (hPa) –5 –4 –3 –2 –1 0 12345 –5 –4 –3 –2 –1 0 1 2 345 (a) (b) Pressure bias (hPa) Pressure bias (hPa) –5 –4 –3 –2 –1 0 1 2 345 –5 –4 –3 –2 –1 0 12345 (c) (d) Figure 5: Average seasonal sea-level pressure bias in the CNRM-CM5.1 ensemble mean for the 1956−2005 period: (a) boreal spring (March–May), (b) boreal summer (June–August), (c) boreal autumn (September–November), and (d) boreal winter (December–February). Over East Asia, CNRM-CM5.1 simulated the prevailing southeastern Atlantic Ocean and Paci c Ocean. �is bias wintertime monsoon-related northerly advection quite well was already reported for the CMIP3 GCMs, and it was linked (Figure 6(c)); however, the frequencies of the northwestern to erroneous southward displacement of the intertropical and western circulation types were markedly overestimated convergence zone, subsequent modi cation of westerly at the expense of the northeastern circulation type. �e latter winds, and deepened thermocline, which reduce upwelling of cold water. In addition, the bias is related also to improper is linked to the bias in the zonal SLP gradient (Figure 5(d)). By contrast, CNRM-CM5.1 failed to simulate the prevailing simulation of low-altitude clouds in these regions [26]. An southerly advection associated with monsoon during analogous issue was reported for CMIP5 simulations [27], summer. �e frequency of the southerly type was almost and Wang et al. [3] revealed other interhemispheric links halved in CNRM-CM5.1 compared to the reanalysis. between these warm biases and distant large-scale factors, In all, too frequent zonal ¢ow was found across the such as weaker monsoons in the Northern Hemisphere. Northern midlatitudes. �is bias is clearly associated with �erefore, a reduction of these biases cannot be based on overestimated/underestimated SLP in two belt-shaped pat- correction of regional processes only but has to be carried terns, one between about 20 and 30 N and the other around out with a complex insight. about 55 N (Figure 5(d)). Furthermore, the underestimated Another notable de ciency of CMIP5 GCMs (including SLP in midlatitudes is linked to the overestimated (under- CNRM-CM5.1) is the oceanic bias dipole, probably origi- nating from a weakened northward heat transport due to estimated) frequency of strongly cyclonic (anticyclonic) cir- culation types in all examined regions (Figure 6). a reduced AMOC [3], resulting in the warmer Southern Ocean and the colder North Atlantic Ocean compared to the reference data. �e cold bias over the North Atlantic 5. Discussion may possibly be related to the persisting negative phase CNRM-CM5.1 exhibited similar temperature biases as have of Atlantic multidecadal oscillation [28] in the model out- been shown for the CMIP5 multimodel mean, with respect puts. CMIP3 GCMs, however, have di©culties to simulate to their spatial patterns and magnitude [25]. Pronounced this feature, mainly due to a low signal-to-noise ratio and warm biases were found over upwelling regions in the a long period of oscillation [29], and it is possible that 8 Advances in Meteorology 30 30 40 25 25 15 15 5 5 0 0 0 N NE E SE S SW W NW A C U N NE E SE S SW W NW A C U N NE E SE S SW W NW A C U Circulation type Circulation type Circulation type 30 30 25 25 20 20 10 10 5 5 0 0 N NE E SE S SW W NW A C U N NE E SE S SW W NW A C U N NE E SE S SW W NW A C U Circulation type Circulation type Circulation type 30 40 25 25 20 20 15 15 10 10 5 5 0 0 NE NE SE S SW W NW A C U N NE E SE S SW W NW A C U N NE E SE S SW W NW A C U Circulation type Circulation type Circulation type 30 30 40 25 25 20 20 15 15 10 10 5 5 0 0 0 N NE E SE S SW W NW A C U NE NE SE S SW W NW A C U N NE E SE S SW W NW A C U Circulation type Circulation type Circulation type (a) (b) (c) Figure 6: Mean wintertime frequency of circulation types in NOAA-CIRES 20th Century Reanalysis (black dots) over (a) North America, (b) Europe, and (c) East Asia. Grey bands indicate a span between individual ensemble members in CNRM-CM5.1 for each circulation type. of reference data (i.e., the NOAA-CIRES 20th Century CNRM-CM5.1 suŠers from the same de ciency as well. �e negative temperature bias in northern Atlantic and Europe Reanalysis V2c), inasmuch as Stryhal and Huth [31] showed may also possibly be linked to improper simulation of Gulf that the choice of diŠerent reanalyses can have a profound Stream, which carries warm water erroneously towards the eŠect on GCM validation over Europe in winter. Canadian Archipelago with consequent impacts on the Temperature diŠerences between individual ensemble simulated North American climate. Huszar et al. [21] re- members are relatively small on the global scale, which is ported an overestimated sea ice extent in the CNRM-CM5.1, in accordance with Kharin et al. [32], who showed that which may explain the strong cold bias over the Arctic a spread between individual GCMs was larger compared to Ocean in spring and winter found in our study. the diŠerence between ensemble members of one particular GCM. In our study, the diŠerence between the ensemble Besides polar regions, considerable negative temperature biases over land were found over mountainous areas, which members in simulating temperature was smallest over equatorial areas and gradually increased poleward. �e is in accordance with IPCC [25] and Su et al. [30] who evaluated the performance of CMIP5 GCMs over the Ti- largest discrepancies were found over polar oceans, in ac- betan Plateau and suggested that the negative bias may be cordance with Kharin et al. [32], and may be related to linked to snow-cover feedback processes in the models. diŠerent boundaries of sea ice extent in individual ensemble Besides these physical processes, however, the bias may be members that would considerably alter a surface climate. aŠected by a simple representation of orography or a lack of Small diŠerences among the individual ensemble observed data in these remote areas that aŠects the per- members were found also when assessing diŠerences in formance of the reanalysis. In addition, it should be noted sea-level pressure elds (except polar regions) and fre- that the magnitude of biases may be in¢uenced by a selection quencies of circulation types over North America, Europe, Frequency (%) Frequency (%) Frequency (%) Frequency (%) SON JJA MAM DJF Frequency (%) Frequency (%) Frequency (%) Frequency (%) SON JJA MAM DJF Frequency (%) Frequency (%) Frequency (%) Frequency (%) SON JJA MAM DJF Advances in Meteorology 9 and East Asia. )e common deficiency in these three regions Acknowledgments was an overestimated frequency of zonal flow, which is )is work was supported by the Ministry of Education, probably associated with the known issue of GCMs not Youth and Sports of the Czech Republic within the Na- being able to correctly simulate the occurrence of atmo- tional Sustainability Program I (NPU I), Grant no. LO1415. spheric blocking [33, 34]. )e authors acknowledge the CNRM-CM5.1 global cli- mate model developed by CNRM-GAME (Centre Na- 6. Conclusions tional de Recherches Me´te´orologiques-Groupe d’e´tudes ` ´ ´ de l’Atmosphere Meteorologique) and Cerfacs (Centre We evaluated temperature and SLP biases and frequency Europe´en de Recherche et de Formation Avance´e). )e of circulation types in three historical ensemble members authors also acknowledge the Twentieth Century Re- of CNRM-CM5.1 GCM against the NOAA-CIRES 20th analysis Project version 2c dataset that is provided by the Century Reanalysis. )e main conclusions are summarised U.S. Department of Energy, Office of Science Biological as follows: and Environmental Research (BER), and by the National (i) )e CNRM-CM5.1 model had a relatively good Oceanic and Atmospheric Administration Climate Pro- skill in reproducing global temperature patterns; gram Office. )e authors thank Jan Stryhal, Institute of however, it exhibited drawbacks similar to those Atmospheric Physics, Prague, for his valuable comments reported for other CMIP5 GCMs. An oceanic on the draft of the paper. bias dipole pattern is present in CNRM-CM5.1, resulting in a too warm Southern Ocean and an References excessively cold North Atlantic Ocean and Arctic [1] J. 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