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212 3. Results I. Meinke, J. Roads, and M. Kanamitsu (2007) compared gridded observations of the Global Precipitation Climatology Project (GPCP) and the Global Precipitation Climatology Center (GPCC), as well as CEOP reference site precipitation observations with the RSM simulated precipitation for the first half of the CEOP Enhanced Observation Period (EOP) III (October 2002 to March 2003). After estimating the uncertainty ranges of both the model and the observations, model deficiencies were obtained for almost all model domains in terms of the amount of simulated precipitation. Although the RSM is able to accurately simulate the seasonal evolution and spatial distribution of precipitation, the RSM has an almost uniform positive bias (i.e., RSM values are greater than observed values) over almost all the domains. Most of the positive bias is associated with convection in the Intertropical Convergence Zone (ITCZ) or monsoonal convection in Southeast Asia. Predicted stratiform precipitation is also excessive over areas of elevated topography. As the control simulations used a Relaxed Arakawa-Schubert scheme (RAS), sensitivity tests with three additional convection schemes were then carried out to assess whether the simulations could be improved. The additional convection schemes were: 1) the Simplified Arakawa-Schubert scheme (SAS); 2) the Kain-Fritsch scheme (KF); and 3) the National Centers for Atmospheric Research (NCAR) Community Climate Model (CCM) scheme. The precipitation simulation was significantly improved for almost all domains when using either the KF scheme or the SAS scheme. The best simulations of ITCZ convective precipitation and Southeast Asian monsoon convective precipitation were achieved using the SAS convection scheme. B. Rockel and B. Geyer (2008) perfomed similar comparison as I. Meinke et al. but with the regional climate model CLM. As expected, the quality of the simulations for temperate and continental climates is similar to those over Europe. Tropical climates, however, display systematic differences with a land-sea contrast. Here, precipitation is overestimated over warm oceans and underestimated over land. Another similarity in all regions is the positive bias in precipitation occurring over high and narrow mountain ranges which stand perpendicular to the main wind direction. In these cases, the CLM produces higher precipitation values than those given in the Global Precipitation Climatology Project (GPCP) data set. A comparison to three other regional climate models indicates that the findings are not CLM-specific. It also stresses the major role of the convection scheme in tropical regions. The study confirms the assumption that in order to gain optimal results, one standard model setup is not appropriate for all climate zones. Dominique Paquin (Ouranos) has looked at a mini-ensemble of ICTS runs for the large Asia/Himalaya domain. In addition to the requested simulations over 7 domains, supplementary simulations with the CRCM over the GAME domain (Asia) were generated with the aim of estimating the internal variability of the model. This estimation is needed to assess how much of the inter-model variance observed in this domain can be explained simply by model internal variability (sensitivity to initial conditions), rather than model configuration differences. Two different configurations were used: a) the standard configuration of the model that includes spectral nudging of the horizontal wind in the higher levels of the atmosphere, and b) a configuration without spectral nudging. Each configuration was run twice with different initial dates (twin simulations). The internal variability responses of the two configurations are evaluated for temperature and precipitation over observation points. Time series and diurnal cycle are studied. Results show that at some locations internal variability for simulations without spectral nudging can be as large as are the differences between different model configurations or other models. Z. Kodhavala (University of Quebec) and others compared MOLTS data of ICTS regional model results with CEOP reference sites observations with respect to frequency distributions and diurnal cycle. B. Gutowski (Iowa State University) and others performed studies on the diurnal cycle. References Christensen , , J. H., T. R. Carter, M. Rummukainen, and G. Amanatidis, Evaluating the performance and utility of climate models: the PRUDENCE pro ject, Climatic Change, 81, 1–6, doi:10.1007/s10584-006-9211-6, 2007 Druyan , L., M. Fulakeza, and P. Lonergan, Spatial variability of regional model simulated June- September mean precipitation over West Africa, Geophys. Res. Lett., 34, L18709, doi:doi: 10.1029/2007GL031270, 2007 Kanamitsu , M., A. Kumar, H. M. H. Juang, J. K. Schemm, W. Q. Wang, F. L. Yang, S. Y. Hong, P. T. Peng, W. Chen, S. Moorthi, and M. Ji, NCEP dynamical seasonal forecast system 2000, Bulletin of the American Meteorological Society, 83 (7), 1019– 1037, 2002. Koike, T., The coordinated enhanced observing period – an initial step for integrated global water cycle observation, WMO Bulletin, 53, 9, 2004 Meinke, I., J. Roads, and M. Kanamitsu, Evaluation of the RSM Simulated Precipitation During CEOP, Vol. 85A, pp.145-166, 2007. Paeth , H., K. Born, R. Podzun, and D. Jacob, Regional dynamical downscaling over West Africa: Model evaluation and comparison of wet and dry years, Meteorol. Z., 14, 349–367 2005 Rockel , B., I. Meinke, J. Roads, W. J. Gutowski, Jr., R. W. Arrit, E. S. Takle, and C. Jones, The Inter-CSE Transferability Study, CEOP Newsletter, 8, 4–5, 2005 Rockel, B. and B. Geyer, 2008: The performance of the regional climate model CLM in dierent climate regions, based on the example of precipitation, Meteorol. Z., Volume 12, Number 4, 487-49 Takle, , E. S., K. Roads, B. Rockel, W. J. Gutowski, Jr., R. W. Arrit, I. Meinke, C. G. Jones, and A. Zadra, Transferability intercomparison: An opportunity for new insight on the global water cycle and energy budget, Bul l. Amer. Soc, 88, 375–384, 2005 Vizy E. K., and K. H. Cook (2002), Development and application of a mesoscale climate model for the tropics: Influence of sea surface temperature anomalies on the West African monsoon, J. Geophys. Res., 107(D3), 4023, doi:doi: 10.1029/2001JD000686., 2002
213 ENSEMBLES regional climate model simulations for Western Africa (the “AMMA” region) Markku Rummukainen 1 , Filippo Giorgi 2 and the ENSEMBLES RT3 participants 1 SMHI, SE-601 76 Norrköping, Sweden. Markku.Rummukainen@smhi.se 2 ICTP, Strada Costiera 11, IT- 34014 Trieste, Italy 1. Introduction This presentation introduces and discusses common aspects and lessons learnt in a co-ordinated regional climate modeling experiment for Western Africa, performed within the EU Integrated Project ENSEMBLES, see e.g. Hewitt and Griggs (2004), and collaborating with the AMMA project (Redelsperger et al. 2006). 2. Regional climate and Africa While the threat of climate change to Europe is real, the threat to other regions of the world is far greater both from a climate change perspective and due to vulnerability. This is especially true for Africa, as is emphasized in Boko et al (2007). At the same time, there is a deep lack of climate data on regional and local scales both on the recent past and on possible future conditions (cf. Christensen et al. 2007). Whereas information on the recent past conditions is important in characterizations of climate variability including extremes, as well as in development and evaluation of seasonal forecasting and impact models, future projections in turn are needed for weather and water related risk assessments and adaptation efforts. Estimates of regional/local climate change are therefore an important question for Africa. 3. ENSEMBLES regional climate modelling One research theme of the EU Integrated Project ENSEMBLES is on regional climate models (RCM). In particular, there is co-ordinated experimentation with a number of European and one Canadian RCM. These simulations are made in two patches. One is for a recent past period (not least for assessing RCM performance), using global reanalysis (ERA40, Uppala et al. (2006)) data as boundary conditions, in runs performed for Europe, followed with climate change projections, with ENSEMBLES global model projections providing the boundary conditions, run mostly with the SRES A1B emissions. Similar set-up is now made for Western Africa. The ENSEMBLES regional climate model study covers the so-called AMMA region. Again, there are two simulation streams. The first one covers 1989-2007, relying on ERA- Interim reanalysis data for boundary conditions (see www.ecmwf.int). As for Europe, a second stream consists of climate change projections, for the 1990-2050 period and run at 50 km resolution, relying on few GCMs for boundary conditions and again the A1B emission scenario (Nakićenović et al. 2000). The RCM runs for western Africa are performed by 11 regional climate modeling groups, with almost as many RCMs (HIRHAM (in two versions), RCA3 (by two groups9, RACMO2, RegCM, HadRM, REMO, PROMES, ALADIN CY28T3, CCLM). Whenever possible, the models are run with the same (minimum) domain with perfectly overlapping grids (so as to provide for easier comparisons). As some the RCMs have different co-ordinate systems, some of the models have slightly different grids compared to the body of the contributing models. Figure 1. ENSEMBLES general Western Africa minimum RCM domain. 4. ENSEMBLES – AMMA collaboration The RCM simulations have been set up and are evaluated in dialogue with the EU/AMMA project. Herein focus is on one hand on relevant climate aspects for significant impact applications, and on the other hand, the capture of important climate phenomena in the region. Indeed, whereas ENSEMBLES benefits from the experience of applying the RCMs in another region, thus putting them to test under a different climate regime (cf. Rockel et al. 2005), EU/AMMA gains a database covering the past two decades and extending to the middle 21 st Century. Compared to earlier, rather sporadic regional climate model simulations for the region, this constitutes a major resource for impact research. 5. Models and data The RCM runs have been underway in 2008-2009. Various evaluation data are collected. Especially the databases collected by AMMA are valuable for this purpose. The first analyses concerned temperature, precipitation and surface energy fluxes. Some of the comparisons were made for specific regions, such as river catchments. Continued evaluation efforts are to extend to look at the course and character of such meteorological phenomena as the timing of the monsoon season, diurnal cycles and variability on different scales. In many respects, the RCMs exhibit skill in reproducing the regional climate, but also deficiencies can be found. These provide information for model development efforts and also for identifying data needs. 6. Next steps The ENSEMBLES RCM efforts will include joint analyses and RCM intercomparisons for both the simulations over the recent climate period (see above) and the future scenario period. The model data will be available for other researchers’ efforts, thus facilitating
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2 nd International Lund RCM Worksho
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Associated Organisations ENSEMBLES
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Preface This Second Lund Regional-s
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II High-resolution dynamical downsc
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IV Investigation of added value at
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VI Soil organic layer: Implications
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VIII Session 4: Regional Observatio
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X Predicting water resources in Wes
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XII The impact of atmospheric therm
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XIV Observation and simulation with
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XVI Favot F .......................
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XVIII Ruoho-Airola T...............
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2 5. Influence of SN on the Ensembl
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4 (+/- 5 days) were used to increas
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6 Dynamical downscaling: Assessment
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8 Improvement of long-term integrat
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10 air temperature annual cycle (Fi
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12 Sensitivity study of CRCM-simula
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14 Regional precipitation anomalies
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16 Assessment of precipitation as s
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18 The Asian summer monsoon in ERA4
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20 Added value of limited area mode
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22 Sensitivity of CRCM basin annual
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24 High-resolution dynamical downsc
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26 There is nevertheless a large ag
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28 technique, as it is based on the
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30 4. Heating mechanism In order to
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32 The geographical distribution of
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34 The CCM scheme reveals a distinc
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36 Performance of pattern scaling i
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38 Evaluation of the analyzed large
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40 Sensitivity studies with a stati
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42 5SL, the results suggest that 5S
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44 are however occasional episodes
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46 this season, as well as the high
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48 Figure 1. Precipitation rate (mm
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50 References Biau. G., E. Zorita,
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52 Investigation of regional climat
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54 Selected examples of the added v
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56 The climate change in Europe sim
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58 Simulation of South Asian summer
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7.5 8.5 9.5 10.5 60 For the climato
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62 precipitation field was more clo
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64 3. Results Fig. 2 shows the anal
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66 Is the position of the model dom
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68 question E. Finally a 10-member
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Figure 2. Same as in Fig.1 but for
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72 Will, A., M. Baldauf, B. Rockel,
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74 ( ) with i = 1,K,n; j = 1,K,m;
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76 Investigation of precipitation o
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78 Impacts of the spectral nudging
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80 Extremes and predictability in t
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82 Large-scale skill in regional cl
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84 Figure 3: As Figure 1 but for Wi
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86 The models capture this pattern
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88 of the simulations due to the di
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91 Examining the relative roles con
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93 Dynamical coupling of the HIRHAM
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95 A parameterization of aircraft i
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97 Stratospheric variability and it
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99 Effects of numerical methods on
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101 Development of a climate model
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103 Study of the capability of the
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105 Evaluation of the land surface
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107 Simulation of the precipitation
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109 Climate simulations over North
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111 Soil organic layer: Implication
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113 4. Results The method how to do
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115 Figure 1. Observed (a) and simu
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117 Evaluation of the Rossby Centre
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119 Simulating aerosols in the regi
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121 Moisture availability and the r
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123 Temperature and precipitation s
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125 4. Preliminary results for down
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127 a) Knutson, T.R., J.J. Sirutis,
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129 Effects of variations in climat
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131 3.2. Precipitation The ensemble
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133 knowledge is essential to asses
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135 pronounced biases in the simula
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137 variability was concentrated at
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139 Investigation of ‘Hurricane K
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141 Evaluation of seasonal forecast
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143 NASH J. E., SUTCLIFFE J. V., 19
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145 Climate change assessment over
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147 For relative operating characte
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149 responsible for the excessive s
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151 and 30km). Domains D1 (90 and 6
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153 can be seen in Fig. 2, where th
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155 High-resolution simulation of a
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157 MesoClim - A mesoscale alpine c
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159 Observed and modeled extremes i
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Page 207 and 208: 181 Verification of simulated near
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Page 233 and 234: 207 Influence of soil moisture init
Page 235 and 236: 209 Projection of the Changes in th
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Page 241 and 242: 215 Assessing the performance of se
Page 243 and 244: 217 Applying the Rossby Centre Regi
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Page 249 and 250: 223 experiment of recreating the pr
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Page 253 and 254: 227 Use of regional climate models
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Page 269 and 270: 243 Very high-resolution regional c
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Page 279 and 280: 253 Application of regional-scale c
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263 Forest damage in a changing cli
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265 Future challenges for regional
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267 Linking climate factors and ada
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269 GCMs. In the end of the 21 st c
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271 Climate change impacts on extre
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273 Winter storms with high loss po
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275 The impact of land use change a
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277 6. Analysis of Results (Rain) T
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279 (a) (b) Figure 3. Impact of veg
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281 that cold (Fig. 5). Winter temp
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283 Streamflow in the upper Mississ
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285 Dynamical downscaling of urban
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287 Souma, K., K. Tanaka, E. Nakaki
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289 In April 2000, the fire emissio
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291 Impacts of vegetation on global
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International BALTEX Secretariat Pu
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No. 34: BALTEX Phase II 2003 - 2012
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