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Long term and high resolution observations of climate altering halogenated gases are carried out in several research stations worldwide distributed in the frame of long lasting programs, like AGAGE (Advanced Global Atmospheric Gases Experiment), or European funded projects, like SOGE (System for Observation of Halogenated Greenhouse Gases in Europe). Aims of long term observations are the definition of atmospheric trends, meanwhile a high temporal resolution of the measurements is needed in order to identify source regions and quantify emissions. For both purposes a careful evaluation of the atmospheric baseline is needed. Most of the station where the long term measurements are performed are classified as “baseline stations”, which are mainly under the influence of an airflow coming from clean sectors. Other stations, closer to source regions, are more frequently reached by polluted air masses. The Mt Cimone WMO-GAW (2165 m asl, 44°11’ N, 10°42’ E) Atmospheric Research Station is part of the SOGE network. It is located in the highest mountains of the Northern Apennines, to the south of the Alps and the Po Valley and to the north of the Mediterranean Sea. It is characterized by a 360° free horizon, with major towns and industrial areas situated in the lowlands, about 40 km away; Tyrrhenian and Adriatic Seas are respectively at about 45 and 130 km of distance. This work has been made possible thanks to our participation in the EC FP5 project SOGE and the EC FP6 NoE ACCENT. Ray Weiss and the SIO98 scale, The University of Bristol and the UB 98 scale are greatly acknowledged, as well as the colleagues participating in the SOGE consortium. The combination of models with observations from global networks , like AGAGE, where most of the stations are “baseline”, allows to quantify emissions of the halogenated gases on a global scale [Sthol et al, 2009, ACP, 9, 1597-1620] On the other hand, if the focus is the evaluation of emission on a regional scale, is more useful to combine models with data from continental station, which can be occasionally reached by polluted air masses [Maione et al., 2008, Sci. Tot. Env, 232-240] IMPORTANCE OF AN ACCURATE BASELINE CALCULATION Since emissions are estimated on the base of the occurrence of high concentration peaks (i.e. values exceeding the baseline), a careful evaluation of the atmospheric baseline is needed. Several different methods, based on both statistical and meteorological analysis, have been used for the evaluation of the baseline in remote stations. However, such methods are not applicable to the situation of Mt Cimone, where the polluted events are more intense and frequent. Here, a two-step procedure has been used: 1) In the first step, we calculate the Probability Density Function (PDF) of the biases of the mixing ratio values with respect to the median calculated on a 30-days moving window. The use of such window allows the detrending of the time series both on an interannual and seasonal basis. 2) The resulting PDF can be seen as the sum of two PDFs: a Gaussian representing the distribution of the “final” baseline and a Gamma PDF representing the distribution of contribution from fresh sources. As can be seen in Figure 2, the overlapping region includes both baseline values and contributions from weak and/or far away sources . The upper limit of the band including baseline values is set where the Gamma PDF exceeds the Gaussian PDF. The obtained baseline is represented in Figure 1 (baseline : yellow, average baseline: black ). Fig 1. Time series of HFC-125 recorded at Mt. Cimone. For explanation, see text Fig 2. PDF distribution of HFC-125 concentration data recorded at Mt.Cimone 0 100 200 300 400 500 600 700 -1.5 -1.2 -0.9 -0.6 -0.3 0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 A lldata gaussian pdf residualpdf ppt N ° ofD ata 1 2 3 4 5 6 7 8 9 10 HFC125 -M t.Cim one 2001 ppt 2002 2003 2004 2005 2006 2007 2008 EMISSION ESTIMATES. In order to identify source regions baseline data are subtracted from the full data set and an inversion modelling cascade, which makes use of MM5 (V 3.7) model to reproduce meteorological fields and of FLEXPART (V 3.1) to simulate tracer dispersion, is used to find the best emissions map that fits the observations. The method here proposed implies initially that concentrations at the receptor site, produced by a homogeneous arbitrary emission field, are simulated. The choice of enhancing factors, converting simulated concentrations into observed ones, could be assimilated to a multiple linear regression problem. Variations in the emission fields is evaluated as a function of annual variability. 0 1 2 3 4 5 simulated observed H FC 125 M aceH ead 2004 ppt Jan Feb M ar Apr M ay Jun Jul Sep O ct Nov Dec Aug 0 2 4 6 8 10 simulated observed H FC 125 M tC im one 2004 ppt Jan Feb M ar Apr M ay Jun Jul Sep O ct Nov Dec Aug R 2 =. 8 R 2 =.65 The comparison between the modelled and observed signals at two European stations is reported in Fig.4. It is clear as the model reproduces very well the observed. It is important underline that among the localised sources, sea areas are not included. Noteworthy, the sea represents more than one third of the entire domain, but contributes only by 3% to the reconstructed signal. Fig. 4: Sensitivity test for HFC-125 at two European Station 0 1 2 3 4 5 6 7 8 9 10 bestfit m ontly average H C F125 ppt 2001 2002 2003 2004 2005 2006 2007 2008 Baseline data are used to evaluate annual growth rate, trend acceleration, and sesonal cycles as reported in Fig. 3 Fig 3. Best fit for HFC-125 at Monte Cimone growth rate: 0.75 ppt y -1 - acceleration 0.08 ppt y -2 - R 2 0.98 -1 50 0 -1 30 0 -1 10 0 -900 -700 -5 00 -3 00 -1 00 100 300 500 700 900 1100 1300 1500 -1 3 00 -1 2 00 -1 1 00 -1 0 00 -9 00 -8 00 -7 00 -6 00 -5 00 -4 00 -3 00 -2 00 -1 00 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 1 0 to n n /ye ar 5 ton n/ye a r 2 ton n/ye a r 1 ton n/ye a r 0 .5 to nn /ye a r 0 .2 to nn /y ea r 2 0 tonn/year The results obtained with one year of data at Mace Head show that the resolution of the model is acceptable only in regions close to the receptor site. That could be due to the length of the time series. Nevertheless, the use of a longer time series could be the interannual variability of the source field. Therefore, further work should imply the use of cross fields produced by different receptors, in order to improve the resolution on the whole domain.

Long term observations of climate altering gases for deriving emissions on a regional scale

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Long term observations of climate altering gases for deriving emissions on a regional scale. Michela Maione 1 , Umberto Giostra 2 , Francesco Furlani 2 , Jgor Arduini 1 1 University of Urbino, Institute of Chemical Sciences , 2 University of Urbino, Institute of Physics , - PowerPoint PPT Presentation

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Page 1: Long term observations of climate altering gases for deriving emissions on a regional scale

Long term and high resolution observations of climate altering halogenated gases are carried out in several research stations worldwide distributed in the frame of long lasting programs, like AGAGE (Advanced Global Atmospheric Gases Experiment), or European funded projects, like SOGE (System for Observation of Halogenated Greenhouse Gases in Europe). Aims of long term observations are the definition of atmospheric trends, meanwhile a high temporal resolution of the measurements is needed in order to identify source regions and quantify emissions. For both purposes a careful evaluation of the atmospheric baseline is needed. Most of the station where the long term measurements are performed are classified as “baseline stations”, which are mainly under the influence of an airflow coming from clean sectors. Other stations, closer to source regions, are more frequently reached by polluted air masses.

The Mt Cimone WMO-GAW (2165 m asl, 44°11’ N, 10°42’ E) Atmospheric Research Station is part of the SOGE network. It is located in the highest mountains of the Northern Apennines, to the south of the Alps and the Po Valley and to the north of the Mediterranean Sea. It is characterized by a 360° free horizon, with major towns and industrial areas situated in the lowlands, about 40 km away; Tyrrhenian and Adriatic Seas are respectively at about 45 and 130 km of distance.

This work has been made possible thanks to our participation in the EC FP5 project SOGE and the EC FP6 NoE ACCENT. Ray Weiss and the SIO98 scale, The University of Bristol and the UB 98 scale are greatly acknowledged, as well as the colleagues participating in the SOGE consortium.

The combination of models with observations from global networks , like AGAGE, where most of the stations are “baseline”, allows to quantify emissions of the halogenated gases on a global scale [Sthol et al, 2009, ACP, 9, 1597-1620]

On the other hand, if the focus is the evaluation of emission on a regional scale, is more useful to combine models with data from continental station, which can be occasionally reached by polluted air masses [Maione et al., 2008, Sci. Tot. Env, 232-240]

IMPORTANCE OF AN ACCURATE BASELINE CALCULATIONSince emissions are estimated on the base of the occurrence of high concentration peaks (i.e. values exceeding the baseline), a careful evaluation of the atmospheric baseline is needed. Several different methods, based on both statistical and meteorological analysis, have been used for the evaluation of the baseline in remote stations. However, such methods are not applicable to the situation of Mt Cimone, where the polluted events are more intense and frequent. Here, a two-step procedure has been used: 1) In the first step, we calculate the Probability Density Function (PDF) of the biases of the mixing ratio values with respect to the median calculated on a 30-days moving window. The use of such window allows the detrending of the time series both on an interannual and seasonal basis. 2) The resulting PDF can be seen as the sum of two PDFs: a Gaussian representing the distribution of the “final” baseline and a Gamma PDF representing the distribution of contribution from fresh sources. As can be seen in Figure 2, the overlapping region includes both baseline values and contributions from weak and/or far away sources . The upper limit of the band including baseline values is set where the Gamma PDF exceeds the Gaussian PDF. The obtained baseline is represented in Figure 1 (baseline : yellow, average baseline: black ).

Fig 1. Time series of HFC-125 recorded at Mt. Cimone. For explanation, see text

Fig 2. PDF distribution of HFC-125 concentration data recorded at Mt.Cimone

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EMISSION ESTIMATES. In order to identify source regions baseline data are subtracted from the full data set and an inversion modelling cascade, which makes use of MM5 (V 3.7) model to reproduce meteorological fields and of FLEXPART (V 3.1) to simulate tracer dispersion, is used to find the best emissions map that fits the observations.The method here proposed implies initially that concentrations at the receptor site, produced by a homogeneous arbitrary emission field, are simulated. The choice of enhancing factors, converting simulated concentrations into observed ones, could be assimilated to a multiple linear regression problem. Variations in the emission fields is evaluated as a function of annual variability. 

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The comparison between the modelled and observed signals at two European stations is reported in Fig.4. It is clear as the model reproduces very well the observed. It is important underline that among the localised sources, sea areas are not included. Noteworthy, the sea represents more than one third of the entire domain, but contributes only by 3% to the reconstructed signal.  

Fig. 4: Sensitivity test for HFC-125 at two European Station

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Baseline data are used to evaluate annual growth rate, trend acceleration, and sesonal cycles as reported in Fig. 3

Fig 3. Best fit for HFC-125 at Monte Cimone

growth rate: 0.75 ppt y-1 - acceleration 0.08 ppt y-2 - R2 0.98

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1 65 1 66 1 67 1 68 1 69 1 70 1 71 1 72 1 73 1 74 1 75 1 76 1 77 1 78 1 79 1 80 1 81 18 2 18 3 18 4 18 5

1 86 1 87 1 88 1 89 1 90 1 91 1 92 1 93 1 94 1 95 1 96 1 97 1 98 1 99 2 00 2 01 2 02 2 03 2 04 20 5 20 6 20 7 20 8

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2 73 2 74 2 75 2 76 2 77 2 78 2 79 2 80 2 81 2 82 2 83 2 84 2 85 2 86 2 87 2 88 28 9 29 0 29 1 29 2

2 93 2 94 2 95 2 96 2 97 2 98 2 99 3 00 3 01 3 02 3 03 3 04 3 05 3 06 3 07 3 08 3 09 31 0 31 1 31 2 31 3

3 14 3 15 3 16 3 17 3 18 3 19 3 20 3 21 3 22 3 23 3 24 3 25 3 26 3 27 3 28 3 29 3 30 3 31 33 2 33 3 33 4 33 5

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The results obtained with one year of data at Mace Head show that the resolution of the model is acceptable only in regions close to the receptor site. That could be due to the length of the time series. Nevertheless, the use of a longer time series could be the interannual variability of the source field. Therefore, further work should imply the use of cross fields produced by different receptors, in order to improve the resolution on the whole domain.