Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 4 1 Chemistry , M. Butzin 2 and Physics Discussions Atmospheric , and V. Zakharov 4 , F.-M. Breon 2 , M. Werner 1 , J.-L. Bonne 2600 2599 1,2,3 , N. Rokotyan 2 , V. Bastrikov 2 , J. Jouzel 1 , V. Masson-Delmotte 2 This discussion paper is/has beenand under Physics review (ACP). for Please the refer journal to Atmospheric the Chemistry corresponding final paper in ACP if available. Climate and Environmental Physics Laboratory, UralInstitut Federal Pierre University, Simon Russia Laplace, Laboratoire des Sciences du ClimatInstitute et of de Industrial l’Environnement, Ecology UBAlfred RAS, Wegener Russia Institute for Polar and Marine Research, Germany 1 2 France 3 4 O. Cattani Received: 2 January 2013 – Accepted: 14Correspondence January to: 2013 K. – Gribanov Published: ([email protected]) 24 JanuaryPublished 2013 by Copernicus Publications on behalf of the European Geosciences Union. ECHAM5-wiso water vapour isotopologues simulation and its comparison with WS-CRDS measurements and retrievals from GOSAT and ground-based FTIR spectra inatmosphere the of Western Siberia K. Gribanov Atmos. Chem. Phys. Discuss., 13, 2599–2640,www.atmos-chem-phys-discuss.net/13/2599/2013/ 2013 doi:10.5194/acpd-13-2599-2013 © Author(s) 2013. CC Attribution 3.0 License. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ; D at δ ) in v v mea- D 2007 D δ v , δ in atmo- D ciently ro- of surface v δ ffi v O ) is now un- usivity in air O Lacour et al. Steen-Larsen 18 ff ; δ 18 δ 2004 ( ). and 2012 Nassar et al. and v , ; v D 2012 , δ culty partly explains why D b δ , ffi 2007 erent types of observations. )) varies both spatially and , ff composition of water vapour. v Field et al. O soundings, hourly ground-based 2010a 1961 ; Zakharov et al. , v . So far, very few intercomparison , 18 ) to polar locations ( v ). These devices are su D δ O δ 2009 2006 18 Craig 2012 Payne et al. , , 2009 δ and ; , , v D and erent climatic conditions. δ 2602 2601 ff 2006 v amounts, and in situ hourly Picarro Brand erent techniques have been achieved at a given , D v ff δ D δ Herbin et al. ; Tremoy et al. Schneider et al. 2012 O molecules, fractionation processes occur during phase , 18 2 Worden et al. 2009 erences in the saturation vapour pressure and di culties intrinsic to the comparison of integrated with local measure- ff , ffi O and H 16 ) where they have revealed significant diurnal to seasonal variability in re- ered opportunities for monitoring atmospheric water vapour isotopic composi- O expressed in ‰ versus VSMOW ( ff O, HD 2012 18 ) and provides large scale, integrated measurements. Data from ground based , δ 16 2 A third major breakthrough has been accomplished when new infra red (IR) laser The situation has recently changed thanks to technological advances which now This research was supported by the grant of Russian government under the contract stable isotopes must be assessed for di lationship with air massPrior origins, to convection the and deploymentwater surface-atmosphere of vapour moisture a will fluxes. network be of continuously stations monitored, where the the information brought by water vapour bust to allow fieldAfter measurements the of development the ofence calibration waters protocols, and which corrections require forbeen humidity the and introduction deployed instrumental of from drift, refer- suchet tropical instruments al. have ( have became commercially available ( spectrometers have reached the same level of precision as mass spectrometers, and water vapour from instrumentsAtmospheric Composition both Change) from sites theObserving and Network) NDACC from network the (Network ( TCCON for (Total the Carbon Column Detection of 2012 high resolution Fourier Transformto Infrared retrieve (FTIR) information spectrometers about have vertical been exploited profiles of water stable isotopes (mainly allow for either inspheric situ water measurement vapour. The or quantification remote ofbased water estimation on isotopes of in remote tropospheric sensing waterder vapour techniques rapid pioneered development by ( Frankenberg et al. to sample than atmosphericapplications water vapour. dealing This with sampling studies di ics of have atmospheric long been processes limited andhydrology and while atmospheric isotope they dynam- paleoclimatology have (from rapidly ice developed cores in and such other fields archives). as isotope knowledge of their present-day distribution has focused on precipitation, much easier and 11.G34.31.0064. 1 Introduction Owing to slightof di H changes of the water. As a result, the distribution of the water isotopes (hereafter Kourovka, Western Siberia: dailyFTIR mean GOSAT total atmospheric columnar surements. There is ansurface excellent while correlation between the observed comparisoncompares and between well predicted with water FTIR column and values GOSAT estimates. derived from the model tion. Recently developed infrared lasersurements spectrometers allow of for surface continuous in waterof situ vapour mea- measurements conducted using di location, due to di ments. Nudged simulations conducted withprovide high a resolution consistent framework isotopically for enabled comparisonHere, GCMs with we the di comparetypes simulations of conducted water with vapour isotopic the data ECHAM5-wiso obtained during model summer with 2012 three at the forest site of Water stable isotopes providefected integrated by tracers changes of incontinental the air recycling. Novel atmospheric mass remote origin, water sensingcently and cycle, non-convective o in and af- situ convective measuring techniques processes have and re- temporally in the atmospheric water vapour and in the precipitation. Until recently, our Abstract 5 5 25 20 15 10 15 20 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Risi Dee 96.4 ‰ C (Jan- ◦ − 16 − D of δ erences between ). High resolution Frankenberg et al. ff ), the amplitude of ) or convection ( 1987 , 2012a 2005 ( ). While , 2004 , ) has brought together and com- b , Risi et al. ). This site is characterized by a well Jouzel et al. erences between data sets but their 1 ff ; over the Kourovka region using GOSAT, Schmidt et al. 2012a v ( D 1984 δ , 2604 2603 Hamazaki et al. S, see Fig. ◦ Risi et al. ). This intercomparison will focus on a relatively short ) aimed to use all available isotopic information with ). Its position in a pristine peatland and near the per- ) extracted the most robust features and then used the 2009 2010 , 2012b , ( N, 59.545 ◦ Joussame et al. 2012a ( culty which however is largely circumvented by applying a rigor- ffi erent air mass trajectories and summer continental precipitation re- ff Risi et al. 12.76 ‰ as measured at LSCE by IRMS); (ii) YEKA (made by mixing C (July) and about 460 mm of annual precipitation, peaking in summer. − ◦ erent reference water samples: (i) DW (distilled water with Risi et al. ). Berrisford et al. ff ; 17 + Shalaumova et al. O of ected by di 2012a 18 2011 ) have exploited the short-wave infrared GOSAT spectra to estimate the HDO col- ff , , δ Such data model intercomparison is a prerequisite if we want to use the variety of Here, we propose a complementary approach which consists in focusing on one In parallel, our ability to describe and simulate the distribution of water isotopes us- In a comprehensive approach, 2009 using two di and (T63) that has been run in a nudged versionperiod using between ERA-Interim April reanalysis and fields September ( 2012. 2 In situ isotopic measurements ofA surface Picarro water vapour laser instrument2013. Laboratory of tests were type conducted in L2130-i order to was verify received the reproducibility at of the Ural device University in March uary 2009 in a sun-synchronous orbit ( umn, we have for the samewavelength. purpose Here, developed we an therefore algorithm(PICARRO, using inter-compare FTIR the these and thermal three GOSAT) infrared using independent the data outputs sources of the ECHAM5-wiso isotope AGCM mafrost zone is strategic forcarbon the budgets. monitoring At of this the site,vapour coupling measurements we between (PICARRO surface have L2130-i water access instrument). and both Ina addition, to specific we method ground have to based developed retrieve (FTIR)the total and Japanese column Greenhouse in gases situ Observing Satellite, which was launched( on 23 Jan- et al. uary) to It is a cycling ( the consequence that the various data sets do not cover the same periods and the ous model-data comparison methodology. site, the Kourovka Observatory (nearWestern Yekaterinburg, Siberia, close 57.038 to themarked western continental boundary climate, with of monthly mean temperatures varying from seasonal variations, the meridional isotopicconvective gradient tropical and the regions contrast were betweenIGCMs underestimated dry involved and by in LMDZiso the SWING2 ascomparison (Stable project. well Water as INtercomparison by Group six phase 2) other inter- information on isotopic distribution inbased atmospheric and water in vapour (satellite situspheric data, measurements) ground processes to in diagnose GCMstheir biases approach or in infer the information representation about, of e.g. atmo- continental recycling. In same locations, a di LMDZ IGCM (LMDZiso) to understand andthese quantify the data sources sets. of di Theycommon features pointed appear to to significantand be mid di remarkably troposphere, well at reproduced large by scale. LMDZiso However, in in the lower comparison pared satellite data setsPAS) and from ground various based instruments remotein (SCIAMACHY, sensing TES, situ (FTIR ACE techniques at and (surface the vapour MI- NDACC measurements and and TCCON in sites) situ and aircraft data). From this ing atmospheric general circulationbedded models (IGCMs) in has which madeducted fractionation considerable processes in progress are the since em- eighties the pioneering ( studies con- framework. Sensitivity studies to uncertainshown atmospheric the model potential parameterizations of have water vapourprocesses isotopic linked data to, to e.g. constrain cloud theet microphysics representation al. of ( key atmospheric models can nowfor be precise nudged comparisons with to measurements atmospheric in analyses a consistent products, large allowing scale meteorological 5 5 25 10 15 20 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , are cal- v O 18 Tremoy et al. δ 36.71 ‰). A third C). The sampling − ◦ and are above 600 ppm, v 54.05 ‰) is also used v D O of − D δ 18 δ δ O of ect, air measurements were ff 18 and δ v O 18 erent water isotopes, inducing spuri- δ ff 289.0 ‰ and − 424.1 ‰ and 2606 2605 D of − δ D of δ erent humidity levels, from 1000 to 20 000 ppm. These ff erent retention times for di ff ects. The length of the sampling line is 6 m, air being sampled about 7 ma.g.l. ff ) in order to minimize water vapour adsorption, as it was shown that Dekabon Every single ambient air measurement was processed in two stages. At first, the two These frequent calibrations allow to assess the stability of the measurements. Start- The quality of the post-processed data strongly depends on the stability of the cal- Because measurements are sensitive to humidity levels, it is required to establish The measurement protocol consists of continuous ambient air measurements dur- The instrument was installed in Kourovka Observatory in mid March 2013, inside the total number of successful calibrations was 128 for DWclosest and valid YEKA pairs together. of referenceto standard the measurements were same independently humidity corrected were level obtained as on for the the basisand of air were the as measurement. humidity follows: Humidity response correction investigation as functions described above so far to very stable calibrations. ibrations. Water standard measurementsaccount are when considered standard unstable and0.8 deviations ‰ not of and taken 3 humidity, ‰, into respectively. During the measurement period (from April to November) processed only after a timethis delay particular of 13 PICARRO min. device This during time its period installation was and found calibration. appropriateing for from June 2012,one instabilities of were the identified SDM duringitself syringes. calibrations, by This exchange due with may to ambient lead leakage air,instabilities to and in (ii) of two introduction injected biases: of (i) flux airsubsequently bubbles fractionation and into replaced in the resulting using SDM the measurements. a and syringe The new type calibration of module glass was syringe in September 2012, leading not always be achieved, but resultingresponse errors as can be previously corrected described. usingcalibration As estimated with humidity for Standard the air Deliveryhigh measurements, variability Module after in (SDM) switching the thereference from measurement standards instrument in results demonstrated the because very system. of To residual account for traces this of e water from the surrounding atmosphere. Due to high variability of ambient air humidity, this can- 1 ‰ and 0.2 ‰principle, one respectively should are introduce reported reference for waters humidity at humidity levels levels around close 15 000 to ppm. those In of response functions were determinedused to in-situ derive the in required April corrections. 2012 and splineing 6 functions h, were automatically switchingDW to and calibration YEKA sequence referenceAltogether, of waters each successive mixed calibration vaporization with sequence lasts of DRIERITEdurations. about dried Mean 60 min air values after during and accountingculated 30 standard for along min pumping deviations the each. in last 20 humidity, min of calibrations. Typically, standard deviations of 200 ppm, Auto-regulated temperature control along theinput line is ensures protected stable against temperature. raindrops The by air a hard cover andcalibration against response insects by functions areference as net. waters a injected function at di of humidity, based on measurement of Instruments MetPak-II meteorological stationments which of provides atmospheric pressure, every wind secondhumidity. speed The measure- and station direction, is air implemented temperature inset the and up middle relative of to a warrant pineline forest. stable consists Air temperatures conditioning of was inside O’Brienmaterial the optical room was quality (around selected stainless 18 steel based2012 tubing on (3/8 sensitivity inch teststype diameter). conducted material This has at di LSCEous ( e Antarctic snow with distilled water with depleted standard (DOMEC with to assess the linearity of the system. the same room as the FTIR spectrometer. Also the Kourovka site is equipped with Gill 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . , v 2 q and 0 D δ cient be- 246 ‰ on ffi − a) and daily ). In line with 3 3.1 ) considers the iso- 1, in which −
1) 1964 − , ) hourly (Fig. 92 ‰ on 10 May). Indeed, m v α − ( q ) c) or daily temperature in the 0 3 103 ‰ reached on 14 July and /q v − q ) while the link with site temperature )( v Dansgaard 0 q D ) versus ln( δ v D + δ (1 2608 2607 + variations are superimposed on this seasonal ) and ln( = v v v D D D δ δ . This model ( δ v + 232 ‰ on 5 April ) and in fall (minimum q − for DW, dataset extends to 21 November. We have, in Fig. v v and D δ /q ) for YEKA standard, v versus the site temperature using either hourly data for the pe- 3 d) as derived from reanalysis data (see Sect. v ) and v 3 q v stands for the average value of the fractionation coe /q 10 D 4 q δ × ln( m 10 ln( α / / × 4.86 38.7 − , temperature and 449 2.8 v + − + in the water column, we will hereafter focus on this parameter both for the D v δ 13.3 42 , is well approximated by: stands for humidity (ppmv). And secondly, humidity corrected measurements D v − − 98.8 343 v δ b) means and D = = − − q v v 3 δ = = O O are the deuterium content and the amount of water vapour at the oceanic origin of v v With this in mind, we have plotted ln(1 Although much too simplistic, a Rayleigh type model helps to understand this link As expected, the deuterium time series shows a clear seasonal cycle with it low- Finally, we use both hourly and daily average values to present this data set and com- The currently available Although this methodology worked mostly correctly, sometimes the PICARRO SDM D D 18 18 0 riod over which we haveKourovka measurements gridbox at (Fig. the site (Fig. q the airmass while tween the oceanic source and theprevailing sampling at site. the Assuming no oceanic change sourcea in linear (which the relationship again conditions between is ln(1 tooresults from simplistic) the this Clausius–Clapeyron should equation. translate in (Fig. between topic fractionation occurring inassumed an to isolated form air in parcel isotopic equilibriumimmediately in with which from the the the surrounding vapour condensed parcel. and phase Thesite, to is be isotopic removed composition of the water vapour at a given 11 August). While the highesthighest monthly single mean hourly values are value observedlarge, is in and recorded June–August, for to the some occurcycle of in them, which spring( rapid will betions fully are described more when pronouncedthe winter in summer during data fall which will with no fluctuation be amplitudesvariations exceeds in available. reaching 45 temperature ‰. These about and They to are fluctua- 100 associated clearly ‰ changes related than in to the large during amount of water vapour, surements at theentire site period available (as since used for 1 the April simulation). or ERA-interimest values reanalysis in data spring18 for (minimum the November) and highest levels during summer ( been reported on the same figure along with surface temperature using either mea- mean) limited to periods oversively. which The there amount are at of least water 6 vapour hourly measurements (as succes- measured by the PICARRO instrument) has pare it with ECHAM5-wiso model2012 results. over which For we the will periodsurements compare from were data 1 produced and April which model corresponds results, toboth to 2476 30 FTIR 67 hourly September % PICARRO (Sect. of mea- 4) the andabout total GOSAT duration (Sect. (3671 h). 5) As datadiscussion are of specifically the used dataset to and get its information comparison with modeldisplayed results. both hourly individual measurements and a smooth curve (5 point running experienced breakdowns andswitches data automatically acquisition back stopped. toprotocols. In air In this measurements order case, and tocessed the data fulfill and PICARRO are missing missing stored SDMapplied data in afterwards. data, were its this pulled logging logging out. protocol The was same also processing repro- methodology was were of reference standards wereA linearly linear interpolated regression between to the theof standards time measured these values of against standards the theoretical wereThe air values validity calculated measurement. of and calibrations theninstrument. is applied estimated to based on ambient the air evaluation measurements. of the stability of the δ δ δ δ 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), ). On 2011 ). Both , 0.46) or is also in = 2011 v ). The sim- ). The ver- 2 2006 , ◦ set between ), as well as D r , ff 10 ‰. A com- δ 0.67) between 1.9 ± 2011 = 0.72) at a similar × , 2000 2 ◦ = r , Langebroeck et al. with temperature is 2 v ) and ECHAM4 (e.g. Werner et al. r D δ IPCC Werner et al. 1998 erent GNIP stations. Model ; ). Furthermore, it has been , ff Roeckner et al. 0.71) when considering daily ; = Werner et al. 2 2011 2006 r ) and confirmed by observational , , 2006 , 2005 mann et al. erences in the range of ff , ff usivity of the water isotopes. Processes ff Ho ). Additional fractionation processes are de- IAEA-WMO 2610 2609 1996 O in precipitation are rather a regionally integrated , 18 Hagemann et al. ) analogous to the isotope modelling approach used Langebroeck et al. δ ; Schmidt et al. 2011 2003 , ). , O and HDO have been explicitly implemented into its hydro- ) shows the same latitudinal gradients, but an o v q 18 2 Lin and Rood 0.49, not shown). It increases for daily data ( 2009 ) for hourly data which increases ( ). For each phase of “normal” water (vapour, cloud liquid, cloud ice) = , v 2 q r 2001 Werner et al. , Roeckner et al. ) and ln( v D ) indicate that variations of δ + ECHAM5-wiso has been validated with observations of isotope concentrations in Based on our previous findings, we employ in this study the ECHAM5-wiso model Frankenberg et al. 2011 ( signal of several climate variables thantion a changes. proxy This for finding either is local notby temperature just or other valid precipita- modeling for ECHAM5-wiso results results, (e.g. but also supported fairly good agreement with recent observationsvalues from and five measurements di agreeparison well of with the di ECHAM5-wiso simulationsmined with by total the column SCIAMACHY averaged( instrument HDO data on deter- board the environmental satellite ENVISAT precipitation and water vapour ( ulated near-surface isotopic composition of atmospheric water vapour 20–50 ‰ of unknown origin. Focusing on Europe, the results by the other hand non-equilibriumchange, processes and depend therefore even onwhich on the involve the fractionation molecular velocity processes of di they include the can the phase be neglected evaporationto during from ice, evaporation the and from re-evaporation ocean of land), (while liquid condensation precipitation. either to liquid or present-day insolation and greenhouse gas concentrations ( the corresponding phase change is slow enough to allow full isotopic equilibrium. On data (GNIP and ERA-40). with a medium-finetical horizontal resolution spectral is resolution 31 T63 hybrid (about levels. 1.9 The model is forced with prescribed values of a global and Europeanlation scale, results are the in annualwork good as of agreement well Isotopes with in as availableshown Precipitation, observations seasonal GNIP from that ECHAM-5-wiso ( the the simu- Global simulationan Net- of increased water horizontal and isotopes vertical in model precipitation resolution does ( clearly improve for being transported independently inimplemented ECHAM5, a in corresponding the isotopic modelidentically counterpart code. in is The the isotopes GCMtransport and as scheme long the both as “normal” for no active waterresponding phase tracers are passive transitions (moisture, described are tracers cloud liquid concerned. (moisture,the water) Therefore, cloud flux-form and the semi-Lagrangian for water the transport andmented cor- scheme in cloud ECHAM5 for ice ( positive of definite the variables imple- isotopes) is ECHAM5 ( stable water isotopes H logical cycle ( in the previous modelWerner releases et ECHAM3 al. ( occurs in ECHAM5. Two types ofequilibrium fractionation and processes non-equilibrium are processes. considered An in the equilibrium model: fractionation takes place if level as observed for ln( 3 The ECHAM isotopic model and3.1 comparison Model setup Atmospheric simulations were carriedwhich out is using ECHAM5-wiso the ( isotope-enhanced version of the atmospheric general circulation model fined for the water isotope variables whenever a phase change of the “normal” water the primary driver of isotopicln(1 changes, there is a strong correlationdata ( and thus eliminating theweaker diurnal for cycle. hourly The data correlationreanalysis either of data using ( local meteorological measurements ( the Rayleigh model in which the remaining fraction of water remaining in the cloud is 5 5 15 20 25 10 20 15 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) C v ◦ D C at δ ◦ C oc- ◦ 4 + C warmer ◦ C again. erence be- C) between ◦ ≈ ff ◦ ) with ERA-40 ), i.e. modeled 1–3 2.2 Server, operated + ≈ C. This is slightly ◦ 2012 , with reanalysis data, 20 ). If we compare clima- 1 ≈ values (hereafter erences of the simula- 1.0 to − ff air. + Rast v 1 D 2009 − δ , SMIS SRI RAS , ). 2009 , C. Superimposed on this diurnal cycle, ◦ in the lowest atmospheric model layer also air. From mid-July to end of September, the ; implemented by v Berrisford et al. 1 q ; 2612 2611 5–10 − ≈ 1991 C. On the seasonal time scale, the di , 2011 ◦ , air) between the beginning of April and early May, it Berrisford et al. ; 1 ). A clear diurnal cycle is evidenced with typical day- − 4 100 ‰ at the Kourovka site between April and Septem- − 2011 , Dee et al. 200 to − Krishamurti et al. Dee et al. ). The model shows isotopic variations of 30–50 ‰ over a few days, 4 values then fall back into the range 5–10 gkg values are found in early April and early May as well as in mid to late v v q D δ The simulated amount of water vapour ECHAM5-wiso simulates surface-level water vapour Our simulation starts on 1 January 2000, with an internal model time step of 12 min. For the period April to September 2012, an analysis of ERA-40 and ERA-interim sur- In order to allow a comparison with observations at the sub-seasonal scale, the Although the hydrological cycle in our ECHAM5 setup is fully prognostic and not over which are superimposed smallerlowest short-term fluctuations lasting a few hours. The shows strong temporal variations at ain time the scale air of is a rather fewrises days. low thereafter While to (3–6 the gkg values water of content simulated up to 15 gkg mostly in the range ber 2012 (Fig. changes can be astween large as low 10–15 temperature valuestemperature in maximum April in and mid-July September,higher respectively, to than and mid-August the the adds summer climatologicalKourovka up varies observations to between from 960 hPa Yekaterinburg.day and Surface variations 1000 but hPa. pressure clearly This at lacks record both also a shows diurnal some and multi- seasonal cycle. temperatures in all Siberian regions have been anomalous warm3.2 by Model results We briefly describe thethe simulated location near-surface of temperature Kourovkaversus-night and (Fig. temperature surface changes pressure of at the temperature record reveals strong variations within a timescale of a few days. These 1999). Strongest above-average warmingcurred with in temperature April and anomalies June, while of than in May average, and only. July temperatures For wereKourovka August, about and 1–2 we adjacent find regions of stillperatures of Western an the Siberia, same above-average but order warming of also magnitude of cooler in than 1–2 large parts average of tem- East Siberia. For September, warm, as compared to the long-time average temperatures (reference period 1960– tological means of measured surfaceby temperatures the in Space Yekaterinburg Monitoring ( Information Support laboratory face temperature data reveals that the region around Kourovka station was anomalous plicit nudging (e.g. and the agreement withsidered. observations The simulated further total improvesoverestimated column if by water 4–6 monthly mm vapour compared (TCWV) averages with tends are reanalysis to con- fields. be systematically For comparison with thealyze available isotope simulation observational results recordsate for at model Kourovka, the results we period with an- Kourovka, a April we are temporal to using resolution values September of at the one 2012. model hour, We grid if point always not closest evalu- stated to the otherwise. station. For ECHAM5-wiso model isscale nudged atmospheric to dynamics isthermodynamic reanalysis state correctly data, of represented. the which Every model six ensures atmosphere hours is that the constrained the to dynamic- observations large by im- December and April. nudged to thetion ERA-Interim results data, as in comparedinstance, Western to modelled Siberia daily the precipitation hydrometeorological the agrees reanalysis di within fields 1 are mmday small. For fields of surface pressure,Interim temperature, reanalysis divergence fields ( and vorticity are relaxed to ERA- climatology data, we findERA-40 a mean monthly good surface agreement temperaturesfor show of the a the period small May–November, temperature warm and bias seasonal slightly of larger cycle. less deviations The than ( 1 reanalysis data ( with sea-surface temperatures and sea-ice concentrations according to ERA-Interim 5 5 25 15 20 10 10 25 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | v v r D O δ 18 δ 0.77) is (defined strength- values at = 0.73) than v d and v r higher than D in precip- q = v D δ r r D δ ( δ are only weakly 0.89). Absolute v and v q = v D cient r D δ values near surface ffi 0.65). In contrast to δ and v + v D D ≥ ). Modeled surface pres- δ cient ( δ r and surface pressure are ffi v q 2011 , , v 0.2 in all cases). D cients ( set, a high correlation ( ff < δ 0.60). Our results support previous ffi | r = | O of the various water reservoirs and signal of total water column is depleted r ( hourly variations. This result shows that v 18 v 20 ‰. The potential use of the deuterium D δ v q cient δ + D 0.70), while variations of ffi δ 2614 2613 N, with a correlation coe ◦ = r ( Langebroeck et al. v ; q 5 ‰ and 7.99 and a mean deuterium excess value 0.56) and values are less depleted. A distinct peak event in + = v = 2005 3066) as compared to the total number of hourly modeled r D , m = δ values of the total water vapour column and 10.2 ‰. Between April and September, the modeled hourly E and 48–66 c, red lines). The model correctly captures the patterns and erent transport regimes of moisture towards Kourovka will be n (correlation coe v + ◦ 2 ff v D variability at Kourovka is dominated by the synoptic variability, δ D v , the multi-day variations of δ D 4 (Fig. O) of δ : model data comparison D, the isotopic signal of values are often 30–40 ‰ less depleted than the corresponding PI- 0.97, respectively, for hourly values between 1 April and 30 Septem- v v variations on daily and synoptic time scales are often not strongly cor- 18 δ 0.61), only. v q D v δ = 0.60). We note that this might be partly influenced by the lower number D δ = Schmidt et al. D r × δ = δ r 4392) available for the period between April and September 2012. How- 8 r − = D 0.9). n δ > = 20–30 ‰. Precipitation occurs at 1216 1-h intervals between April and Septem- 0.90 and 0.997), with a slope of 70 ‰ as compared to the surrounding vapour. ≈ d = = The PICARRO data exhibit a stronger correlation between Simulated As seen in Fig. Both the simulated In addition to From correlation analyses (not shown) of the simulated daily mean 60 m) while the PICARRO measurements were carried out at a height of 7 m. + 0.5. A similar correlation pattern is found for variations of the water vapour amount , but with slightly higher mean correlation coe r r CARRO measurements exist, the simulatedens correlation slightly between ( values 3.3 Surface The ECHAM5-wiso resultsPICARRO are data first compared to the observed hourly water vapour obtained between model andthe observed intra-seasonal which is correctly resolved by the model in the nudgedsimulated configuration. ( of measured data points ( values ( ever, if we limit the analyses of the ECHAM5 values to those points in time, when PI- a region between 45–75 + q these water quantities, the simulated near-surfaceand temperature shows spatially a much extended stronger correlation between Kourovka and its surroundings (mean than the related model values.wiso This values might be represent explained≈ the by the mean fact, of that the the ECHAM5- lowest atmosphericCARRO data. model This suggests level a (surface lackto of to boundary depletion either layer along mixing. air Despite mass the trajectories or systematic due o of isotope values in vapour at Kourovka are representative for isotopic changes in magnitude of variability, withvalues of a water very vapour measured large with correlation the PICARRO coe instrument are up to 20 % higher related with local temperatureintegrated or signal water of amount the changes, climaticcific but conditions site rather during (e.g. represent the a transport more of the vapour to a spe- by ber (total number of 1-h≈ intervals during this period: 4392) with a meanstrongly enrichment correlated. of From our analyses,of we temperature and find water the amount strongestlinked links to between local variations temperaturefindings ( that occurs between 30 August and 3 September. itation (not shown) are( highly correlated withber). the Compared simulated to the surface values, the Kourovka and the isotopic composition at all other grid cells, we estimate that variations September, while summer fluxes is also modeled withinKourovka, this we ECHAM5-wiso find simulation. a At strong the( grid linear point correlation closest between to hourlyas values of excess values range between excess data to identify di investigated in detail in future studies. sure variations at Kourovkawater vapour, are nor neither to strongly correlated to surface temperatures, 5 5 25 15 20 10 15 10 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ; ) v D W, (1) . At ◦ δ 1 ): 1994 2012 − ). The ( Wunch , 2011 200 ‰ and O retrieved 1996 , 2 − , N, 59.545 ◦ ). GFIT retrieves AERONET ). lags a local surface 2011 v , O, HDO, etc. UAFS is D 2 2012 Wunch et al. δ Kalnay et al. , ,H 2010 4 Notholt and Scherms , Petri )( 4 , CH 2 ). Data from the Ural Atmospheric 2009 Wunch et al. , , bottom panel). These simulated changes 6 O profile as follows ( , top panel). Around 1 May, the atmospheric 2616 2615 2 6 0.16% for XCH , surface temperature) between April and Septem- <  q O 2 apr H x , and O, temperature and pressure a-priori profiles are based  2 2 Hannigan et al. 10 ; log + 2011 values between end of April and early May for a detailed analysis , v 8.0 ) which provide accurate and precise retrieval of column-averaged D  ). In the model framework, the minimum in were derived from total column abundances of HDO and H δ 5 O v 2010 2 negative excursions with minimum and maximum values of apr H D , 0.20% for XCO 2011 , middle panel). During the following days, this northerly airflow caused v x δ 6 , < D variations at Kourovka are related to passages of dynamic low and high δ v 0.16 D 2010 δ = , and related quantities (vapour v Values of We conclude that PICARRO measurements and ECHAM5-wiso simulation results of The PICARRO observations and ECHAM5-wiso results consistently depict two pro- 100 ‰, respectively, for the first days of April 2012 and May 2012. Another negative D apr HDO Fourier Station (UAFS) in270 Kourovka m astronomical altitude, observatory 80 (57.048 kmson with to ECHAM5-wiso the output.vations West UAFS of from provides atmospheric Yekaterinburg high-resolution transmittanceTCCON city) ground-based in sites, were obser- the operating used spectral instruments for region are of Bruker compari- IFS-120HR 4000–11 000 cm and IFS-125HR ( standard TCCON software GFIT was used ( equipped with Bruker IFS-125Min July mobile 2012). spectrometer At (alignedments present, but TCCON by some does TCCON studies not members showprecision accept that ( mobile they versions are of able IFS-125 to instru- achieve the required accuracyfrom the and measurements from July to August 2012 in Kourovka. For data processing, the Wunch et al. et al. atmospheric concentrations of such gases as CO 4.1 Description of the data andGround-Based comparison Fourier-Transform technique Intrared (FTIR)remote spectrometers measurements are of the widely atmospheric used composition for ( x 4 Ground-Based FTIR the total number of moleculesof in profile the scaling vertical retrievaltrieved atmospheric with gas column, the using is assumption the well thaton algorithm known. the reanalysis H data shape provided of byNational the National Center profile Centers for of for Atmospheric Environmental theHDO Prediction Research a-priori re- and (NCEP/NCAR) profile the is ( calculated from H isotopic variations near Kourovka in future studies. atively enriched vapour to this region (Fig. for the location of Yekaterinburg (not shown). δ ber 2012 are inhourly good time agreement. scale Even are short-termsafely correctly use isotope the reproduced variations ECHAM5-wiso in occurring model this on results an nudged for an simulation. improved Thus, interpretation one of observed may circulation changed due toa a result, pronounced the low main pressure airlevels system flow (Fig. was north then of transported Kourovka.the from As central cooling Siberia at with depleted Kourovka.Kourovka Starting was again from dominating 7 the May, atmospheric a flow new pattern, high bringing warm pressure and system rel- south of Kourovka (Fig. pressure minimum by 1reaches day its and lowest temperatures precedessuch 4 a days drop later. in Thispressure surface sequence systems air of and temperature, events advectionvestigated suggests which of by that analysis remote of air the masses.event, isobaric the This flow Kourovaka at hypothesis area 850 is hPa. wastively further A receiving warm few in- days southwesterly and before air enriched this masses depletion vapour transporting (Fig. rela- in atmospheric transport to Kourovkament between with 20 back April trajectory and analyses 11 of May air are masses, in available from good the agree- nounced − excursion occurs on 12highly September. depleted Exemplarily, we have chosenof the the atmospheric May event conditions with leading to this fast and strong isotope shift in vapour at 5 5 15 10 25 20 25 15 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), O (2) 2 dur- erent is the is the ff 2010 , to more ). is the a- sim apr TCVW O profiles q ) was then O q 2 ): D 2 , apr H δ i 2011 x TCVW , TCVW D is the thickness 99 ‰ in July and from 1.0 to 1.09. D δ i 2012a P δ a-priori profiles for − , ∆ v Wunch et al. 0.91 and scatter with D TCVW δ = D 2 δ r O, respectively, 2 Risi et al. Shillings et al. 134 ‰ to ; − . O ). We do not find any systematic 2 2003 9 H , Q and range from . Microwindows containing saturated H from morning to noon by about 20 ‰. Ob- 7 5) ‰) for 23 August. Multiple measurements HDO 2617 2618 O and corresponding ± 2 Q TCVW of total column water vapour ( TCVW D v D  δ (180 δ D − apr δ i is the gravity acceleration. Column averaging vectors erent solar zenith angles of measurements are shown q ff ) g i Rodgers and Connor A ect of the applying column averaging kernels to the original − ff erences. The observations are systematically shifted to the (1 ff + sim i q ∗ i A is the a-priori HDO volume mixing ratio (vmr) profile, and  is the retrieved total column mass of HDO or H i -th component of the column averaging kernel vector, P i g O vmr profile. Examples for H Q apr HDO . TCCON a-priori and averaging kernel profiles are tabulated using a di ∆ 2 -th atmospheric layer, x 8 i 1 n = i X is the Here, FTIR measurements were carried out in Kourovka on three days in July 2012 and on Since the model provides hourly-averaged output data, data retrieved from FTIR = i specific humidity profilespecific simulated humidity by in the the same(converted model layer according from for to wet atmospheric the to a-priori layer profile dry-mole used fractional in the valuesof retrieval according the to as a function of pressurein for Fig. di vertical coordinate system than thelayers). model profiles To (71 ensure pressure levels numericalvertical versus consistency, 31 resolution hybrid all (31 profiles pressureintegration were was levels interpolated carried in out. to the The calculated the range from the same 1000–20 hPa) normalized ratio before of the vertical in spectroscopic line intensities ining the 10 linelist. July The is measuredthe also increase model, found of this in fast the isotopictropospheric model enrichment temperatures, results coincides exhibiting with for but Kourovka thethe with a summer temporal a months. pronounced evolution smaller Given of diurnal the amplitude lower cycle limiteda (10 number during ‰). rigid of In observations interpretation that and arepostponed available assessment to so the far, FTIR future. measurements from Kourovka has to be A on July 10 recordservations an and increase retrieved of modelan results absolute are standard correlated deviationtrend with of underlying 5.8 ‰ the (seehigher di Fig. values comparing to the model results. It can be explained by the uncertainties Q expected slope between retrievedThe and positive originally shift simulated oftween retrieval 1000 values and 200 is hPa, essentially thehigher isotopic a than ratio consequence in of the of TCCONther a ECHAM the investigation. priori simulations. fact profiles The that is small systematically be- change of the slopes23 deserves August, fur- 2012. Observationsshow of significantly lower values ( Before we enter thevations, comparison we between consider retrieved the ECHAM5-wiso e model results and results. obser- Inpositive Kourovka, values it by shifts about 5 the ‰ original in the model average, results and also for induces a slight change of the 4.2 Results of the comparison measurements taken withinmodel 1 h and FTIR were observationsare also true, we and averaged. we assume For simulate thatequation the the to measurement the the of comparison modeled model the result instrument HDO between ( by and applying the H following lines were excluded from finalspectral results parameters, to the achieve revised more water robust line retrieval. list As data was used base of ( where priori H each day of July 2012 are shown in Fig. 5 5 20 25 15 15 20 10 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . ) ). is ): (5) 10 are a 1 Grib- S is the 2000 h 2000 can be , 2000 k ( spectral , y a 0.78, see 1 S and − = 1 r a O and HDO con- Rodgers Rodgers 2 Chevallier et al. is the Jacobian of forward respectively, k j A h is modeled using the following was close to temperature at sur- a is the vector combined of vertical 1 S and ) were used to calculate the column − k is the measured spectrum, i 2 x h − y . Initial guess vertical profile of HDO was O and HDO retrieval is shown in Fig. 2 ), (3) shows daily means of PICARRO measure- k ) which were used for this study. The retrieval x 2620 2619 1 ). TIGR 2000 database ( 11 − − is identity matrix, O and HDO. 0 I 2 x 1982 )( ( k A . (4) k , (6) ε O and HDO (molm is the measurement error covariance matrix, and is the initial guess, C 2 S ! 0 was used to retrieve vertical temperature profile. At third, T k | ε − j was used to retrieve surface temperatures. Then the spec- x I A S 1 ( h -th iteration, 1 − 1 1 − − + − k h O and HDO vertical profiles joint retrieval. Only cloudless mea- i )  2 1 k h | − a y − S  O and HDO vertical profiles were retrieved simultaneously from the -diagonal blocks were filled with zeros. Because of absence of a suf- spectral interval of GOSAT TANSO-FTS band 4 measurements. The − ) was used for all retrievals. The 4-step retrieval scheme described are concentrations at altitudes 2 ff +

http://www.esrl.noaa.gov/psd/ 1 j y ( k − 0 q HDO k A O and HDO, exp S 2001 C 1 2 . The averaging kernel of joint H j Rozanski and Sonntag − ε , v O + q and 2 S i D 0 k H T k i q δ x S q 1 A a  =  1 = = = + Total column amounts of H In this study, o Here (for the last stage of retrieval algorithm), k a k ij above was applied to allthe summer proximity spectra to of 2012 Kourovka selected site. by Figure criteria of cloudless and correlation between direct PICARROGOSAT spectra measurements measured in in proximity Kourovka to and Kourovka. The retrievals best from correlation ( where parameters adjusted separately for H ratio of This figure demonstrates that the retrieval methodcentrations is at not surface. sensitive to A H modifiedanov version et of al. the FIRE-ARMS software package ( ments in Kourovka andjustable parameters GOSAT in measurements the within model 250 covariance km matrices range were selected around. to All reach ad- the best S S ficiently representative dataset ofagonal directly measured sub vertical block profiles offormula: of the HDO, a-priori each covariance di- matrix radiative transfer model, the a-priori covariance matrix.34 Vertical altitude profiles levels were from thegrouped represented into surface on sub to altitude blocks 65 as km. mesh The of a-priori covariance matrix simulated spectrum at profiles of H 1200–1227 cm retrieval of vertical profiles is based on the followingx iterative formula ( where C interval around 820 cm tral interval 680–765 cm a small surfaceinterval. temperature It correction was was done toity. made And compensate for finally, uncertainty H the in our 1200–1206 cm knowledge on surface emissiv- Reanalysis data provided by the NOAA/OAR/ESRLtheir PSD, web Boulder, site Colorado, USA at from taken from was used for temperaturewere generated covariance for matrix H calculation.surements of Model GOSAT were covariance selected. A matrices if spectral measurement brightness was temperature treated in as cloudless spectrumface in near NCEP 820 cm reanalysis data. At the first step of retrieval procedure, the short spectral Sensor TANSO-FTS onthermal board infrared GOSAT (band 4, satellite 650–2006 cm method provides is spectral based measurementsThe on in initial technique guess vertical of profiles optimal for temperature estimation and described humidity by were taken from NCEP 5 GOSAT 5 5 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ), , last 2604 2012 , distribu- v D δ web/index.html Frankenberg et al. ) with the aim to contribute 1 0.72). Obviously, a general circu- = 2 from the GOSAT thermal infrared band r E, see Fig. 0 v D 23 δ ◦ 2622 2621 . GOSAT retrievals were shifted in one day for ), a significant part of the daily isotopic variations http://aeronet.gsfc.nasa.gov/new N, 66 12 0 erences of associated fractionations (e.g convective 39 1992 ff ◦ , values including for rapid excursions related to concurrent in the water column. They are both very satisfying although v v D comparison between three observational approaches and D δ δ v 0.71) and temperature ( 2615 D = δ We (Zakharov, V. I. and Rokotyan, N. V.) thank Igor Ptashnik for fruitful 2 r Rozanski et al. and climatic parameters. There is indeed an excellent correlation between v D at the scale of the whole region of interest. Moreover, because of the time shift ) between PICARRO measurements at surface and columnar retrievals from δ ) observed in Kourovka water vapour is explained by local changes in the amount 11 12 v The ERA-Interim archive, ERA ReportForecast, Series, Shinfield European Park, Centre Reading, for Berkshire Medium RG29AX,2611 Range United Weather Kingdom, August 2009. access: 1 July 2012. This data model comparison fully justifies the use of ECHAM5-wiso to evaluate two To sum up, the As expected from a Rayleigh type model, and generally observed in middle and high D δ Berrisford, P., Dee, D., Fielding, K., Fuentes, M., Kallberg, P., Kobayashi, S., and Uppala, S.: and the method currently appliedas in well the short-wave as infrared the ( purposes. development of At an last the improved algorithmdata use in of to a a exploit larger second FTIR geographical IGCM data context. (LMDZiso) forAcknowledgements. should isotopic help to interpret these Aerosol Robotic Network (AERONET): a medium-high resolution IGCM,promising. undertaken Data acquisition for with the the Picarro firsttinuous instrument basis time will and be at a now a second performedlocated on instrument given near a will site, con- the be is Arctic deployed circle quite into (66 summer an 2013 Arctic at network Labytnangi nowof under oxygen-18 development. and Further associated deuterium work excess willof from include the the the algorithm PICARRO exploitation data, developed a to comparison infer column discussions regarding impact ofof error the of retrieval spectral and parameters his suggestion of to target use molecules the on revised precision water line list. References changes in atmospheric circulation. methods, respectively based on themote exploitation measurements of of FTIR and GOSAT data,being allowing limited re- to a small number of days. observed and predicted versus non-convective systems) istween a more appropriate tool to examine the link be- them against theIGCM results focusing derived on this from region. a dedicated simulationlatitude of regions ( the ECHAM5-wiso ( of water vapour ( lation model which accountsweather for situations the and for origin the of di the water vapour, for the complexity of GOSAT spectra was foundclose for to Kourovka daily is means shown in in July Fig. 2012. The GOSAT spot pattern changes under aproject warming and climate. the The resultsbe isotopic that considered approach we as is have acontained a in presented first water key and and vapour. To element discussed necessaryto this end in of acquire step we data this this to have (continuous combined article fully three surface should exploit independent measurements, methods the FTIR and isotopic GOSAT) information and evaluate Fig. considered. Ajustment of retrievalKourovka scheme site combined parameters with using ECHAM5-wiso directtions will over allow measurements the to in entire obtain region columnar of Western Sibera. 6 Conclusions The present study is partin of a permafrost project aiming regions to and investigate the pristine water and peatlands carbon of cycles Western Siberia and their projected the best correlation. 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(Eds.): IPCC Special Report on Emissions Scenarios, IPCC, 5 5 30 25 20 10 15 30 10 25 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , , 2616 2609 2603 O ratio ´ eon, F.- 2 2602 D profiles ´ ulveda, E., δ 2602 , 2005. , 2011. , 2010. 2609 2602 ´ enez, R., Keppel- 2616 ensen, J. P., Sper- , 2010b. O and ff , 2006. 2602 2 2617 a, O. E., Sep ı ´ , 2004. 10.1029/2010JD014311 , 2011. , 2012. , 2011. 10.1029/2005JD005790 2602 2602 10.5194/amt-3-1351-2010 doi:10.1029/2011JD015681 10.1029/2005jd006606 10.5194/amt-3-1785-2010 , 2006. O ratio: estimation approach and characteriza- mann, G., and Jouzel, J.: Latitudinal distribution th, D. W. T., Hurst, D. F., Jim 2 ff 10.1029/2004GL019433 , 2010a. ffi 2605 10.1098/rsta.2010.0240 2627 2628 , ´ alez, Y., Yoshimura, K., Garc 10.5194/amt-5-3007-2012 th, D. W. T., Smale, D., and Robinson, J.: Ground-based erent spectral infrared regions, Atmos. Meas. Tech., 3, 2602 erent timescales, Geophys. Res. Lett., 39, L08805, ffi ff ff 10.5194/acp-11-4273-2011 a, O., Hase, F., and Blumenstock, T.: Remote sensing of ı ´ mann, G.: Isotopic composition and origin of polar precipi- ff , 2012. O record of water vapor in Niamey (Niger) reveals insight- 18 δ 2602 ´ ottir, A. E., Vinther, B. M., and White, J.: Continuous monitoring of sum- 10.5194/acp-6-4705-2006 ´ ulveda, E., Garc 10.5194/amt-3-1599-2010 ¨ ornsd en, K., Sveinbjornsdottir, A. E., Svensson, A., and White, J.: Understanding the ff 2613 , 10.1029/2012GL051298 tation in present and glacial climate simulations, Tellus B, 53, 53–71, 2001. fith, D. W. T., Sherlock, V.,Philos. and T. Wennberg, R. P. O.: Soc. the A, total 369, carbon 2087–2112, column doi: observing network, 2011. Balslev-Clausen, D., Blunier, T., Dahl-Jensen, D.,sted, Ellehøj, A., M. Jouzel, D., Falourd, J., S., Popp, T., Gkinis, Sheldon, V., Grin- S., Simonsen, S. B., Sjolte, J., Ste Oi, M.: Aful 1-year atmospheric long doi: processes at di topes in theon ECHAM5 a general global circulation scale, J. model: Geophys. toward Res., high-resolution 16, D15109, isotope modeling 2617 of the deuterium to hydrogen ratiodata, in Geophys. the atmospheric Res. water Lett., vapor 31, retrieved from L12104, IMG/ADEOS doi: M., Clausen, H.Kageyama, M., B., Lerche, H., Dahl-Jensen, Minster,der, B., J., Picard, D., Ste G., Falourd, Punge, H.climatic S., J., signal Risi, Fettweis, C., in Salas, the X., D.,northwest Schwan- water Greenland, Gallee, stable J. isotope Geophys. H., Res.-Atmos., records 116, from Jouzel, D06108, the doi: J., NEEM shallow firn/ice cores in 2610 Gunson, M., Herman, R.,Rodgers, Kulawik, C., S. Sander, S., S., Shephard, Lampel,ter M., M., observations and Luo, of Worden, M., the H.: Osterman,tion, Tropospheric tropospheric emission J. G., HDO/H spectrome- Geophys. Rinsland, Res.-Atmos., C., 111, D16309, doi: Aleks, G., Kort,Park, E. S., A., Robinson, Macatangay, J.,Zondlo, R., Roehl, M. Machida, A.: C. Calibration M., T., of Matsueda, Sawa,file the Y., Total H., data, Carbon Sherlock, Atmos. Column Moore, V., Observing Sweeney, Meas. F., C., Network Morino, Tech., using Tanaka, 3, I., T., aircraft and pro- 1351–1362, doi: lich, P., Sveinbj mer surface water vapour isotopicRes., composition submitted, above 2013. the Greenland Ice Sheet, J. Geophys. Uchino, O., Abshire,man, J. K. P.,Browell, B., E. V., Campos, Bernath, T.,Elkins, Connor, B. P., J. J., Daube, Biraud, W., B. C., Gerbig, S. Deutscher, N. C., C., M., Gottlieb, Diao, M., Blavier, E., J.-F. Gri L., Boone, C., Bow- remotely-sensed from ground in1599–1613, di doi: remote sensing of troposphericmos. water Meas. vapour Tech., 5, isotopologues 3007–3027, within doi: the project MUSICA, At- 2604 water dimer absorption inChem. Phys., the 11, 750 4273–4287, nm doi: spectral region and a revised water line list, Atmos. water exchange, J. Geophys. Res.,2610 110, D21314, doi: profiles: introduction and validation of6, an 4705–4722, innovative retrieval doi: approach, Atmos. Chem. Phys., water vapour profiles in theCON), framework Atmos. of Meas. the Tech., 3, Total Carbon 1785–1795, Column doi: Observing Network (TC- Gomez-Pelaez, A., Gisi, M.,ner, Kohlhepp, E., R., Strong, Dohe, K., S.,une, Weaver, B., Blumenstock, D., Demoulin, P.,Jones, Palm, T., N., Wiegele, M., Gri A., Deutscher, N. Christ- M., Warneke, T., Notholt, J., Leje- climate in the second half of the 20th century, Russ. Meteorol. Hydrol., 35, 107–114, 2010. Werner, M., Heimann, M., and Ho Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, Connor, B. J., Grif- Tremoy, G., Vimeux, F., Mayaki, S., Souley, I., Cattani, O., Risi, C., Favreau,Werner, G., M., and Langebroek, P. M., Carlsen, T., Herold, M., and Lohmann, G.: Stable water iso- Steen-Larsen, H. C., Johnsen, S. J., Masson-Delmotte, V., Stenni, B., Risi, C., Sodemann, H., Zakharov, V. I., Imasu, R., Gribanov, K. G., Ho Steen-Larsen, H. C., Masson-Delmotte, V., Sjolte, J., Johnsen, S. J., Vinther, M. B., Br Worden, J., Bowman, K., Noone, D., Beer, R., Clough, S., Eldering, A., Fisher, B., Goldman, A., Wunch, D., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Stephens, B. B., Fischer, M. L., Schneider, M., Toon, G. C., Blavier, J.-F., Hase, F., and Leblanc, T.: H Shillings, A. J. L., Ball, S. M., Barber, M. J., Tennyson, J., and Jones, R. L.: An upper limit for Schneider, M., Hase, F., and Blumenstock, T.: Ground-based remote sensing of HDO/H Schneider, M., Sep Schneider, M., Barthlott, S., Hase, F., Gonz Shalaumova,Yu. V., Fomin, V. V., and Kapralov, D. S.: Spatiotemporal dynamics of the Urals 5 5 10 30 30 15 25 20 25 15 10 20

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

PICARRO

PICARRO

v

, permil , D

70˚ 65˚ 60˚ 55˚ δ

v

, permil , D

δ s a e m

℃

, , T

s a e m ℃ , , 90˚ 90˚ T local temperature -200 -250 30 20 10 0 -50 -100 -150 Elevation, m 20 10 0 -50 -100 -150 -200 -250 30 (c) 85˚ 85˚ Dec Dec hourly (gray dots) and running Nov Nov 80˚ 80˚ (b) Oct Oct Sep Sep 75˚ 75˚ local temperatures measured at Kourovka by Aug Aug 2630 2629 (d) measured by PICARRO at Kourovka, 70˚ 70˚ Jul Jul 0 200 400 600 800 1000 1200 1400 1600 1800 δD Jun Jun hourly (gray dots) and running (5 points, blue curve) means of D measured by PICARRO at Kourovka, 65˚ 65˚ δ (a) May May Apr c) d) b) a) Apr 60˚ 60˚ c) d) b) a) 0 8 4 0

Time series including a) hourly (gray dots) and running

30 20 10 20 16 12

v 0 8 4 0

, g/kg , q

m ri e nt I A R E

Map of the target region (Western Siberia). Kourovka ob- ℃

, ,

10 T 12 30 20 20 16

v

, g/kg , q

m ri e nt I A R E ℃ , , T

55˚ PICARRO 55˚ 70˚ 65˚ 60˚ 55˚ PICARRO

Time series including IMG/ADEOS data, Geophys. Res.doi:10.1029/2004GL019433, 2004. Lett., 31, No. 12, L12104, Map of the target region (Western Siberia). Kourovka observation site is marked with red

Fig. 2. (5 points, blue curve) meansCARRO at of Kourovka specific station, humidity b) measuredpoint, hourly red by curve) (gray PI- means dots) of andc) running local (5 temperature derived fromlocal ERA-interim temperatures reanalysis measured data, atlogical d) Kourovka station. by MetPak-II meteoro- Fig. 1. servation site is marked withwith red red circle. star and White star Yekaterinburg is stands marked for future site in Labytnangi. Fig. 2. specific humidity measured by PICARRO at Kourovka station, (5 point, red curve) means of MetPak-II meteorological station. derived from ERA-interim reanalysis data, Fig. 1. star and Yekaterinburg is marked with red circle. White star stands for future site in Labytnangi.

record of wa-

O 18 δ Jouzel, J.:drogen Latitudinal ratio distribution in of the the atmospheric deuterium water to vapor hy- retrieved from Notholt, Connor,Wennberg, B.J., P.O.: Griffith, Thework, D.W.T., Total Sherlock, Carbon Philos.doi:10.1098/rsta.2010.0240, Column V., 2011. Trans. Observing and Net- R. Soc. A, 369(1943), 20872112, ing network using aircraft13511362, profile doi:10.5194/amt-3-1351-2010, data, 2010. Atmos. Meas. Tech., 3, Eldering, A.,man, Fisher, R., B., Kulawik,G., Goldman, S. Rinsland, A., S.,and C., Gunson, Lampel, Worden, H.: Rodgers, M., M., Tropospherictions Luo, emission C., Her- of spectrometer the M., observa- Sander, troposphericand HDO/H2O Osterman, S., ratio: characterization, Estimation J. Shephard, approach doi:10.1029/2005jd006606, Geophys. 2006. M., Res.-Atmos., 111, D16309, G.: Stable watermodel: isotopes in Toward the highresolution ECHAM5scale, J. general isotope Geophys. circulation Res., modeling 16, D15109, on 2011. a global B., Risi, C.,Dahl-Jensen, Sodemann, D., H., Ellehj, Balslev-Clausen,sted, M.D., A., D., Jouzel, Falourd, J., Blunier, Popp, T., S., T.,J., Sheldon, S., Gkinis, Steffensen, Simonsen, S.B., J.P., V., Sjolte, Sperlich, Grin- B.M., White, P., J.: Sveinbjrnsdttir, A.E., Continuouster monitoring Vinther, of vapour isotopic summer surface composition wa- abovesubmitted. the Greenland Ice Sheet, C., Favreau, G., and Oi,ter M.: vapor in A Niamey 1-year (Niger)cesses long reveals at insightful different atmospheric timescales, pro- Geophys.doi:10.1029/2012GL051298, 2012. Res. Lett., 39, L08805, and origin of polarsimulations, precipitation Tellus Ser.B-Chem. in Phys. present Meteorol.,2001. and 53(1), glacial 53-71, climate Vinther, M.B., Bron,Falourd, F.-M., S., Fettweis, Clausen, X., H.B.,Lerche, Gallee, H., H., Dahl-Jensen, Minster, Jouzel, B., D., J., Picard,D., Kageyama, G., M., Schwander, Punge, J., H.J., Steffen, Risi,son, C., K., A., Salas, White, Sveinbjornsdottir, J.: A.E.,ter Understanding Svens- stable the isotope climatic records signal fromin in the northwest the NEEM Greenland, wa- shallow J. firn/ice Geophys.doi:10.1029/2010JD014311, cores Res-Atmos., 2011. 116, D06108, upper limit for water dimer absorptiongion in and a the revised 750 water nm line spectral4287. list, re- Available Atmos. at: Chem. http://discovery.ucl.ac.uk/1325236/, Phys., 2011. 11, 4273- dynamics of the Urals climate intury, Russ. the Meteorol. second Hydrol., half 35(2), of the 107-114, 20th 2010. cen- water vapor isotopologues within theMeas. project Tech. Discuss., MUSICA, Atmos. 5,2012, 5357-5418, 2012. doi:10.5194/amtd-5-5357- Zakharov, V.I., Imasu, R., Gribanov, K.G., Hoffmann, G., and Wunch, D., Toon, G.C., Blavier, J.-F. L., Washenfelder, R.A., Wunch, D., et al.: Calibration of the total carbon column observ- Worden, J., Bowman, K., Noone, D., Beer, R., Clough, S., Werner, M., Langebroek, P. M., Carlsen, T., Herold, M., Lohmann, Steen-Larsen, H.C., Johnsen, S.J., Masson-Delmotte, V., Stenni, Tremoy, G., Vimeux, F., Mayaki, S., Souley, I., Cattani, O., Risi, Werner, M., Heimann, M., and Hoffmann, G.: Isotopic composition Steen-Larsen, H.C., Masson-Delmotte, V., Sjolte, J., Johnsen, S.J., Shillings, A., Ball, S., Barber, M., Tennyson, J., and Jones, R.: An Shalaumova,Yu.V., Fomin, V.V., Kapralov, D.S.: Spatiotemporal K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals 11 1080 1075 1065 1070 1060 1045 1050 1055 1035 1040 1030 1025 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | v q and d) (c) Dec Nov Oct Sep Aug Jul Jun May Apr Mar Dec Nov Oct Sep Aug Jul Jun May Apr Mar (PICARRO) 3 v

D 0 δ 3 7 2 vapour amount a*x + b 0 3 7 a*x + b 5 3.92 2.5 =0.71 (c) 2 0 =0.72 r f(x) = a = 0. b = -0. 2 2 of the r f(x) = a = 4. b = -21 2

means; , ℃ v Linear Fit Linear Fit s i q ), g/kg v lys 0 (b) 1 ana e r 1.5 Daily Averaged T log(q 0 1

b) d) and daily (PICARRO) vs ERA-interim reanalysis -10 0.5 v (a) D

-220 -260 -0.1 -0.2 -0.3 -100 -140 -180

of the water vapour (yellow).

v

D

-0.25 δ -0.15

v δ D + log(1 ) δ ; δD (c) surface temperature (blue), 2632 2631 Dec Nov Oct Sep Aug Jul Jun May Apr Mar Dec Nov Oct Sep Aug Jul Jun May Apr Mar ), hourly v D of the water vapour (yellow). In panel (b) q δ 3

0 (d) 3 =0.67 2.5 2 ) vs ln( =0.46 2 r v r 0 D 2 2 δ

, ℃ + ), g/kg red v 0 asu 1 e Time series of ECHAM5-wiso simulation values between m T 1.5 log(q Hourly Averaged 0 1

lowest model grid box (grey),In (d) panel c) and(red d) lines) the are related show smoothed fortaken comparison, PICARRO from measurements the too. grid The box model enclosing Kourovka values station. are all Fig. 4. April 1st and Septemberline), 30th (b) 2012 surface of temperature (a) (blue), surface (c) pressure vapour (green amount . c) a) (d) -10 0.5

surface pressure (green line),

-0.1 -0.2 -0.3 -260 -100 -140 -180 -220

v

D

-0.15 -0.25 δ

v D + log(1 ) δ Aug Jul Jun May Apr Mar Dec Nov Oct Sep Aug Jul Jun May Apr Mar Dec Nov Oct Sep (a) Time series of ECHAM5-wiso simulation values between 1 April and 30 September Scatter plots for ln(1 3

0 3 7 2 a*x + b 0 3 7 a*x + b 5 3.92 2.5 =0.71 2 0 =0.72 r f(x) = a = 0. b = -0. 2 2 r f(x) = a = 4. b = -21 2012 of Fig. 4. of the lowest modelthe grid related box smoothed PICARRO (grey), model measurements values (red are lines) all are taken show from for the comparison, grid too. box The enclosing Kourovka station. Fig. 3. vs local temperature measuredtemperatures at Kourovka 2

, ℃ Linear Fit Linear Fit s , hourly (a) and i ) ), g/kg v lys 0 v 1 ana e q r 1.5 Daily Averaged T log(q ln( 0 1

d) b) vs ) -10 0.5 v

-260 -100 -140 -180 -220 -0.1 -0.2 -0.3

v

D

-0.15 -0.25 δ

v D + log(1 ) δ δD Apr Mar Dec Nov Oct Sep Aug Jul Jun May Nov Oct Sep Aug Jul Jun May Apr Mar Dec 3

ln(1 + 0 3 (PICARRO) vs local temperature measured =0.67 2.5 2 =0.46 (PICARRO) vs ERA-interim reanalysis tem- 2 r r

0 v v

2

2

, ℃ δD

δD

), g/kg red

v

0

asu

1

e

m

T 5

1. log(q Hourly Averaged 0 1

Scatter plots for c) a) -10 0.5

-0.3 -0.2 -0.1 -260 -140 -180 -220 -100

v

D

-0.25 δ -0.15

v D + log(1 ) δ at Kourovka (c); daily (b) means; peratures (d). Fig. 3. 12 K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals E as ◦ compo- δD W - 90 ◦ N, 15 ◦ N - 75 ◦ ‰

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | E as Horizontal wind flow at 850hPa (vectors) and E ◦ of total column water vapour and isobaric flow @850 hPa ◦ D compo- δ W–90 Fig. 6. sition of the totalb) water May column 1, c) (colored May pattern) 10 for for a) the region April 45 23, simulated by ECHAM5-wiso. Themarked location by of red Kourovka cross. station is δD ◦ W - 90 ◦ N, 15 ◦ N, 15 ◦ N–75 ◦ N - 75 ◦ D composition of the total water column δ 10 May for the region 45 ‰ 2634 2633 (c) 1 May , Horizontal wind flow at 850hPa (vectors) and (b) of total column water vapour and isobaric flow @850 hPa D δ , but for the period 22 April to 13 May. 4 Fig. 6. sition of the totalb) water May column 1, c) (colored Maysimulated pattern) 10 by for for ECHAM5-wiso. a) the The region Aprilmarked 45 location 23, by of red Kourovka cross. station is 23 April, The same as Fig. 4, but for the period April 22 to May 13. (a) Fig. 5. K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals 13 Horizontal wind flow at 850 hPa (vectors) and The same as Fig. Fig. 6. (colored pattern) for Fig. 5. as simulated by ECHAM5-wiso. The location of Kourovka station is marked by red cross. The same as Fig. 4, but for the period April 22 to May 13. Fig. 5. K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals 13 -50 1 a*x + b 6 .10 vertical profile re- -100 =0.91 2 r f(x) = a = 0. b = -74 -50 Linear Fit HDO R I and T 1 F a*x + b 6 .10 D -150 O δ vertical profile re- 2 -100 =0.91 H 2 r f(x) = a = 0. b = -74 Linear Fit HDO 90 R . I and T F D -150 O -200 = 0 δ 2 r H 90 . -200 = 0 r Averaging kernels for FTIR observations versus ECHAM5-wiso simulations in -250

-50

-100 -150 -200 -250

V N O C

D δ

M HA C Discussion Paper | Discussion Paper | DiscussionE Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Averaging kernels for FTIR observations versus ECHAM5-wiso simulations in -250 Fig. 10. trievals. Fig. 9. July-August, 2012,

-50

-200 -250 -100 -150

V N O C

D δ

M HA C E

Fig. 10. trievals. Fig. 9. July-August, 2012, Solar Zenith Angle Zenith Solar Number of the day in July in day the of Number

0 0 5 0 5

Solar Zenith Angle Zenith 0 Solar 0 0 0 0 0 Number of the day in July in 1 day the of 5 Number 0 3 2 2 1 5 4 3 8 7 6

5 0 5 0 0 0 0 0 0 0 0 3 2 2 1 1 5 0 3 7 6 5 4 3 8

5 0 3 .

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a-priori profiles derived from

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p 2 18000 m p

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H

Column Averaging O 5 O and 2 1 . 2 and corresponding 6000 0

0 H 0 0 0 0 0 0

0 0 0 0 0 0 H 0

0 0 0 0 O 0

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1

1

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0 0 0 0 0 0 H 0

0 0 0 0 0

0 0 0 0 8 2 4 6 0 6 8 2 4 0

Fig. 8. solar zenith angles NCEP/NCAR reanalysis data using formula (1) Fig. 7. 14 K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals

1

1

).

) (hPa Pressure ) a P h ( e r u s s e r P 1 O and corresponding O and HDO column averaging kernels for di 2 2 H H solar zenith angles Fig. 8. NCEP/NCAR reanalysis data using formula (1) Fig. 7. 14 K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals Fig. 8. Fig. 7. using Eq. ( Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.90. = r -50 -50 1 1 a*x + b 6 .10 a*x + b 6 .10 vertical profile re- vertical profile re- -100 -100 =0.91 =0.91 2 2 r f(x) = a = 0. b = -74 r f(x) = a = 0. b = -74 Linear Fit Linear Fit Linear HDO HDO R R I I and and T T F F D D -150 O -150 O δ δ 2 2 H H 2638 2637 90 90 . . -200 -200 O and HDO vertical profile retrievals. = 0 = 0 2 r r Averaging kernels for Averaging kernels for FTIR observations versus ECHAM5-wiso simulations in FTIR observations versus ECHAM5-wiso simulations in -250 -250 -50

-50

-150 -200 -250 -100

-200 -250 -100 -150

V N O C

V N O C D

δ D δ

M HA C E M HA C E Averaging kernels for H FTIR observations versus ECHAM5-wiso simulations in July–August, 2012, Fig. 10. Fig. 9. July-August, 2012, trievals. trievals. Fig. 10. Fig. 9. July-August, 2012,

Fig. 10. Fig. 9.

Solar Zenith Angle Zenith Solar Number of the day in July in day the of Number Solar Zenith Angle Zenith Solar Number of the day in July in day the of Number 0 0 5 0 5 0 0 5 0 5 0 0 0 0 0 0 0 0 0 0 0 0 1 5 0 3 2 2 1 5 4 3 8 7 6 1 5 0 3 2 2 1 5 4 3 8 7 6

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) (hPa Pressure

) a P h ( e r u s s e r P

) (hPa Pressure ) a P h ( e r u s s e r P Fig. 8. solar zenith angles Fig. 7. NCEP/NCAR reanalysis data using formula (1) 14 K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals Fig. 8. solar zenith angles Fig. 7. NCEP/NCAR reanalysis data using formula (1) 14 K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0 800 600 400 200 1600 1400 1200 1000 0 800 600 400 200 Elevation, m 1600 1400 1200 1000 59˚ 58˚ 57˚ 56˚ 55˚ 54˚ Elevation, m 65˚ 65˚ 59˚ 58˚ 57˚ 56˚ 55˚ 54˚ 65˚ 65˚ 64˚ 64˚ 64˚ 64˚ dHDO dHDO 78 63˚ 63˚ . 78 63˚ 63˚ . = 0 = 0 62˚ 62˚ 2640 2639 r 62˚ 62˚ r 61˚ 61˚ 61˚ 61˚ 60˚ 60˚ 60˚ 60˚ 59˚ 59˚ 59˚ 59˚ −200 −180 −160 −140 −120 −100 GOSAT observation spots (colored circles) in the vicinity Gosat retrievals vs PICARRO measurements in July 2012, −200 −180 −160 −140 −120 −100 GOSAT observation spots (colored circles) in the vicinity Gosat retrievals vs PICARRO measurements in July 2012, 58˚ 58˚ 59˚ 58˚ 57˚ 56˚ 55˚ 54˚ 58˚ 58˚ 0.78 Gosat retrievals vs PICARRO measurements in July 2012, daily means, 1 day time 54˚ 59˚ 58˚ 57˚ 56˚ 55˚ GOSAT observation spots (colored circles) in the vicinity to Kourovka (brown star) and = Fig. 12. to Kourovka (brown star) and Yekaterinburg (brown circle). Fig. 11. daily means, 1 day time shift, K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals 15 r Fig. 12. to Kourovka (brown star) and Yekaterinburg (brown circle). Fig. 11. daily means, 1 day time shift, K.Gribanov et al.: ECHAM5-wiso water vapour isotopologues simulation and its comparison with retrievals 15 Fig. 12. Yekaterinburg (brown circle). Fig. 11. shift,