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  • 41. Yom-Tov, Elad
    et al.
    Yom-Tov, Yoram
    Yom-Tov, Shlomith
    Andersen, Mogens
    Rosenfeld, Daniel
    Devasthale, Abhay
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Geffen, Eli
    The complex effects of geography, ambient temperature, and North Atlantic Oscillation on the body size of Arctic hares in Greenland2017Ingår i: Biological Journal of the Linnean Society, ISSN 0024-4066, E-ISSN 1095-8312, Vol. 120, nr 4, s. 909-918Artikel i tidskrift (Refereegranskat)
  • 42. Bennartz, Ralf
    et al.
    Hoschen, Heidrun
    Picard, Bruno
    Schroder, Marc
    Stengel, Martin
    Sus, Oliver
    Bojkov, Bojan
    Casadio, Stefano
    Diedrich, Hannes
    Eliasson, Salomon
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Fell, Frank
    Fischer, Jurgen
    Hollmann, Rainer
    Preusker, Rene
    Willén, Ulrika
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    An intercalibrated dataset of total column water vapour and wet tropospheric correction based on MWR on board ERS-1, ERS-2, and Envisat2017Ingår i: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, nr 4, s. 1387-1402Artikel i tidskrift (Refereegranskat)
  • 43.
    Johansson, Erik
    et al.
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Devasthale, Abhay
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Tjernstrom, Michael
    Ekman, Annica M. L.
    L'Ecuyer, Tristan
    Response of the lower troposphere to moisture intrusions into the Arctic2017Ingår i: Geophysical Research Letters, ISSN 0094-8276, E-ISSN 1944-8007, Vol. 44, nr 5, s. 2527-2536Artikel i tidskrift (Refereegranskat)
  • 44. Wu, Dong L.
    et al.
    Baum, Bryan A.
    Choi, Yong-Sang
    Foster, Michael J.
    Karlsson, Karl-Göran
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Heidinger, Andrew
    Poulslsen, Caroline
    Pavolonis, Michael
    Riedi, Jerome
    Roebeling, Robert
    Sherwood, Steven
    Thoss, Anke
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Watts, Philip
    TOWARD GLOBAL HARMONIZATION OF DERIVED CLOUD PRODUCTS2017Ingår i: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 98, nr 2, s. ES49-ES52Artikel i tidskrift (Refereegranskat)
  • 45.
    Sedlar, Joseph
    et al.
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Tjernstrom, Michael
    Clouds, warm air, and a climate cooling signal over the summer Arctic2017Ingår i: Geophysical Research Letters, ISSN 0094-8276, E-ISSN 1944-8007, Vol. 44, nr 2, s. 1095-1103Artikel i tidskrift (Refereegranskat)
  • 46. Huuskonen, Asko
    et al.
    Haase, Gunther
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Michelson, Daniel
    Leijnse, Hidde
    Holleman, Iwan
    Probert, Matt
    Gill, Rashpal
    Frech, Michael
    Kurri, Mikko
    Hohti, Harri
    Solar Monitoring as a Tool for improving the Homogeneity of Radar Networks2016Konferensbidrag (Övrigt vetenskapligt)
  • 47. Leung, W. -YH.
    et al.
    Savre, J.
    Bender, F. A. -M
    Komppula, M.
    Portin, H.
    Romakkaniemi, S.
    Sedlar, Joseph
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Noone, K.
    Ekman, A. M. L.
    Sensitivity of a continental night-time stratocumulus-topped boundary layer to varying environmental conditions2016Ingår i: QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, ISSN 0035-9009, Vol. 142, nr 700, s. 2911-2924Artikel i tidskrift (Refereegranskat)
  • 48.
    Devasthale, Abhay
    et al.
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Sedlar, Joseph
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Kahn, Brian H.
    Tjernstrom, Michael
    Fetzer, Eric J.
    Tian, Baijun
    Teixeira, Joao
    Pagano, Thomas S.
    A DECADE OF SPACEBORNE OBSERVATIONS OF THE ARCTIC ATMOSPHERE Novel. Insights from NASA's AIRS Instrument2016Ingår i: BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, ISSN 0003-0007, Vol. 97, nr 11, s. 2163-2176Artikel i tidskrift (Refereegranskat)
  • 49.
    Berg, Peter
    et al.
    SMHI, Forskningsavdelningen, Hydrologi.
    Norin, Lars
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Olsson, Jonas
    SMHI, Forskningsavdelningen, Hydrologi.
    Creation of a high resolution precipitation data set by merging gridded gauge data and radar observations for Sweden2016Ingår i: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 541, s. 6-13Artikel i tidskrift (Refereegranskat)
  • 50. Sporre, Moa K.
    et al.
    O'Connor, Ewan J.
    Håkansson, Nina
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Thoss, Anke
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Swietlicki, Erik
    Petaja, Tuukka
    Comparison of MODIS and VIIRS cloud properties with ARM ground-based observations over Finland2016Ingår i: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 9, nr 7, s. 3193-3203Artikel i tidskrift (Refereegranskat)
  • 51.
    Landelius, Tomas
    et al.
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Dahlgren, Per
    SMHI, Forskningsavdelningen, Meteorologi.
    Gollvik, Stefan
    SMHI, Forskningsavdelningen, Meteorologi.
    Jansson, A.
    Olsson, Esbjörn
    SMHI, Forskningsavdelningen, Meteorologi.
    A high-resolution regional reanalysis for Europe. Part 2: 2D analysis of surface temperature, precipitation and wind2016Ingår i: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 142, nr 698, s. 2132-2142Artikel i tidskrift (Refereegranskat)
  • 52.
    Dahlgren, Per
    et al.
    SMHI, Forskningsavdelningen, Meteorologi.
    Landelius, Tomas
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Kållberg, Per
    SMHI, Forskningsavdelningen, Meteorologi.
    Gollvik, Stefan
    SMHI, Forskningsavdelningen, Meteorologi.
    A high-resolution regional reanalysis for Europe. Part 1: Three-dimensional reanalysis with the regional HIgh-Resolution Limited-Area Model (HIRLAM)2016Ingår i: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 142, nr 698, s. 2119-2131Artikel i tidskrift (Refereegranskat)
  • 53. Pareeth, Sajid
    et al.
    Delucchi, Luca
    Metz, Markus
    Rocchini, Duccio
    Devasthale, Abhay
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Raspaud, Martin
    SMHI, Samhälle och säkerhet.
    Adrian, Rita
    Salmaso, Nico
    Neteler, Markus
    New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data2016Ingår i: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, nr 3Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Analyzing temporal series of satellite data for regional scale studies demand high accuracy in calibration and precise geo-rectification at higher spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites provide daily observations for the last 30 years at a nominal resolution of 1.1 km at nadir. However, complexities due to on-board malfunctions and orbital drifts with the earlier missions hinder the usage of these images at their original resolution. In this study, we developed a new method using multiple open source tools which can read level 1B radiances, apply solar and thermal calibration to the channels, remove bow-tie effects on wider zenith angles, correct for clock drifts on earlier images and perform precise geo-rectification by automated generation and filtering of ground control points using a feature matching technique. The entire workflow is reproducible and extendable to any other geographical location. We developed a time series of brightness temperature maps from AVHRR local area coverage images covering the sub alpine lakes of Northern Italy at 1 km resolution (1986-2014; 28 years). For the validation of derived brightness temperatures, we extracted Lake Surface Water Temperature (LSWT) for Lake Garda in Northern Italy and performed inter-platform (NOAA-x vs. NOAA-y) and cross-platform (NOAA-x vs. MODIS/ATSR/AATSR) comparisons. The MAE calculated over available same day observations between the pairs-NOAA-12/14, NOAA-17/18 and NOAA-18/19 are 1.18 K, 0.67 K, 0.35 K, respectively. Similarly, for cross-platform pairs, the MAE varied between 0.5 to 1.5 K. The validation of LSWT from various NOAA instruments with in-situ data shows high accuracy with mean R-2 and RMSE of 0.97 and 0.91 K respectively.

  • 54. Soci, Cornel
    et al.
    Bazile, Eric
    Besson, Francois
    Landelius, Tomas
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    High-resolution precipitation re-analysis system for climatological purposes2016Ingår i: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 68, artikel-id 29879Artikel i tidskrift (Refereegranskat)
  • 55. Pareeth, Sajid
    et al.
    Delucchi, Luca
    Metz, Markus
    Rocchini, Duccio
    Devasthale, Abhay
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Raspaud, Martin
    SMHI, Samhälle och säkerhet.
    Adrian, Rita
    Salmaso, Nico
    Neteler, Markus
    New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data2016Ingår i: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, nr 3, s. NIL_481-NIL_508Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Analyzing temporal series of satellite data for regional scale studies demand high accuracy in calibration and precise geo-rectification at higher spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites provide daily observations for the last 30 years at a nominal resolution of 1.1 km at nadir. However, complexities due to on-board malfunctions and orbital drifts with the earlier missions hinder the usage of these images at their original resolution. In this study, we developed a new method using multiple open source tools which can read level 1B radiances, apply solar and thermal calibration to the channels, remove bow-tie effects on wider zenith angles, correct for clock drifts on earlier images and perform precise geo-rectification by automated generation and filtering of ground control points using a feature matching technique. The entire workflow is reproducible and extendable to any other geographical location. We developed a time series of brightness temperature maps from AVHRR local area coverage images covering the sub alpine lakes of Northern Italy at 1 km resolution (1986-2014; 28 years). For the validation of derived brightness temperatures, we extracted Lake Surface Water Temperature (LSWT) for Lake Garda in Northern Italy and performed inter-platform (NOAA-x vs. NOAA-y) and cross-platform (NOAA-x vs. MODIS/ATSR/AATSR) comparisons. The MAE calculated over available same day observations between the pairs-NOAA-12/14, NOAA-17/18 and NOAA-18/19 are 1.18 K, 0.67 K, 0.35 K, respectively. Similarly, for cross-platform pairs, the MAE varied between 0.5 to 1.5 K. The validation of LSWT from various NOAA instruments with in-situ data shows high accuracy with mean R-2 and RMSE of 0.97 and 0.91 K respectively.

  • 56.
    Norin, Lars
    et al.
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Devasthale, Abhay
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    L'Ecuyer, T. S.
    Wood, N. B.
    Smalley, M.
    Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden2015Ingår i: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 8, nr 12, s. 5009-5021Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Accurate snowfall estimates are important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain gauges to estimate precipitation in this context. In particular, the Cloud Profiling Radar (CPR) on board CloudSat is proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and its ability to provide near-global vertical structure. CloudSat snowfall estimates play a particularly important role in the high-latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. In this paper, snowfall estimates from two observing systems-Swerad, the Swedish national weather radar network, and CloudSat - are compared. Swerad offers a well-calibrated data set of precipitation rates with high spatial and temporal resolution, at very high latitudes. The measurements are anchored to rain gauges and provide valuable insights into the usefulness of CloudSat CPR's snowfall estimates in the polar regions. In total, 7 : 2 x 10(5) matchups of CloudSat and Swerad observations from 2008 through 2010 were intercompared, covering all but the summer months (June to September). The intercomparison shows encouraging agreement between the two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46-82 km), where the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station, as Swerad's sensitivity decreases as a function of distance. Swerad also tends to overshoot low-level precipitating systems further away from the station, leading to an underestimation of snowfall rate and occasionally to missing precipitation altogether. Several statistical metrics-including the probability of detection, false alarm rate, hit rate, and Pierce's skill score - are calculated. The sensitivity of these metrics to the snowfall rate, as well as to the distance from the nearest radar station, are summarised. This highlights the strengths and the limitations of both observing systems at the lower and upper ends of the snowfall distributions as well as the range of uncertainties that can be expected from these systems in high-latitude regions.

  • 57.
    Karlsson, Karl-Göran
    et al.
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Johansson, Erik
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Devasthale, Abhay
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: Probabilistic formulations for NOAA AVHRR data2015Ingår i: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 158, s. 126-139Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Two alternative methods for probabilistic cloud masking of images from the Advanced Very High Resolution Radiometer (AVHRR) sensor have been examined. Both methods are based on Bayesian theory and were trained using data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. Results were evaluated by comparing to independent CALIPSO-CALIOP observations and to a one-year ground-based cloud dataset composed from five different remote sensing systems over the observation site in Cabauw in the Netherlands. In addition, results were compared to two different cloud masks; one derived from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor and one from the Climate Monitoring Satellite Application Facility Clouds (CMSAF), Albedo and Radiation dataset from AVHRR data (CLARA-A1). It was demonstrated that the probabilistic methods compare well with the referenced satellite datasets and for daytime conditions they provide even better performance than the reference methods. Among the two probabilistic approaches, it was found that the formulation based on a Naive Bayesian formulation (denoted PPS-Prob Naive) performed clearly superior to the formulation based on a linear summation of conditional cloud probabilities (denoted PPS-Prob SPARC) for daytime conditions. For the study based on the observations over the Cabauw site, the overall daytime Kuipers Skill Score for PPS-Prob Naive was 0.84, for PPS-Prob SPARC 0.79, for CLARA-A1 0.74 and for SEVIRI 0.66. Corresponding results for night-time conditions were less favourable for the probabilistic formulations (Kuipers Skill Score 0.74 for PPS-Prob Naive, 0.68 for PPS-Prob SPARC, 0.80 for CLARA-A1 and 0.79 for SEVIRI) but still relatively close to the reference dataset The Cabauw distribution of cloudiness occurrences in different octa categories was reproduced very closely by all methods, including the probabilistic formulations. Results based on Cabauw observations were also largely in good agreement with results deduced from comparisons with the CALIPSO-CALIOP cloud mask. The PPS-Prob Naive approach will be implemented in an upcoming version of the Polar Platform System (PPS) cloud software issued by the EUMETSAT Nowcasting Satellite Application Facility (NWC SAF). It will also be used in the second release of the CMSAF CLARA cloud climate data record based on historic AVHRR GAC data (to be denoted CIARA-A2). (C) 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license

  • 58. Stengel, M.
    et al.
    Mieruch, S.
    Jerg, M.
    Karlsson, Karl-Göran
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Scheirer, Ronald
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Maddux, B.
    Meirink, J. F.
    Poulsen, C.
    Siddans, R.
    Walther, A.
    Hollmann, R.
    The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements2015Ingår i: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 162, s. 363-379Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Cloud property retrievals from 3 decades of the Advanced Very High Resolution Radiometer (AVHRR) measurements provide a unique opportunity for a long-term analysis of clouds. In this study, the accuracy of AVHRR-derived cloud properties cloud mask, cloud-top height, cloud phase and cloud liquid water path is assessed using three state-of-the-art retrieval schemes. In addition, the same retrieval schemes are applied to the AVHRR heritage channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) to create AVHRR-like retrievals with higher spatial resolution and based on presumably more accurate spectral calibration. The cloud property retrievals were collocated and inter-compared with observations from CloudSat, CALIPSO and AMSR-E The resulting comparison exhibited good agreement in general. The schemes provide correct cloud detection in 82 to 90% of all cloudy cases. With correct identification of clear-sky in 61 to 85% of all clear areas, the schemes are slightly biased towards cloudy conditions. The evaluation of the cloud phase classification shows correct identification of liquid clouds in 61 to 97% and a correct identification of ice clouds in 68 to 95%, demonstrating a large variability among the schemes. Cloud-top height (CTH) retrievals were of relatively similar quality with standard deviations ranging from 2.1 km to 2.7 km. Significant negative biases in these retrievals are found in particular for cirrus clouds. The biases decrease if optical depth thresholds are applied to determine the reference CTH measure. Cloud liquid water path (LWP) is also retrieved well with relative low standard deviations (20 to 28 g/m(2)), negative bias and high correlations. Cloud ice water path (IWP) retrievals of AVHRR and MODIS exhibit a relative high uncertainty with standard deviations between 800 and 1400 g/m2, which in relative terms exceed 100% when normalized with the mean IWP. However, the global histogram distributions of IWP were similar to the reference dataset MODIS retrievals are for most comparisons of slightly better quality than AVHRR-based retrievals. Additionally, the choice of different near-infrared channels, 3.7 mu M as opposed to 1.6 mu m, can have a significant impact on the retrieval quality, most pronounced for IWP, with better accuracy for the 1.6 mu m channel setup. This study presents a novel assessment of the quality of cloud properties derived from AVHRR channels, which quantifies the accuracy of the considered retrievals based on common approaches and validation data. Furthermore, it assesses the capabilities of AVHRR-like spectral information for retrieving cloud properties in the light of generating climate data records of cloud properties from three decades of AVHRR measurements. (C) 2013 Elsevier Inc. All rights reserved.

  • 59. Sun, Bomin
    et al.
    Free, Melissa
    Yoo, Hye Lim
    Foster, Michael J.
    Heidinger, Andrew
    Karlsson, Karl-Göran
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations2015Ingår i: Journal of Climate, ISSN 0894-8755, E-ISSN 1520-0442, Vol. 28, nr 11, s. 4373-4389Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Variability and trends in total cloud cover for 1982-2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres-Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as "ground truth" in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of -0.40% decade(-1), and satellite products show larger downward trends ranging from -0.55% to -5.00% decade(-1) for 1984-2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.

  • 60.
    Thomas, Manu Anna
    et al.
    SMHI, Forskningsavdelningen, Luftmiljö.
    Kahnert, Michael
    SMHI, Forskningsavdelningen, Luftmiljö.
    Andersson, Camilla
    SMHI, Forskningsavdelningen, Luftmiljö.
    Kokkola, H.
    Hansson, Ulf
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Jones, Colin
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Langner, Joakim
    SMHI, Forskningsavdelningen, Luftmiljö.
    Devasthale, Abhay
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Integration of prognostic aerosol-cloud interactions in a chemistry transport model coupled offline to a regional climate model2015Ingår i: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 8, nr 6, s. 1885-1898Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To reduce uncertainties and hence to obtain a better estimate of aerosol (direct and indirect) radiative forcing, next generation climate models aim for a tighter coupling between chemistry transport models and regional climate models and a better representation of aerosol-cloud interactions. In this study, this coupling is done by first forcing the Rossby Center regional climate model (RCA4) with ERA-Interim lateral boundaries and sea surface temperature (SST) using the standard cloud droplet number concentration (CDNC) formulation (hereafter, referred to as the 'stand-alone RCA4 version' or 'CTRL' simulation). In the stand-alone RCA4 version, CDNCs are constants distinguishing only between land and ocean surface. The meteorology from this simulation is then used to drive the chemistry transport model, Multiple-scale Atmospheric Transport and Chemistry (MATCH), which is coupled online with the aerosol dynamics model, Sectional Aerosol module for Large Scale Applications (SALSA). CDNC fields obtained from MATCH-SALSA are then fed back into a new RCA4 simulation. In this new simulation (referred to as 'MOD' simulation), all parameters remain the same as in the first run except for the CDNCs provided by MATCH-SALSA. Simulations are carried out with this model setup for the period 2005-2012 over Europe, and the differences in cloud microphysical properties and radiative fluxes as a result of local CDNC changes and possible model responses are analysed. Our study shows substantial improvements in cloud microphysical properties with the input of the MATCH-SALSA derived 3-D CDNCs compared to the stand-alone RCA4 version. This model setup improves the spatial, seasonal and vertical distribution of CDNCs with a higher concentration observed over central Europe during boreal summer (JJA) and over eastern Europe and Russia during winter (DJF). Realistic cloud droplet radii (CD radii) values have been simulated with the maxima reaching 13 mu m, whereas in the stand-alone version the values reached only 5 mu m. A substantial improvement in the distribution of the cloud liquid-water paths (CLWP) was observed when compared to the satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the boreal summer months. The median and standard deviation values from the 'MOD' simulation are closer to observations than those obtained using the stand-alone RCA4 version. These changes resulted in a significant decrease in the total annual mean net fluxes at the top of the atmosphere (TOA) by -5 W m(-2) over the domain selected in the study. The TOA net fluxes from the 'MOD' simulation show a better agreement with the retrievals from the Clouds and the Earth's Radiant Energy System (CERES) instrument. The aerosol indirect effects are estimated in the 'MOD' simulation in comparison to the pre-industrial aerosol emissions (1900). Our simulations estimated the domain averaged annual mean total radiative forcing of -0.64 W m(-2) with a larger contribution from the first indirect aerosol effect (-0.57 W m(-2)) than from the second indirect aerosol effect (-0.14 W m(-2)).

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