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  • 1
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Watcham, Emma P; Bentley, Michael J; Hodgson, Dominic A; Roberts, Stephen J; Fretwell, Peter; Lloyd, Jerry M; Larter, Robert D; Whitehouse, Pippa L; Leng, Melanie J; Monien, Patrick; Moreton, Steven Grahame (2011): A new Holocene relative sea level curve for the South Shetland Islands, Antarctica. Quaternary Science Reviews, 30(21-22), 3152-3170, https://doi.org/10.1016/j.quascirev.2011.07.021
    Publication Date: 2023-11-04
    Description: Precise relative sea level (RSL) data are important for inferring regional ice sheet histories, as well as helping to validate numerical models of ice sheet evolution and glacial isostatic adjustment. Here we develop a new RSL curve for Fildes Peninsula, South Shetland Islands (SSIs), a sub-Antarctic archipelago peripheral to the northern Antarctic Peninsula ice sheet, by integrating sedimentary evidence from isolation basins with geomorphological evidence from raised beaches. This combined approach yields not only a Holocene RSL curve, but also the spatial pattern of how RSL change varied across the archipelago. The curve shows a mid-Holocene RSL highstand on Fildes Peninsula at 15.5 m above mean sea level between 8000 and 7000 cal a BP. Subsequently RSL gradually fell as a consequence of isostatic uplift in response to regional deglaciation. We propose that isostatic uplift occurred at a non-steady rate, with a temporary pause in ice retreat ca. 7200 cal a BP, leading to a short-lived RSL rise of ~1 m and forming a second peak to the mid-Holocene highstand. Two independent approaches were taken to constrain the long-term tectonic uplift rate of the SSIs at 0.22-0.48 m/ka, placing the tectonic contribution to the reconstructed RSL highstand between 1.4 and 2.9 m. Finally, we make comparisons to predictions from three global sea level models.
    Keywords: Age, 14C calibrated, CALIB (Stuiver & Reimer, 1993); Age, 14C milieu/reservoir corrected (Milliken et al., 2009); Age, AMS 14C conventional; Age, dated; Age, dated material; Age, dated standard deviation; Ardley_lake; Belen_lake; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Comment of event; Core; CORE; Event label; Fildes Peninsula, King George Island; Gaoshan_lake; Laboratory; Laguna_Tern, Lake_Albatross; Lake_Shanhaicuan; Latitude of event; Long_lake; Longitude of event; Ozero_Dlinnoye; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; Probability; Sample ID; SPP1158; Yanou_lake; Yue_Ya_Hu, Laguna_Ripamonti; δ13C
    Type: Dataset
    Format: text/tab-separated-values, 928 data points
    Location Call Number Limitation Availability
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  • 2
    Publication Date: 2024-04-20
    Description: We provide a global 0.5-degree grid of vertical land motion (in mm/a) of the LM17.3 glacial isostatic adjustment (GIA) model. The radially varying earth model part is profile VM5a (Peltier et al. 2015). The ice load is different to any other GIA model and combines regional ice loads without taking care of balancing the global sea-level equivalent of all ice sheets and glaciers with that expected from paleo-sea-level indicators. The regional models are: * GLAC-1D for North America (Tarasov et al. 2012), * HUY3 for Greenland (Lecavalier et al. 2014), * GLAC #71340 for Fennoscandia/Barents Sea (Tarasov et al., 2014), * ANU-ICE for Iceland, High Mountain Areas, Siberian Mountains and Tibet (Lambeck et al. 2014), * IJ04_Patagonia for Patagonia (updated from Ivins & James 2004), * ICE-6G_C for New Zealand (Argus et al. 2014, Peltier et al. 2015), * GLAC-1D for Antarctica (Briggs et al. 2014). Additional models (W12, Whitehouse et al. 2012, and IJ05_R2, Ivins et al. 2013, for Antarctica; ANU-ICE, Lambeck et al. 2017, and NAIce, Gowan et al. 2016, for North America) were tested in the development of the model but not used in the end. Little ice age is not included nor any ice mass change during the last 100 years. The eustatic sea-level equivalent at last glacial maximum amounts to 113.8 m for all ice sheets and glaciers together. Because we use an ice model that has not been tuned to fit global constraints, it may highlight areas which cannot match commonly used GIA observations. However, we note that the earth model used in our calculations is different to the earth model used in the development of some regional ice models, e.g. HUY3, ANU-ICE, IJ04_Patagonia (see respective references), thus some differences can be related to this. The LM17.3 model was introduced in Jäggi et al. (2019), and its DDK5-filtered geoid and water heights can be found in the EGSIEM plotter (http://plot.egsiem.eu/index.php?p=timeseries). The GIA model uses material compressibility and includes time-dependent coastlines and rotational feedback. The vertical land motion can be used/tested in sea-level investigations and projections. Work towards a model that incorporates 3D earth structure, and an updated ice model, is ongoing.
    Keywords: EGSIEM; European Gravity Service for Improved Emergency Management; glacial isostatic adjustment; sea level
    Type: Dataset
    Format: application/zip, 1.9 MBytes
    Location Call Number Limitation Availability
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  • 3
    Publication Date: 2024-04-29
    Description: We created a 3D GNSS surface velocity field to estimate tectonic plate motion and test the effect of a set of 1D and 3D Glacial Isostatic Adjustment (GIA) models on tectonic plate motion estimates. The main motivation for creating a bespoke 3D velocity field is to include a larger number of GNSS sites in the GIA-affected areas of investigation, namely North America, Europe, and Antarctica. We created the GNSS surface velocity field using the daily network solutions submitted to the International GNSS Service (IGS) “repro2” data processing campaign, and other similarly processed GNSS solutions. We combined multiple epoch solutions into unique global epoch solutions of high stability. The GNSS solutions we used were processed with the latest available methods and models at the time: all the global and regional solutions adhere to IGS repro2 standards. Every network solution gives standard deviations of site position coordinates and the correlations between the network sites. We deconstrained and combined the global networks and aligned them to the most recent ITRF2014 reference frame on a daily level. Additionally, several regional network solutions were deconstrained and aligned to the unique global solutions. The process was performed using the Tanya reference frame combination software (Davies & Blewitt, 1997; doi:10.1029/2000JB900004) which we updated to facilitate changes in network combination method and ITRF realisation. This resulted in 57% reduction of the WRMS of the alignment post-fit residuals compared to the alignment to the previous ITRF2008 reference frame for an overlapping period. We estimated linear velocities from the time series of GNSS coordinates using the MIDAS trend estimator (Blewitt et al., 2016; doi:10.1002/2015JB012552). The sites selected through multiple steps of quality control constitute a final GNSS surface velocity field which we denote NCL20. This velocity field has horizontal uncertainties mostly within 0.5 mm/yr, and vertical uncertainties mostly within 1 mm/yr, which make it suitable for testing GIA models and estimating plate motion models.
    Keywords: 1LSU_GNSS; 1NSU_GNSS; 1ULM_GNSS; AB04_GNSS; AB08_GNSS; AB12_GNSS; AC58_GNSS; ACOR_GNSS; ACP1_GNSS; ACP6_GNSS; ACSO_GNSS; ACUM_GNSS; ADE1_GNSS; ADIS_GNSS; ADRI_GNSS; AJAC_GNSS; AL30_GNSS; AL40_GNSS; AL50_GNSS; AL60_GNSS; AL70_GNSS; AL90_GNSS; ALCI_GNSS; ALES_GNSS; ALGO_GNSS; ALIC_GNSS; ALRT_GNSS; AMC2_GNSS; ANDO_GNSS; ANG1_GNSS; ANP5_GNSS; ANTO_GNSS; AOML_GNSS; AOPR_GNSS; ARBT_GNSS; ARCM_GNSS; ARFY_GNSS; ARGI_GNSS; ARHP_GNSS; ARHR_GNSS; ARJM_GNSS; ARP3_GNSS; ARPG_GNSS; ARTU_GNSS; ASC1_GNSS; ASCG_GNSS; ASHV_GNSS; ASUB_GNSS; AUCK_GNSS; AUDR_GNSS; AUS5_GNSS; AUTN_GNSS; AVCA_GNSS; AXPV_GNSS; BACA_GNSS; BACK_GNSS; BACO_GNSS; BADH_GNSS; BAHR_GNSS; BAIA_GNSS; BAIE_GNSS; BAKE_GNSS; BAN2_GNSS; BARH_GNSS; BARN_GNSS; BAUS_GNSS; BAYR_GNSS; BBYS_GNSS; BCLN_GNSS; BELE_GNSS; BELF_GNSS; BELL_GNSS; BENN_GNSS; BET1_GNSS; BIAZ_GNSS; BIL5_GNSS; BISK_GNSS; BJCO_GNSS; BJU0_GNSS; BLA1_GNSS; BNDY_GNSS; BNFY_GNSS; BOD3_GNSS; BOGI_GNSS; BOMJ_GNSS; BOR1_GNSS; BORJ_GNSS; BORK_GNSS; BORR_GNSS; BPDL_GNSS; BRAZ_GNSS; BRFT_GNSS; BRGS_GNSS; BRIP_GNSS; BRMF_GNSS; BRMU_GNSS; BRST_GNSS; BRTW_GNSS; BRU5_GNSS; BRUS_GNSS; BSCN_GNSS; BSMK_GNSS; BUDP_GNSS; BUE1_GNSS; BUMS_GNSS; BURI_GNSS; BVHS_GNSS; BYDG_GNSS; CACE_GNSS; CAEN_GNSS; CAGL_GNSS; CAGS_GNSS; CALU_GNSS; CANT_GNSS; CAPF_GNSS; CARM_GNSS; CAS1_GNSS; CASB_GNSS; CASC_GNSS; CASP_GNSS; CAYU_GNSS; CBMD_GNSS; CBSB_GNSS; CCV5_GNSS; CEBR_GNSS; CEDU_GNSS; CEFE_GNSS; CFRM_GNSS; CGGN_GNSS; CHA1_GNSS; CHAN_GNSS; CHAT_GNSS; CHB5_GNSS; CHIZ_GNSS; CHL1_GNSS; CHPI_GNSS; CHR1_GNSS; CHT1_GNSS; CHTI_GNSS; CHUR_GNSS; CJTR_GNSS; CKIS_GNSS; CLIB_GNSS; CLK5_GNSS; CLRK_GNSS; CN13_GNSS; CN14_GNSS; CN15_GNSS; CN16_GNSS; CN23_GNSS; CN24_GNSS; CN28_GNSS; CN29_GNSS; CN33_GNSS; CN34_GNSS; CN35_GNSS; CN41_GNSS; CN46_GNSS; CN53_GNSS; CNC0_GNSS; CNIV_GNSS; CNMR_GNSS; COLA_GNSS; CONO_GNSS; CORB_GNSS; CORC_GNSS; COTE_GNSS; COVG_GNSS; COVX_GNSS; CPAR_GNSS; CRAK_GNSS; CRAO_GNSS; CRDI_GNSS; CRST_GNSS; CTAB_GNSS; CTBR_GNSS; CTGU_GNSS; CTPU_GNSS; CTWN_GNSS; CUIB_GNSS; CUSV_GNSS; CVMS_GNSS; DAKR_GNSS; DANE_GNSS; DARE_GNSS; DAVM_GNSS; DEAR_GNSS; DEFI_GNSS; DEGE_GNSS; DELM_GNSS; DENE_GNSS; DENT_GNSS; DEVI_GNSS; DGLS_GNSS; DNRC_GNSS; DOBS_GNSS; DOMS_GNSS; DOUR_GNSS; DREM_GNSS; DRV5_GNSS; DSL1_GNSS; DUBO_GNSS; DUM1_GNSS; DUPT_GNSS; EBRE_GNSS; ECSD_GNSS; EDOC_GNSS; EGLT_GNSS; EIJS_GNSS; ELEN_GNSS; ENG1_GNSS; ENIS_GNSS; ENTZ_GNSS; EPRT_GNSS; ESCO1_GNSS; ESCU_GNSS; EUR2_GNSS; EUSK_GNSS; EVPA_GNSS; EXU0_GNSS; FALL_GNSS; FFMJ_GNSS; FIE0_GNSS; FLIN_GNSS; FLIU_GNSS; FLM5_GNSS; FLRS_GNSS; FONP_GNSS; FOYL_GNSS; FREE_GNSS; FREI_GNSS; FRKN_GNSS; FTP4_GNSS; FUNC_GNSS; GAAT_GNSS; GABR_GNSS; GACC_GNSS; GACL_GNSS; GACR_GNSS; GAIA_GNSS; GAIT_GNSS; GAL1_GNSS; GANP_GNSS; GARF_GNSS; GAST_GNSS; GCEA_GNSS; GDMA_GNSS; Glacial Isostatic Adjustment (GIA) model; GLPM_GNSS; GLPS_GNSS; GLSV_GNSS; GMSD_GNSS; GNSS; GNSS Receiver; GNVL_GNSS; GODE_GNSS; GOGA_GNSS; GOPM_GNSS; GOUG_GNSS; GRAS_GNSS; GRE0_GNSS; GRIS_GNSS; GRN0_GNSS; GRTN_GNSS; GTK0_GNSS; GUAM_GNSS; GUAX_GNSS; GUIP_GNSS; GUUG_GNSS; GWWL_GNSS; HAAG_GNSS; HAC6_GNSS; HAG6_GNSS; HALY_GNSS; HAMM_GNSS; HAMP_GNSS; HARK_GNSS; HASM_GNSS; HBCH_GNSS; HBRK_GNSS; HCES_GNSS; HDIL_GNSS; HELG_GNSS; HERS_GNSS; HILB_GNSS; HILO_GNSS; HIPT_GNSS; HJOR_GNSS; HKLO_GNSS; HLFX_GNSS; HNLC_GNSS; HNPT_GNSS; HNUS_GNSS; HOB2_GNSS; HOBU_GNSS; HOE2_GNSS; HOLM_GNSS; HONS_GNSS; horizontal GIA; HOS0_GNSS; HOUM_GNSS; HOUS_GNSS; HOWE_GNSS; HOWN_GNSS; HRMM_GNSS; HRST_GNSS; HUGO_GNSS; HYDE_GNSS; IBIZ_GNSS; ICT1_GNSS; IGEO_GNSS; IGGY_GNSS; IISC_GNSS; ILDX_GNSS; ILHA_GNSS; ILSA_GNSS; ILUC_GNSS; IMBT_GNSS; IMPZ_GNSS; INAB_GNSS; INES1_GNSS; INGG_GNSS; INVM_GNSS; INWN_GNSS; IQAL_GNSS; IQUI_GNSS; IRBE_GNSS; IRKM_GNSS; ISCO_GNSS; ISPA_GNSS; IZAN_GNSS; JAB2_GNSS; JCT1_GNSS; JFNG_GNSS; JFWS_GNSS; JOEN_GNSS; JONM_GNSS; JOZE_GNSS; JXVL_GNSS; KAR0_GNSS; KARL_GNSS; KARR_GNSS; KAT1_GNSS; KAUS_GNSS; KELY_GNSS; KERM_GNSS; KEVO_GNSS; KEW5_GNSS; KHAJ_GNSS; KHAR_GNSS; KIRI_GNSS; KIRM_GNSS; KIRU_GNSS; KIVE_GNSS; KJUN_GNSS; KLOP_GNSS; KMOR_GNSS; KNGS_GNSS; KNS5_GNSS; KNTN_GNSS; KOK1_GNSS; KOKM_GNSS; KOSG_GNSS; KOUC_GNSS; KOUG_GNSS; KOUR_GNSS; KRA0_GNSS; KRSS_GNSS; KRTV_GNSS; KST5_GNSS; KSTU_GNSS; KSU1_GNSS; KULU_GNSS; KUN0_GNSS; KUNZ_GNSS; KURE_GNSS; KUUJ_GNSS; KUUS_GNSS; KUWT_GNSS; KVTX_GNSS; KWJ1_GNSS; KWST_GNSS; KYBO_GNSS; KYMH_GNSS; KYTB_GNSS; KYTC_GNSS; KYTD_GNSS; KYTE_GNSS; KYTG_GNSS; KYTH_GNSS; KYTK_GNSS; KYTL_GNSS; KYW1_GNSS; KZN2_GNSS; LAMA_GNSS; LAMT_GNSS; LANS_GNSS; LATITUDE; LCDT_GNSS; LCHS_GNSS; LCKM_GNSS; LCSB_GNSS; LEBA_GNSS; LEES_GNSS; LEIJ_GNSS; LEK0_GNSS; LEON_GNSS; LESV_GNSS; LHCL_GNSS; LHUE_GNSS; LIL2_GNSS; LKHU_GNSS; LLIV_GNSS; LMNO_GNSS; LODZ_GNSS; LOFS_GNSS; LONGITUDE; LOVM_GNSS; LPAL_GNSS; LPGS_GNSS; LPIL_GNSS; LPLY_GNSS; LROC_GNSS; LSBN_GNSS; LSUA_GNSS; LWN0_GNSS; LWX1_GNSS; LYCO_GNSS; LYNS_GNSS; LYRS_GNSS; MACC_GNSS; MADM_GNSS; MADO_GNSS; MAG0_GNSS; MAIR_GNSS; MAJU_GNSS; MALD_GNSS; MALL_GNSS; MAN2_GNSS; MAPA_GNSS; MAR6_GNSS; MARJ_GNSS; MARN_GNSS; MARS_GNSS; MAS1_GNSS; MAUI_GNSS; MAW1_GNSS; MAYZ_GNSS; MCAR_GNSS; MCD5_GNSS; MCIL_GNSS; MCM4_GNSS; MCN1_GNSS; MCNE_GNSS; MCTY_GNSS; MDOR_GNSS; MDR6_GNSS; MDVJ_GNSS; MET6_GNSS; MET7_GNSS; METG_GNSS; MFLD_GNSS; MIAR_GNSS; MICW_GNSS; MIDS_GNSS; MIGD_GNSS; MIHO_GNSS; MIHT_GNSS; MIIR_GNSS; MIKL_GNSS; MIL1_GNSS; MIMN_GNSS; MIMQ_GNSS; MIN0_GNSS; MINI_GNSS; MIPR_GNSS; MIST_GNSS; MKEA_GNSS; MLF1_GNSS; MLVL_GNSS; MNBD_GNSS; MNBE_GNSS; MNCA_GNSS; MNDN_GNSS; MNGR_GNSS; MNJC_GNSS; MNP1_GNSS; MNPL_GNSS; MNRM_GNSS; MNRT_GNSS; MNRV_GNSS; MNSC_GNSS; MNTF_GNSS; MNVI_GNSS; MOAL_GNSS; MOB1_GNSS; MOBS_GNSS; MOED_GNSS; MOEL_GNSS; MOGF_GNSS; MOPN_GNSS; MORP_GNSS; MOVB_GNSS; MPLA_GNSS; MPLE_GNSS; MRO1_GNSS; MRRN_GNSS; MSB5_GNSS; MSHT_GNSS; MSKU_GNSS; MSNA_GNSS; MSPK_GNSS; MSSC_GNSS; MSYZ_GNSS; MTMS_GNSS; MTNT_GNSS; MTY2_GNSS; NAIN_GNSS; NAMA_GNSS; NAPL_GNSS; NAS0_GNSS; NAUR_GNSS; NAUS_GNSS; NBR6_GNSS; NCDU_GNSS; NCGO_GNSS; NCJA_GNSS; NCPO_GNSS; NCSW_GNSS; NCWH_GNSS; NCWI_GNSS; NDMB_GNSS; NEDR_GNSS; NEGI_GNSS; NEIA_GNSS; NESC_GNSS; NEWL_GNSS; NHUN_GNSS; NIST_GNSS; NIUM_GNSS; NJCM_GNSS; NJHC_GNSS; NJI2_GNSS; NJOC_GNSS; NJTW_GNSS; NKLG_GNSS; NLIB_GNSS; NMKM_GNSS; NNOR_GNSS; NOR0_GNSS; NOR1_GNSS; NOR3_GNSS; NOUM_GNSS; NPLD_GNSS; NPRI_GNSS; NRCM_GNSS; NRIL_GNSS; NRL1_GNSS; NRMD_GNSS; NTUS_GNSS; NYBH_GNSS; NYBT_GNSS; NYCL_GNSS; NYCP_GNSS; NYDV_GNSS; NYFD_GNSS; NYFS_GNSS; NYFV_GNSS; NYHC_GNSS; NYHM_GNSS; NYHS_GNSS; NYIR_GNSS; NYLV_GNSS; NYMD_GNSS; NYML_GNSS; NYNS_GNSS; NYON_GNSS; NYPD_GNSS; NYPF_GNSS; NYRB_GNSS; NYST_GNSS; NYWL_GNSS; NYWT_GNSS; OAKH_GNSS; ODS5_GNSS; OHAS_GNSS; OHFA_GNSS; OHHU_GNSS; OHLI_GNSS; OHMO_GNSS; OHMR_GNSS; OHPR_GNSS; OKAN_GNSS; OKAR_GNSS; OKBF_GNSS; OKCB_GNSS; OKCL_GNSS; OKDT_GNSS; OKGM_GNSS; OKHV_GNSS; OKMA_GNSS; OKOM_GNSS; OLKI_GNSS; OMH5_GNSS; ONSM_GNSS; OPMT_GNSS; ORMD_GNSS; OSKM_GNSS; OSLS_GNSS; OSPA_GNSS; OST0_GNSS; OUAG_GNSS; OULU_GNSS; OVE0_GNSS; P032_GNSS; P033_GNSS; P037_GNSS; P038_GNSS; P039_GNSS; P040_GNSS; P042_GNSS; P043_GNSS; P044_GNSS; P049_GNSS; P050_GNSS; P051_GNSS; P052_GNSS; P053_GNSS; P054_GNSS; P055_GNSS; P070_GNSS; P728_GNSS; P775_GNSS; P776_GNSS; P777_GNSS; P778_GNSS; P779_GNSS; P780_GNSS; P802_GNSS; P803_GNSS; P807_GNSS; P817_GNSS; PAAP_GNSS; PAFU_GNSS; PALK_GNSS; PAMS_GNSS; PAPC_GNSS; PARK_GNSS; PARY_GNSS; PASA_GNSS; PASS_GNSS; PATN_GNSS; PATT_GNSS; PBCH_GNSS; PBRM_GNSS; PECE_GNSS; PICL_GNSS; PIGT_GNSS; PIRT_GNSS; PKTN_GNSS; plate motion model; PLTC_GNSS; PNBM_GNSS; PNGM_GNSS; PNR6_GNSS; POAL_GNSS; POHN_GNSS; POLV_GNSS; POR2_GNSS; POTS_GNSS; POUS_GNSS; POVE_GNSS; PRCO_GNSS; PRDS_GNSS; PREI_GNSS; PREM_GNSS; PRPT_GNSS; PSU1_GNSS; PTBB_GNSS; PTGV_GNSS; PTIR_GNSS; PUB5_GNSS; PUIN_GNSS; PULK_GNSS; PUO1_GNSS; PUYV_GNSS; PWEL_GNSS; QAQ1_GNSS; QIKI_GNSS; RAMG_GNSS; RAMO_GNSS; RANT_GNSS; RAT0_GNSS; RBAY_GNSS; RCMV_GNSS; RECF_GNSS; REDU_GNSS; REDZ_GNSS; Reference frame; RESO_GNSS; REUN_GNSS; RG13_GNSS; RG15_GNSS; RG16_GNSS; RG17_GNSS; RG18_GNSS; RG19_GNSS; RG23_GNSS; RG24_GNSS; RIC1_GNSS; RIGA_GNSS; RIO1_GNSS; RIOJ_GNSS; RIS5_GNSS; RLAP_GNSS; RMBO_GNSS; ROB4_GNSS; ROBN_GNSS; ROMU_GNSS; ROSS_GNSS; ROTH_GNSS; RWSN_GNSS;
    Type: Dataset
    Format: text/tab-separated-values, 6755 data points
    Location Call Number Limitation Availability
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  • 4
    facet.materialart.
    Unknown
    GSA (Geological Society of America)
    In:  Geology, 35 (8). pp. 747-750.
    Publication Date: 2021-04-30
    Description: The geomorphology of the western Siberian Arctic coast represents a significant departure from the global trend of Holocene delta formation by major rivers. The Ob' and Yenisei Rivers in western Siberia drain into the Arctic Ocean via estuaries ∼900 and ∼500 km long, respectively. Eastern Siberian rivers such as the Lena, Indigirka, and Kolyma terminate at significant marine deltas. We show that this spatial variation in coastal geomorphology can be explained by the glacial isostatic adjustment of the region. The development and collapse of a peripheral bulge in western Siberia, associated with the glaciation and subsequent deglaciation of the Eurasian ice sheets, led to a distinct spatial variation in sea-level change that continues to this day. In particular, since the marked decrease in global-scale ice melting ca. 7 ka, our model predicts a sea-level rise at the mouth of the Ob' River of ∼14 m, compared to a rise of ∼6 m at the mouth of the Lena River, which ceased at 3 ka. We propose that the enhanced sea-level rise in the western Siberian Arctic associated with peripheral bulge subsidence has prevented the establishment of marine deltas at the mouths of the Ob' and Yenisei Rivers. We conclude that regional variations in relative sea-level change driven by glacial isostatic adjustment should be considered when interpreting large-scale coastal morphology and deltaic stratigraphy, which is normally assumed to correlate with eustatic fluctuations.
    Type: Article , PeerReviewed
    Format: text
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  • 5
    Publication Date: 2022-01-31
    Description: Highlights • Exposure ages that constrain ice sheet thickness collated from an online database. • Thinning rates are reconstructed from 23 sites across Antarctica. • Palaeo-thinning rates are comparable to modern observations. • Wide-spread thinning during the Holocene, but after Meltwater Pulse 1A. Abstract Constraining Antarctic ice sheet evolution provides a way to validate numerical ice sheet models that aid predictions of sea-level rise. In this paper we collate cosmogenic exposure ages from exposed nunataks in Antarctica that have been used, or have the potential to be used, to constrain rates of thinning of the Antarctic Ice Sheets since the Last Glacial Maximum. We undertake quality control of the data and adopt a Bayesian approach to outlier detection. Past thinning rates are modelled by Monte Carlo linear regression analysis. We present thinning rates from 23 sites across Antarctica. The resulting data set is the first Antarctic-wide collation of past ice sheet thinning rates and provides an empirical starting point for future model-data comparisons. Palaeo-thinning rates are spatially variable with high rates appearing to correlate to areas of contemporary rapid changes. On centennial timescales past thinning rates are comparable to modern day observations implying that modern day thinning has the potential to persist for centuries in numerous parts of Antarctica. The onset of abrupt thinning from all sites post-dates Meltwater Pulse 1A suggesting that its source region(s) are distal to areas where exposure age constraints on ice surface geometry exist.
    Type: Article , PeerReviewed
    Format: text
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  • 6
    Publication Date: 2020-08-03
    Description: The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 7
    Publication Date: 2020-08-03
    Description: The Greenland Ice Sheet has been a major contributor to global sea-level rise in recent decades1,2, and it is expected to continue to be so3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the magnitude and trajectory of the ice sheet’s mass imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. The ice sheet was close to a state of balance in the 1990s, but annual losses have risen since then, peaking at 345 ± 66 billion tonnes per year in 2011. In all, Greenland lost 3,902 ± 342 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.8 ± 0.9 millimetres. Using three regional climate models, we show that the reduced surface mass balance has driven 1,964 ± 565 billion tonnes (50.3 per cent) of the ice loss owing to increased meltwater runoff. The remaining 1,938 ± 541 billion tonnes (49.7 per cent) of ice loss was due to increased glacier dynamical imbalance, which rose from 46 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. The total rate of ice loss slowed to 222 ± 30 billion tonnes per year between 2013 and 2017, on average, as atmospheric circulation favoured cooler conditions15 and ocean temperatures fell at the terminus of Jakobshavn Isbræ16. Cumulative ice losses from Greenland as a whole have been close to the rates predicted by the Intergovernmental Panel on Climate Change for their high-end climate warming scenario17, which forecast an additional 70 to 130 millimetres of global sea-level rise by 2100 compared with their central estimate.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 8
    Publication Date: 2019-01-25
    Description: Analysis | Published: 13 June 2018 Mass balance of the Antarctic Ice Sheet from 1992 to 2017 The IMBIE team Naturevolume 558, pages219–222 (2018) | Download Citation Abstract The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.
    Description: Published
    Description: 219-222
    Description: 5A. Paleoclima e ricerche polari
    Description: JCR Journal
    Keywords: Antarctica ; Ice sheet mass balance ; 02.02. Glaciers ; 04.03. Geodesy
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 9
    Publication Date: 2017-06-15
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 10
    Publication Date: 2022-05-31
    Description: Antarctic geothermal heat flow (GHF) affects the ice sheet temperature, determining how it slides and internally deforms, as well as the rheological behaviour of the lithosphere. However, GHF remains poorly constrained, with few borehole-derived estimates, and there are large discrepancies in currently available glaciological and geophysical estimates. This SCAR White Paper details current methods, discusses their challenges and limitations, and recommends key future directions in GHF research. We highlight the timely need for a more multidisciplinary and internationally-coordinated approach to tackle this complex problem.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Other , notRev
    Format: application/pdf
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