Modelling the Vadret da Tschierva, Switzerland: calibration with the historical length record and future response to climate change

Abstract The Vadret da Tschierva (Vd Tschierva) is a 4 km long glacier in the Swiss Alps spanning an altitude range of 2400–4049 m a.s.l. Length observations since 1855 show steady retreat interrupted by a period of advance from 1965 until 1985. The total retreat is ~2200 m (period 1855–2018). We have studied the Vd Tschierva with a flowline model, combined with ‘buckets’ that represent steep hanging glaciers and ice-free rock faces delivering mass to the main stream. The model is calibrated by a control method, in which an ELA history is objectively determined by finding the best match between observed and simulated glacier length. There is a modest correlation between the reconstructed ELA and an ELA record based on meteorological observations at Segl-Maria (only 8 km away from the glacier). It is difficult to reproduce the observed length record when the glacier model is driven by climate model output (Coupled Model Intercomparison Project 5). We have calculated the future evolution of the Vd Tschierva for different rates of ELA rise. For a constant rise of 4 ${\rm m\;}{\rm a}^{ \hbox{-} 1}$ , we predict that the glacier length will change from the current 3.2 km to ~1.7 km in the year 2100.

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A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections

10.5194/tc-2019-277 ◽

2019 ◽

Cited By ~ 3

Author(s):

Nicolas C. Jourdain ◽

Xylar Asay-Davis ◽

Tore Hattermann ◽

Fiammetta Straneo ◽

Helene Seroussi ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Ice Sheet ◽

Coupled Model ◽

Model Intercomparison ◽

Ice Shelf ◽

Simple Formulation ◽

Future Evolution ◽

Calibration Methods ◽

Intercomparison Project

Abstract. Climate model projections have previously been used to compute ice-shelf basal melt rates in ice-sheet models, but the strategies employed – e.g. ocean input, parameterization, calibration technique, and corrections – have varied widely and are often ad-hoc. Here, a methodology is proposed for the calculation of circum-Antarctic basal melt rates for floating ice, based on climate models, that is suitable for ISMIP6, the Ice Sheet Model Intercomparison Project for CMIP6 (6th Coupled Model Intercomparison Project). The past and future evolution of ocean temperature and salinity is derived from a climate model by estimating anomalies with respect to the modern day, which are added to an present-day climatology constructed from existing observational datasets. Temperature and salinity are extrapolated to any position potentially occupied by a simulated ice shelf. A simple formulation is proposed for a basal-melt parameterization in ISMIP6, constrained by the observed temperature climatology, with a quadratic dependency on either the non-local or local thermal forcing. Two calibration methods are proposed: 1) based on the mean Antarctic melt rate (MeanAnt) and 2) based on melt rates near Pine Island's deep grounding line (PIGL). Future Antarctic mean melt rates are an order of magnitude greater in PIGL than in MeanAnt. The PIGL calibration, and the local parameterization, result in more realistic melt rates near grounding lines. PIGL is also more consistent with observations of interannual melt rate variability underneath Pine Island and Dotson ice shelves. This work stresses the need for more physics and less calibration in the parameterizations, and for more observations of hydrographic properties and melt rates at interannual and decadal time scales.

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A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections

The Cryosphere ◽

10.5194/tc-14-3111-2020 ◽

2020 ◽

Vol 14 (9) ◽

pp. 3111-3134 ◽

Cited By ~ 1

Author(s):

Nicolas C. Jourdain ◽

Xylar Asay-Davis ◽

Tore Hattermann ◽

Fiammetta Straneo ◽

Hélène Seroussi ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Ice Sheet ◽

Coupled Model ◽

Model Intercomparison ◽

Ice Shelf ◽

Simple Formulation ◽

Future Evolution ◽

Calibration Methods ◽

Intercomparison Project

Abstract. Climate model projections have previously been used to compute ice shelf basal melt rates in ice sheet models, but the strategies employed – e.g., ocean input, parameterization, calibration technique, and corrections – have varied widely and are often ad hoc. Here, a methodology is proposed for the calculation of circum-Antarctic basal melt rates for floating ice, based on climate models, that is suitable for ISMIP6, the Ice Sheet Model Intercomparison Project for CMIP6 (6th Coupled Model Intercomparison Project). The past and future evolution of ocean temperature and salinity is derived from a climate model by estimating anomalies with respect to the modern day, which are added to a present-day climatology constructed from existing observational datasets. Temperature and salinity are extrapolated to any position potentially occupied by a simulated ice shelf. A simple formulation is proposed for a basal melt parameterization in ISMIP6, constrained by the observed temperature climatology, with a quadratic dependency on either the nonlocal or local thermal forcing. Two calibration methods are proposed: (1) based on the mean Antarctic melt rate (MeanAnt) and (2) based on melt rates near Pine Island's deep grounding line (PIGL). Future Antarctic mean melt rates are an order of magnitude greater in PIGL than in MeanAnt. The PIGL calibration and the local parameterization result in more realistic melt rates near grounding lines. PIGL is also more consistent with observations of interannual melt rate variability underneath Pine Island and Dotson ice shelves. This work stresses the need for more physics and less calibration in the parameterizations and for more observations of hydrographic properties and melt rates at interannual and decadal timescales.

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Past and future changes of streamflow in Poyang Lake Basin, Southeastern China

Hydrology and Earth System Sciences ◽

10.5194/hess-16-2005-2012 ◽

2012 ◽

Vol 16 (7) ◽

pp. 2005-2020 ◽

Cited By ~ 18

Author(s):

S. L. Sun ◽

H. S. Chen ◽

W. M. Ju ◽

J. Song ◽

J. J. Li ◽

...

Keyword(s):

Poyang Lake ◽

Water Cycle ◽

Coupled Model ◽

Soil Water Storage ◽

Future Climate Change ◽

Lake Basin ◽

Poyang Lake Basin ◽

The Future ◽

Intercomparison Project ◽

Meteorological Observations

Abstract. To understand the causes of the past water cycle variations and the influence of climate variability on the streamflow, lake storage, and flood potential, we analyze the changes in streamflow and the underlying drivers in four typical watersheds (Gaosha, Meigang, Saitang, and Xiashan) within the Poyang Lake Basin, based on the meteorological observations at 79 weather stations, and datasets of streamflow and river level at four hydrological stations for the period of 1961-2000. The contribution of different climate factors to the change in streamflow in each watershed is estimated quantitatively using the water balance equations. Results show that in each watershed, the annual streamflow exhibits an increasing trend from 1961–2000. The increases in streamflow by 4.80 m3 s−1 yr−1 and 1.29 m3 s−1 yr−1 at Meigang and Gaosha, respectively, are statistically significant at the 5% level. The increase in precipitation is the biggest contributor to the streamflow increment in Meigang (3.79 m3 s−1 yr−1), Gaosha (1.12 m3 s−1 yr−1), and Xiashan (1.34 m3 s−1 yr−1), while the decrease in evapotranspiration is the major factor controlling the streamflow increment in Saitang (0.19 m3 s−1 yr−1). In addition, radiation and wind contribute more than actual vapor pressure and mean temperature to the changes in evapotranspiration and streamflow for the four watersheds. For revealing the possible change of streamflow due to the future climate change, we also investigate the projected precipitation and evapotranspiration from of the Coupled Model Intercomparison Project phase 3 (CMIP3) under three greenhouse gases emission scenarios (SRESA1B, SRESA2 and SRESB1) for the period of 2061–2100. When the future changes in the soil water storage changes are assumed ignorable, the streamflow shows an uptrend with the projected increases in both precipitation and evapotranspiration (except for the SRESB1 scenario in Xiashan watershed) relative to the observed mean during 1961–2000. Furthermore, the largest increase in the streamflow is found at Meigang (+4.31%) and Xiashan (+3.84%) under the SRESA1B scenario, while the increases will occur at Saitang (+6.87%) and Gaosha (+5.15%) under the SRESB1 scenario.

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How to combine climate models and observations in event attribution?

10.5194/egusphere-egu21-14636 ◽

2021 ◽

Author(s):

Yoann Robin ◽

Aurélien Ribes

Keyword(s):

Bayesian Method ◽

Climate Models ◽

Climate Model ◽

Coupled Model ◽

Model Data ◽

Human Influence ◽

Scale Parameters ◽

Event Attribution ◽

Intercomparison Project ◽

Climate Model Simulations

We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (GEV) distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP) models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. This new method is applied to the July 2019 French heatwave. Our results show that both the probability and the intensity of that event have increased significantly in response to human influence. Remarkably, we find that the heat wave considered might not have been possible without climate change. Our results also suggest that combining model data with observations can improve the description of hot temperature distribution.

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Setup of the PMIP3 paleoclimate experiments conducted using an Earth System Model, MIROC-ESM

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-2527-2012 ◽

2012 ◽

Vol 5 (3) ◽

pp. 2527-2569 ◽

Cited By ~ 1

Author(s):

T. Sueyoshi ◽

R. Ohgaito ◽

A. Yamamoto ◽

M. O. Chikamoto ◽

T. Hajima ◽

...

Keyword(s):

Climate Model ◽

Coupled Model ◽

Earth System Model ◽

Future Climate ◽

System Model ◽

Climate Feedback ◽

Climate Projections ◽

Earth System ◽

Intergovernmental Panel ◽

Intercomparison Project

Abstract. The importance of climate model evaluation using paleoclimate simulations for better future climate projections has been recognized by the Intergovernmental Panel on Climate Change. In recent years, Earth System Models (ESMs) were developed to investigate carbon-cycle climate feedback, as well as to project the future climate. Paleoclimate events, especially those associated with the variations in atmospheric CO2 level or land vegetation, provide suitable benchmarks to evaluate ESMs. Here we present implementations of the paleoclimate experiments proposed by the Coupled Model Intercomparison Project phase 5/Paleoclimate Modelling Intercomparison Project phase 3 (CMIP5/PMIP3) using an Earth System Model, MIROC-ESM. In this paper, experimental settings and procedures of the mid-Holocene, the Last Glacial Maximum, and the Last Millennium experiments are explained. The first two experiments are time slice experiments and the last one is a transient experiment. The complexity of the model requires various steps to correctly configure the experiments. Several basic outputs are also shown.

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Ozone sensitivity to varying greenhouse gases and ozone-depleting substances in CCMI simulations

10.5194/acp-2017-565 ◽

2017 ◽

Cited By ~ 2

Author(s):

Olaf Morgenstern ◽

Hideharu Akiyoshi ◽

Yousuke Yamashita ◽

Douglas E. Kinnison ◽

Rolando R. Garcia ◽

...

Keyword(s):

Greenhouse Gases ◽

Climate Model ◽

Phase 1 ◽

Coupled Model ◽

Total Column Ozone ◽

Ozone Sensitivity ◽

Intercomparison Project ◽

Ozone Depleting Substances ◽

Column Ozone ◽

Total Column

Abstract. Ozone fields simulated for the Chemistry-Climate Model Initiative (CCMI) will be used as forcing data in the 6th Coupled Model Intercomparison Project (CMIP6). Here we assess, using reference and sensitivity simulations produced for phase 1 of CCMI, the suitability of CCMI-1 model results for this process, investigating the degree of consistency amongst models regarding their responses to variations in individual forcings. We consider the influences of methane, nitrous oxide, a combination of chlorinated or brominated ozone-depleting substances (ODSs), and a combination of carbon dioxide and other greenhouse gases (GHGs). We find varying degrees of consistency in the models' responses in ozone to these individual forcings, including some considerable disagreement. In particular, the response of total-column ozone to these forcings is less consistent across the multi-model ensemble than profile comparisons. The likely cause of this is lower-stratospheric transport and dynamical responses exhibiting substantial inter-model differences. The findings imply that the ozone fields derived from CCMI-1 are subject to considerable uncertainties regarding the impacts of these anthropogenic forcings.

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Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models

Journal of Hydrometeorology ◽

10.1175/2011jhm1336.1 ◽

2011 ◽

Vol 12 (4) ◽

pp. 556-578 ◽

Cited By ~ 252

Author(s):

Stefan Hagemann ◽

Cui Chen ◽

Jan O. Haerter ◽

Jens Heinke ◽

Dieter Gerten ◽

...

Keyword(s):

Bias Correction ◽

Climate Model ◽

Hydrological Cycle ◽

Coupled Model ◽

Model Output ◽

Hydrological Models ◽

Statistical Bias ◽

Max Planck ◽

Climate Model Output ◽

Statistical Bias Correction

Abstract Future climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Météorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)—were bias corrected. After the validation of the bias-corrected data, the original and the bias-corrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071–2100 are considered relative to 1961–90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.

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Reducing uncertainties in projections of Antarctic ice mass loss

The Cryosphere Discussions ◽

10.5194/tcd-9-2625-2015 ◽

2015 ◽

Vol 9 (2) ◽

pp. 2625-2654 ◽

Cited By ~ 1

Author(s):

G. Durand ◽

F. Pattyn

Keyword(s):

Mass Loss ◽

Climate Model ◽

Drainage Basin ◽

Ice Sheet ◽

Coupled Model ◽

Model Average ◽

Grounding Line ◽

Polar Ice Sheets ◽

Order Of Magnitude ◽

Intercomparison Project

Abstract. Climate model projections are often aggregated into multi-model averages of all models participating in an Intercomparison Project, such as the Coupled Model Intercomparison Project (CMIP). A first initiative of the ice-sheet modeling community, SeaRISE, to provide multi-model average projections of polar ice sheets' contribution to sea-level rise recently emerged. SeaRISE Antarctic numerical experiments aggregate results from all models willing to participate without any selection of the models regarding the processes implemented in. Here, using the experimental set-up proposed in SeaRISE we confirm that the representation of grounding line dynamics is essential to infer future Antarctic mass change. We further illustrate the significant impact on the ensemble mean and deviation of adding one model with a known biais in its ability of modeling grounding line dynamics. We show that this biased model can hardly be discriminated from the ensemble only based on its estimation of volume change. However, tools are available to test parts of the response of marine ice sheet models to perturbations of climatic and/or oceanic origin (MISMIP, MISMIP3d). Based on recent projections of the Pine Island Glacier mass loss, we further show that excluding ice sheet models that do not pass the MISMIP benchmarks decreases by an order of magnitude the mean contribution and standard deviation of the multi-model ensemble projection for that particular drainage basin.

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Modelling the mass budget and future evolution of Tunabreen, central Spitsbergen

10.5194/tc-2021-155 ◽

2021 ◽

Author(s):

Johannes Oerlemans ◽

Jack Kohler ◽

Adrian Luckman

Keyword(s):

Response Times ◽

Equilibrium Line Altitude ◽

Future Evolution ◽

State Response ◽

The Past ◽

Glacier Length ◽

Model Geometry ◽

Calving Rate ◽

Meteorological Observations

Abstract. Tunabreen is a 26-km long tidewater glacier. It is the most frequently surging glacier in Svalbard, with four documented surges in the past hundred years. We have modelled the evolution of this glacier with a Minimal Glacier Model (MGM), in which ice mechanics, calving and surging are parameterized. The model geometry consists of a flow band to which three tributaries supply mass. The calving rate is set to the mean observed value for the period 2012–2019, and kept constant. For the past 120 years, a smooth Equilibrium Line Altitude (ELA) history is reconstructed by finding the best possible match between observed and simulated glacier length. There is a modest correlation between this ELA history and meteorological observations from Longyearbyen. The simulated glacier retreat is in good agreement with observations. Runs with and without surging show that the effect of surging on the long term glacier evolution is limited. Due to the low surface slope and associated strong height -mass balance feedback, Tunabreen is very sensitive to changes in ELA. For a constant future ELA equal to the reconstructed value for 2020, the glacier front will retreat by 8 km during the coming hundred years. For an increase of the ELA of 2 m per year, the retreat is projected to be 13 km and Tunabreen becomes a land-based glacier around 2100. The calving rate is an important parameter: increasing its value by 50 % has about the same effect as a 50 m increase in the ELA, the corresponding equilibrium glacier length being 18 km (as compared to 25.8 km in the reference state). Response times vary from 150 to 400 years, depending on the forcing and on the state of the glacier (tidewater or land-based).

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Simulation of the observed climate extremes trends during 1901–2010 with INMCM5

10.5194/egusphere-egu2020-16407 ◽

2020 ◽

Author(s):

Maria Tarasevich ◽

Evgeny Volodin

Keyword(s):

Climate Models ◽

Climate Model ◽

Great Influence ◽

Coupled Model ◽

Climate Extremes ◽

Reanalysis Data ◽

Natural Systems ◽

Weather Events ◽

Intercomparison Project

Extreme climate and weather events have a great influence on society and natural systems. That&#8217;s why it is important to be able to precisely simulate these events with the climate models. To asses the quality of such simulations 27 climate extremes indices were defined by ETCCDI. In the present work these indices are calculated for the 1901&#8211;2010 in order to estimate their trends. Climate extremes trends are studied on the basis of ten historical runs with the up-to-date INM RAS climate model (INMCM5) under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Developed by ECMWF ERA-20C and CERA-20C reanalyses are taken as observational data. Trends obtained from the reanalysis data are compared with the simulation results of the INMCM5. The comparison shows that the simulated land-averaged climate extremes trends are in good agreement with the reanalysis data, but their spatial distributions differ significantly even between the reanalyses themselves.

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