scholarly journals Regional climate of the Larsen B embayment 1980–2014

2017 ◽  
Vol 63 (240) ◽  
pp. 683-690 ◽  
Author(s):  
A. A. LEESON ◽  
J. M. VAN WESSEM ◽  
S. R. M. LIGTENBERG ◽  
A. SHEPHERD ◽  
M. R. VAN DEN BROEKE ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Local Scale ◽  
Climate Response ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Ice Shelf ◽  
The Antarctic

ABSTRACTUnderstanding the climate response of the Antarctic Peninsula ice sheet is vital for accurate predictions of sea-level rise. However, since climate models are typically too coarse to capture spatial variability in local scale meteorological processes, our ability to study specific sectors has been limited by the local fidelity of such models and the (often sparse) availability of observations. We show that a high-resolution (5.5 km × 5.5 km) version of a regional climate model (RACMO2.3) can reproduce observed interannual variability in the Larsen B embayment sufficiently to enable its use in investigating long-term changes in this sector. Using the model, together with automatic weather station data, we confirm previous findings that the year of the Larsen B ice shelf collapse (2001/02) was a strong melt year, but discover that total annual melt production was in fact ~30% lower than 2 years prior. While the year before collapse exhibited the lowest melting and highest snowfall during 1980–2014, the ice shelf was likely pre-conditioned for collapse by a series of strong melt years in the 1990s. Melt energy has since returned to pre-1990s levels, which likely explains the lack of further significant collapse in the region (e.g. of SCAR Inlet).

Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Near Surface ◽  
Virtual Lab ◽  
Model Variability ◽  
The Antarctic

<p>Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.</p><p>Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.</p>

scholarly journals The Atmospheric River Threat to Antarctic Peninsula Ice-Shelf Stability

2021 ◽  
Author(s):  
Jonathan Wille ◽  
Vincent Favier ◽  
Nicolas Jourdain ◽  
Christoph Kittel ◽  
Jenny Turton ◽  
...  
Keyword(s):  
Antarctic Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Detection Algorithm ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Atmospheric River ◽  
The Antarctic ◽  
Collapse Theories ◽  
Temperature Surface

Abstract The disintegration of the ice shelves along the Antarctic Peninsula have spurred much discussion on the various processes leading to their eventual dramatic collapse, but without a consensus on an atmospheric forcing that could connect these processes. Here, using an atmospheric river (AR) detection algorithm along with a regional climate model and satellite observations, we show that particularly intense ARs have a ~40% probability of inducing extreme events of temperature, surface melt, sea-ice disintegration, or large swells; all processes proven to induce ice-shelf destabilization. This was observed during the collapses of the Larsen A, B, and overall, 60% of calving events triggered by ARs from 2000-2020. The loss of the buttressing effect from these ice shelves leads to further continental ice loss and subsequent sea-level rise. Understanding how ARs connect various disparate processes cited in ice-shelf collapse theories is essential for identifying other at-risk ice shelves like the Larsen C.

An assessment of basal melting parameterisations for Antarctic ice shelves

2021 ◽  
Author(s):  
Clara Burgard ◽  
Nicolas Jourdain
Keyword(s):  
Climate Model ◽  
Ice Sheet ◽  
Coupled Model ◽  
Ice Shelf ◽  
Antarctic Ice Sheet ◽  
Ice Shelves ◽  
Global Sea Level ◽  
Basal Melting ◽  
The Antarctic

<p>Ocean-induced melting at the base of ice shelves is one of the main drivers of the currently observed mass loss of the Antarctic Ice Sheet. A good understanding of the interaction between ice and ocean at the base of the ice shelves is therefore crucial to understand and project the Antarctic contribution to global sea-level rise. </p><p>Due to the high difficulty to monitor these regions, our understanding of the processes at work beneath ice shelves is limited. Still, several parameterisations of varying complexity have been developed in past decades to describe the ocean-induced sub-shelf melting. These parameterisations can be implemented into standalone ice-sheet models, for example when conducting long-term projections forced with climate model output.</p><p>An assessment of the performance of these parameterisations was conducted in an idealised setup (Favier et al, 2019). However, the application of the better-performing parameterisations in a more realistic setup (e.g. Jourdain et al., 2020) has shown that individual adjustments and corrections are needed for each ice shelf.</p><p>In this study, we revisit the assessment of the parameterisations, this time in a more realistic setup than previous studies. To do so, we apply the different parameterisations on several ice shelves around Antarctica and compare the resulting melt rates to satellite and oceanographic estimates. Based on this comparison, we will refine the parameters and propose an approach to reduce uncertainties in long-term sub-shelf melting projections.</p><p><em>References</em><br><em>- Favier, L., Jourdain, N. C., Jenkins, A., Merino, N., Durand, G., Gagliardini, O., Gillet-Chaulet, F., and Mathiot, P.: Assessment of sub-shelf melting parameterisations using the ocean–ice-sheet coupled model NEMO(v3.6)–Elmer/Ice(v8.3) , Geosci. Model Dev., 12, 2255–2283, https://doi.org/10.5194/gmd-12-2255-2019, 2019. </em><br><em>- Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020. </em></p>

scholarly journals Evolution of the Repository in Nizhnekanskiy Massif under the Influence of Climatic Factors

2021 ◽  
Vol 14 (1) ◽  
pp. 63-75
Author(s):  
B. T. Kochkin ◽  
◽  
S. A. Bogatov ◽  
E. A. Saveleva ◽  
◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Climatic Factors ◽  
Regional Climate ◽  
Climate History ◽  
Research Areas ◽  
Current State ◽  
History Of ◽  
General Climate

The study identifies climatic factors related to external influences governing the long-term evolution of the RW disposal system at the Yeniseyskiy site. The paper evaluates the current state of knowledge about these factors and indicates most important research areas. These include: paleoclimatic studies in the repository region to clarify the climate history of past geological epochs; quantitative regional climate forecast based on global numerical climate models; special forecasts based on a general climate model of the region allowing, in particular, to clarify the depth of rock mass freezing during ice periods.

scholarly journals A Comparison of Intra-Annual and Long-Term Trend Scaling of Extreme Precipitation with Temperature in a Large-Ensemble Regional Climate Simulation

2020 ◽  
Vol 33 (21) ◽  
pp. 9233-9245
Author(s):  
Qiaohong Sun ◽  
Francis Zwiers ◽  
Xuebin Zhang ◽  
Guilong Li
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Data Availability ◽  
Climate Simulation ◽  
Large Ensemble ◽  
Near Surface ◽  
High Background

AbstractLong-term changes in extreme daily and subdaily precipitation simulated by climate models are often compared with corresponding temperature changes to estimate the sensitivity of extreme precipitation to warming. Such “trend scaling” rates are difficult to estimate from observations, however, because of limited data availability and high background variability. Intra-annual temperature scaling (here called binning scaling), which relates extreme precipitation to temperature at or near the time of occurrence, has been suggested as a possible substitute for trend scaling. We use a large ensemble simulation of the Canadian regional climate model (CanRCM4) to assess this possibility, considering both daily near-surface air temperature and daily dewpoint temperature as scaling variables. We find that binning curves that are based on precipitation data for the whole year generally look like the composite of binning curves for winter and summer, with the lower temperature portion similar to winter and the higher temperature portion similar to summer, indicating that binning curves reflect seasonal changes in the relationship between temperature and extreme precipitation. The magnitude and spatial pattern of binning and trend scaling rates are also quantitatively different, with little spatial correlation between them, regardless of precipitation duration or choice of temperature variable. The evidence therefore suggests that binning scaling with temperature is not a reliable predictor for future changes in precipitation extremes in the climate simulated by CanRCM4. Nevertheless, external forcing does have a discernable influence on binning curves, which are seen to shift upward and to the right in some regions, consistent with a general increase in extreme precipitation.

scholarly journals Melting over the East Antarctic Peninsula (1999–2009): evaluation of a high-resolution regional climate model

2017 ◽  
Author(s):  
Rajashree T. Datta ◽  
Marco Tedesco ◽  
Cecile Agosta ◽  
Xavier Fettweis ◽  
Peter Kuipers Munneke ◽  
...  
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Spatial Resolution ◽  
Antarctic Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Shelf ◽  
Circulation Patterns ◽  
The Antarctic ◽  
Microwave Data

Abstract. Surface melting over the Antarctic Peninsula (AP) plays a crucial role for the stability of ice shelves and dynamics of grounded ice, hence modulating the mass balance in a region of the world which is particularly sensitive to increasing surface temperatures. Understanding the processes that drive melting using surface energy and mass balance models is fundamental to improving estimates of current and future surface melting and associated sea level rise through ice-shelf collapse. This is even more important in view of both the paucity of in-situ measurements in Antarctica generally and the specific challenges presented by the circulation patterns over the Antarctic Peninsula. In this study, we evaluate the regional climate model Modèle Atmosphérique Régionale (MAR) over the Antarctic Peninsula (AP) at a 10 km spatial resolution between 1999 and 2009, a period which coincides with the availability of active microwave data from the QuikSCAT mission. This is the first time that this model, which has been validated extensively over Greenland, has been applied to the Antarctic Peninsula at a high resolution. We compare melt occurrence modeled by MAR with a combination of estimates from passive and active microwave data. Our primary regional focus is the northern East Antarctic Peninsula (East AP), where we evaluate MAR against wind and temperature data collected by three automatic weather stations (AWS). Our results indicate that satellites estimates show greater melt frequency, a larger melt extent, and a quicker expansion to peak melt extent than MAR in the center and east of the Larsen C ice shelf. The difference between the remote sensing and modeled estimates reduces in the north and west of the East AP. Melting in the East AP can be initiated by both sporadic westerly föhn flow over the AP and northerly winds advecting warm air from lower latitudes. To quantify MAR's ability to simulate different circulation patterns that affect melt, we take a unique approach to evaluate melt occurrence (using satellite data) and concurrent temperature biases associated with specific wind direction biases using AWS data over the Larsen Ice Shelf. Our results indicate that although MAR shows an overall warm bias, it also shows fewer warm, strong westerly winds than reported by AWS stations, which may lead to an underestimation of melt. The underestimation of föhn flow in the east of the Larsen C may potentially be resolved by removing the hydrostatic assumption in MAR or increasing spatial resolution. The underestimation of southwesterly flow in particular may be reduced by using higher-resolution topography.

scholarly journals Predicting global patterns of long-term climate change from short-term simulations using machine learning

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
L. A. Mansfield ◽  
P. J. Nowack ◽  
M. Kasoar ◽  
R. G. Everitt ◽  
W. J. Collins ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Mitigation Measures ◽  
Climate Modelling ◽  
Short Term ◽  
Climate Change Projections

AbstractUnderstanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art climate models remains a central bottleneck in this endeavour. Here we introduce a machine learning approach, which utilises a unique dataset of existing climate model simulations to learn relationships between short-term and long-term temperature responses to different climate forcing scenarios. This approach not only has the potential to accelerate climate change projections by reducing the costs of scenario computations, but also helps uncover early indicators of modelled long-term climate responses, which is of relevance to climate change detection, predictability, and attribution. Our results highlight challenges and opportunities for data-driven climate modelling, especially concerning the incorporation of even larger model datasets in the future. We therefore encourage extensive data sharing among research institutes to build ever more powerful climate response emulators, and thus to enable faster climate change projections.

scholarly journals A Satellite-Based Climatology of Wind-Induced Surface Temperature Anomalies for the Antarctic

2019 ◽  
Vol 11 (13) ◽  
pp. 1539 ◽  
Author(s):  
Günther Heinemann ◽  
Lukas Glaw ◽  
Sascha Willmes
Keyword(s):  
Surface Temperature ◽  
Climate Model ◽  
Regional Climate ◽  
Weddell Sea ◽  
Model Data ◽  
Ice Shelf ◽  
Near Surface ◽  
Surface Stability ◽  
Ross Ice Shelf ◽  
The Antarctic

It is well-known that katabatic winds can be detected as warm signatures in the surface temperature over the slopes of the Antarctic ice sheets. For appropriate synoptic forcing and/or topographic channeling, katabatic surges occur, which result in warm signatures also over adjacent ice shelves. Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) data are used to detect warm signatures over the Antarctic for the winter periods 2002–2017. In addition, high-resolution (5 km) regional climate model data is used for the years of 2002 to 2016. We present a case study and a climatology of wind-induced IST anomalies for the Ross Ice Shelf and the eastern Weddell Sea. The IST anomaly distributions show maxima around 10–15K for the slopes, but values of more than 25K are also found. Katabatic surges represent a strong climatological signal with a mean warm anomaly of more than 5K on more than 120 days per winter for the Byrd Glacier and the Nimrod Glacier on the Ross Ice Shelf. The mean anomaly for the Brunt Ice Shelf is weaker, and exceeds 5K on about 70 days per winter. Model simulations of the IST are compared to the MODIS IST, and show a very good agreement. The model data show that the near-surface stability is a better measure for the response to the wind than the IST itself.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

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