An earth system model shows self-sustained melting of permafrost even if all man-made GHG emissions stop in 2020

AbstractThe risk of points-of-no-return, which, once surpassed lock the world into new dynamics, have been discussed for decades. Recently, there have been warnings that some of these tipping points are coming closer and are too dangerous to be disregarded. In this paper we report that in the ESCIMO climate model the world is already past a point-of-no-return for global warming. In ESCIMO we observe self-sustained melting of the permafrost for hundreds of years, even if global society stops all emissions of man-made GHGs immediately. We encourage other model builders to explore our discovery in their (bigger) models, and report on their findings. The melting (in ESCIMO) is the result of a continuing self-sustained rise in the global temperature. This warming is the combined effect of three physical processes: (1) declining surface albedo (driven by melting of the Arctic ice cover), (2) increasing amounts of water vapour in the atmosphere (driven by higher temperatures), and (3) changes in the concentrations of the GHG in the atmosphere (driven by the absorption of CO2 in biomass and oceans, and emission of carbon (CH4 and CO2) from melting permafrost). This self-sustained, in the sense of no further GHG emissions, melting process (in ESCIMO) is a causally determined, physical process that evolves over time. It starts with the man-made warming up to the 1950s, leading to a rise in the amount of water vapour in the atmosphere—further lifting the temperature, causing increasing release of carbon from melting permafrost, and simultaneously a decline in the surface albedo as the ice and snow covers melts. To stop the self-sustained warming in ESCIMO, enormous amounts of CO2 have to be extracted from the atmosphere.

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Effect of surface albedo, water vapour, and atmospheric aerosols on the cloud-free shortwave radiative budget in the Arctic

Climate Dynamics ◽

10.1007/s00382-011-1280-1 ◽

2012 ◽

Vol 39 (3-4) ◽

pp. 953-969 ◽

Cited By ~ 11

Author(s):

C. Di Biagio ◽

A. di Sarra ◽

P. Eriksen ◽

S. E. Ascanius ◽

G. Muscari ◽

...

Keyword(s):

Water Vapour ◽

Atmospheric Aerosols ◽

Surface Albedo ◽

The Arctic ◽

Effect Of Surface

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Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY

Atmospheric Measurement Techniques ◽

10.5194/amt-11-2949-2018 ◽

2018 ◽

Vol 11 (5) ◽

pp. 2949-2965 ◽

Cited By ~ 6

Author(s):

Dunya Alraddawi ◽

Alain Sarkissian ◽

Philippe Keckhut ◽

Olivier Bock ◽

Stefan Noël ◽

...

Keyword(s):

Time Series ◽

Water Vapour ◽

Satellite Data ◽

Cloud Cover ◽

Surface Albedo ◽

Hydrological Cycle ◽

Radiation Budget ◽

The Arctic ◽

Gnss Measurements ◽

Gnss Time Series

Abstract. Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total column water vapour (TCWV) datasets derived from ground-based GNSS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The comparisons between GNSS and satellite data are carried out for three reference Arctic observation sites (Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of more than a decade (2001–2014) are available. We select hourly GNSS data that are coincident with overpasses of the different satellites over the three sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GNSS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GNSS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitude stations during autumn and winter). SCIAMACHY TCWV data are generally drier than GNSS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Ålesund and Thule. The dry biases of MODIS and SCIAMACHY observations are most pronounced at Sodankylä during the snow season (from October to March). Regarding SCIAMACHY, this bias is possibly linked to the fact that the SCIAMACHY TCWV retrieval does not take accurately into account the variations in surface albedo, notably in the presence of snow with a nearby canopy as in Sodankylä. The MODIS bias at Sodankylä is found to be correlated with cloud cover fraction and is also expected to be affected by other atmospheric or surface albedo changes linked for instance to the presence of forests or anthropogenic emissions. Overall, the results point out that a better estimation of seasonally dependent surface albedo and a better consideration of vertically resolved cloud cover are recommended if biases in satellite measurements are to be reduced in the polar regions.

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Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes

10.5194/gmd-2019-113 ◽

2019 ◽

Author(s):

Alex West ◽

Mat Collins ◽

Ed Blockley

Keyword(s):

Sea Ice ◽

Mass Balance ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

The North ◽

Energy Evaluation ◽

Ice Mass Balance ◽

Arctic Ice

Abstract. Arctic sea ice has declined rapidly over recent decades. Models predict that the Arctic will be nearly ice-free by mid-century, but the spread in predictions of sea ice extent is currently large. The reasons for this spread are poorly understood, partly due to a lack of observations with which the processes by which Arctic atmospheric and oceanic forcing affect sea ice state can be examined. In this study, a method of estimating fluxes of top melt, top conduction, basal conduction and ocean heat flux from Arctic ice mass balance buoy elevation and temperature data is presented. The derived fluxes are used to evaluate modelled fluxes from the coupled climate model HadGEM2-ES in two densely sampled regions of the Arctic, the North Pole and Beaufort Sea. The evaluation shows the model to overestimate the magnitude of summer top melting fluxes, and winter conductive fluxes, results which are physically consistent with an independent sea ice and surface energy evaluation of the same model.

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Climate Warming in Response to Emission Reductions Consistent with the Paris Agreement

Advances in Meteorology ◽

10.1155/2018/2487962 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 7

Author(s):

Fang Wang ◽

Katarzyna B. Tokarska ◽

Jintao Zhang ◽

Quansheng Ge ◽

Zhixin Hao ◽

...

Keyword(s):

Climate Warming ◽

Temperature Increase ◽

Climate Model ◽

Ghg Emissions ◽

Paris Agreement ◽

The Arctic ◽

Emission Reductions ◽

Global Mean Temperature ◽

Mean Temperature ◽

Global Mean

To limit global warming to well below 2°C in accord with the Paris Agreement, countries throughout the world have submitted their Intended Nationally Determined Contributions (INDCs) outlining their greenhouse gas (GHG) mitigation actions in the next few decades. However, it remains unclear what the resulting climate change is in response to the proposed INDCs and subsequent emission reductions. In this study, the global and regional warming under the updated INDC scenarios was estimated from a range of comprehensive Earth system models (CMIP5) and a simpler carbon-climate model (MAGICC), based on the relationship of climate response to cumulative emissions. The global GHG emissions under the updated INDC pledges are estimated to reach 14.2∼15.0 GtC/year in 2030, resulting in a global mean temperature increase of 1.29∼1.55°C (median of 1.41°C) above the preindustrial level. By extending the INDC scenarios to 2100, global GHG emissions are estimated to be around 6.4∼9.0 GtC/year in 2100, resulting in a global mean temperature increase by 2.67∼3.74°C (median of 3.17°C). The Arctic warming is projected to be most profound, exceeding the global average by a factor of three by the end of this century. Thus, climate warming under INDC scenarios is projected to greatly exceed the long-term Paris Agreement goal of stabilizing the global mean temperature at to a low level of 1.5‐2.0°C above the pre-industrial. Our study suggests that the INDC emission commitments need to be adjusted and strengthened to bridge this warming gap.

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Observations of surface radiation and stratospheric processes at Thule Air Base, Greenland, during the IPY

Annals of Geophysics ◽

10.4401/ag-6382 ◽

2014 ◽

Vol 57 (3) ◽

Author(s):

Giovanni Muscari ◽

Claudia Di Biagio ◽

Alcide di Sarra ◽

Marco Cacciani ◽

Svend Erik Ascanius ◽

...

Keyword(s):

Water Vapour ◽

Surface Albedo ◽

Middle Atmosphere ◽

Stratospheric Ozone ◽

Surface Radiation ◽

Atmospheric Composition ◽

Radiation Budget ◽

The Arctic ◽

Western Coast ◽

International Polar Year

Ground-based measurements of atmospheric parameters have been carried out for more than 20 years at the Network for the Detection of Atmospheric Composition Change (NDACC) station at Thule Air Base (76.5°N, 68.8°W), on the north-western coast of Greenland. Various instruments dedicated to the study of the lower and middle polar atmosphere are installed at Thule in the framework of a long standing collaboration among Danish, Italian, and US research institutes and universities. This effort aims at monitoring the composition, structure and dynamics of the polar stratosphere, and at studying the Arctic energy budget and the role played by different factors, such as aerosols, water vapour, and surface albedo. During the International Polar Year (IPY), in winter 2008-2009, an intensive measurement campaign was conducted at Thule within the framework of the IPY project “Ozone layer and UV radiation in a changing climate evaluated during IPY” (ORACLE-O3) which sought to improve our understanding of the complex mechanisms that lead to the Arctic stratospheric O3 depletion. The campaign involved a lidar system, measuring aerosol backscatter and depolarization ratios up to 35 km and atmospheric temperature profiles from 25 to 70 km altitude, a ground-based millimeter-wave spectrometer (GBMS) used to derive stratospheric mixing ratio profiles of different chemical species involved in the stratospheric ozone depletion cycle, and then ground-based radiometers and a Cimel sunphotometer to study the Arctic radiative budget at the surface. The observations show that the surface radiation budget is mainly regulated by the longwave component throughout most of the year. Clouds have a significant impact contributing to enhance the role of longwave radiation. Besides clouds, water vapour seasonal changes produce the largest modification in the shortwave component at the surface, followed by changes in surface albedo and in aerosol amounts. For what concerns the middle atmosphere, during the first part of winter 2008-2009 the cold polar vortex allowed for the formation of polar stratospheric clouds (PSCs) which were observed above Thule by means of the lidar. This period was also characterized by GBMS measurements of low values of O3 due to the catalytic reactions prompted by the PSCs. In mid-January, as the most intense Sudden Stratospheric Warming event ever observed in the Arctic occurred, GBMS and lidar measurements of O3, N2O, CO and temperature described its evolution as it propagated from the upper atmosphere to the lower stratosphere.

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Variability of water vapour in the Arctic stratosphere

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-22013-2015 ◽

2015 ◽

Vol 15 (16) ◽

pp. 22013-22045

Author(s):

L. Thölix ◽

L. Backman ◽

R. Kivi ◽

A. Karpechko

Keyword(s):

Water Vapour ◽

Large Scale ◽

Climate Model ◽

Model Simulation ◽

Polar Vortex ◽

The Arctic ◽

Cold Pool ◽

Model Calculations ◽

Water Vapour Concentration ◽

Stratospheric Water Vapour

Abstract. This study evaluates the stratospheric water vapour distribution and variability in the Arctic. A FinROSE chemistry climate model simulation covering years 1990–2013 is compared to observations (satellite and frostpoint hygrometer soundings) and the sources of stratospheric water vapour are studied. According to observations and the simulations the water vapour concentration in the Arctic stratosphere started to increase after year 2006, but around 2011 the concentration started to decrease. Model calculations suggest that the increase in water vapour during 2006–2011 (at 56 hPa) is mostly explained by transport related processes, while the photochemically produced water vapour plays a relatively smaller role. The water vapour trend in the stratosphere may have contributed to increased ICE PSC occurrence. The increase of water vapour in the precense of the low winter temperatures in the Arctic stratosphere led to more frequent occurrence of ICE PSCs in the Arctic vortex. The polar vortex was unusually cold in early 2010 and allowed large scale formation of the polar stratospheric clouds. The cold pool in the stratosphere over the Northern polar latitudes was large and stable and a large scale persistent dehydration was observed. Polar stratospheric ice clouds and dehydration were observed at Sodankylä with accurate water vapour soundings in January and February 2010 during the LAPBIAT atmospheric sounding campaign. The observed changes in water vapour were reproduced by the model. Both the observed and simulated decrease of the water vapour in the dehydration layer was up to 1.5 ppm.

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On the role of sea-ice transport in modifying arctic responses to global climate change

Annals of Glaciology ◽

10.1017/s0260305500013860 ◽

1997 ◽

Vol 25 ◽

pp. 102-106 ◽

Cited By ~ 6

Author(s):

James Maslanik ◽

Jeremy Dunn

Keyword(s):

Climate Change ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

The Arctic ◽

Climate Change Responses ◽

Ice Conditions ◽

Arctic Ice ◽

Ice Pack

The role of dynamics in modifying the response of the Arctic ice pack to inter-annually varying forcings and to climate perturbations is investigated using simulations from a two-dimensional ice model and a global climate model (GCM). Inter-annual variability in ice-covered area for 1985-93 is dominated by ice transport, and different transport regimes affect substantially the response of the ice pack to climate perturbations. The thermodynamic-only simulations are more sensitive to initial ice conditions, and respond less than the dynamk-thermodynamic model to small perturbations, but with a greater response to larger perturbations. Comparisons of GCM simulations that use different ice treatments highlights the importance of considering the distribution of ice thickness and extent in assessing climate-change responses.

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On determining the Point of no Return in Climate Change

10.5194/esd-2016-40 ◽

2016 ◽

Author(s):

Brenda C. van Zalinge ◽

Qing Yi Feng ◽

Henk A. Dijkstra

Keyword(s):

Climate Models ◽

Climate Model ◽

Ghg Emissions ◽

Quantitative Measure ◽

Linear Response Theory ◽

Stochastic Viability ◽

Period 1880 ◽

Point Of No Return ◽

Dangerous Anthropogenic Interference ◽

Global Mean

Abstract. Earth's Global Mean Surface Temperature (GMST) has increased by about 1.0 °C over the period 1880–2015. One of the main causes is thought to be the increase in atmospheric greenhouse gases (GHGs). If GHG emissions are not substantially decreased, several studies indicate there will be a dangerous anthropogenic interference (DAI) with climate by the end of this century. However, there is no good quantitative measure to determine when it is "too late" to start reducing GHGs in order to avoid DAI. In this study, we develop a method for determining a so-called Point of No Return (PNR) for several GHG emission scenarios. The method is based on a combination of stochastic viability theory and uses linear response theory to estimate the probability density function of the GMST. The innovative element in this approach is the applicability to high-dimensional climate models as is demonstrated by results obtained with the PLASIM climate model.

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Comparison of total water vapour content in the Arctic derived from GPS, AIRS, MODIS and SCIAMACHY

10.5194/amt-2017-195 ◽

2017 ◽

Cited By ~ 2

Author(s):

Dunya Alraddawi ◽

Alain Sarkissian ◽

Philippe Keckhut ◽

Olivier Bock ◽

Stefan Noël ◽

...

Keyword(s):

Time Series ◽

Water Vapour ◽

Satellite Data ◽

Cloud Cover ◽

Surface Albedo ◽

Radiation Budget ◽

The Arctic ◽

Gps Measurements ◽

Data Set ◽

Gps Time Series

Abstract. Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total Column Water Vapour (TCWV) data set derived from ground-based GPS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectrometer (MODIS), the Atmospheric Infrared System (AIRS), and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The comparisons between GPS and satellite data are carried out for three reference Arctic observation sites (Sodankyla, Ny-Alesund and Thule) where long homogeneous GPS time series are available. We select hourly GPS data that are coincident with overpasses of the different satellites over the 3 sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GPS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GPS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i.e. high latitude stations during fall and winter). SCIAMACHY TCWV data are generally drier than GPS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Alesund and Thule. The dry biases of MODIS and SCIAMACHY observations are most pronounced at Sodankyla during the snow season (from October to March). Regarding SCIAMACHY, this bias is possibly linked to the fact that the SCIAMACHY TCWV retrieval does not take accurately into account the variations in surface albedo, notably in the presence of snow with a nearby canopy as in Sodankyla. The MODIS bias at Sodankyla is found to be correlated with cloud cover fraction and is also expected to be affected by other atmospheric or surface albedo changes linked for instance to the presence of forests or anthropogenic emissions. Overall, the results point out that a better estimation of seasonally-dependent surface albedo and a better consideration of vertically-resolved cloud cover are recommended if biases in satellite measurements are to be reduced in polar regions.

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