scholarly journals Comments on “Current GCMs' Unrealistic Negative Feedback in the Arctic”

2013 ◽  
Vol 26 (19) ◽  
pp. 7783-7788 ◽  
Author(s):  
Felix Pithan ◽  
Thorsten Mauritsen
Keyword(s):  
Permutation Test ◽  
Coupled Model ◽  
Lapse Rate ◽  
The Arctic ◽  
Model Ensemble ◽  
Cmip3 Model ◽  
Rate Feedback ◽  
Intercomparison Project ◽  
Arctic Temperature ◽  
Temperature Inversions

Abstract In contrast to prior studies showing a positive lapse-rate feedback associated with the Arctic inversion, Boé et al. reported that strong present-day Arctic temperature inversions are associated with stronger negative longwave feedbacks and thus reduced Arctic amplification in the model ensemble from phase 3 of the Coupled Model Intercomparison Project (CMIP3). A permutation test reveals that the relation between longwave feedbacks and inversion strength is an artifact of statistical self-correlation and that shortwave feedbacks have a stronger correlation with intermodel spread. The present comment concludes that the conventional understanding of a positive lapse-rate feedback associated with the Arctic inversion is consistent with the CMIP3 model ensemble.

scholarly journals Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change

2020 ◽  
Vol 14 (9) ◽  
pp. 3155-3174 ◽  
Author(s):  
Eleanor J. Burke ◽  
Yu Zhang ◽  
Gerhard Krinner
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
High Latitude ◽  
Soil Profile ◽  
Coupled Model ◽  
The Arctic ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Northern High Latitude ◽  
Intercomparison Project

Abstract. Permafrost is a ubiquitous phenomenon in the Arctic. Its future evolution is likely to control changes in northern high-latitude hydrology and biogeochemistry. Here we evaluate the permafrost dynamics in the global models participating in the Coupled Model Intercomparison Project (present generation – CMIP6; previous generation – CMIP5) along with the sensitivity of permafrost to climate change. Whilst the northern high-latitude air temperatures are relatively well simulated by the climate models, they do introduce a bias into any subsequent model estimate of permafrost. Therefore evaluation metrics are defined in relation to the air temperature. This paper shows that the climate, snow and permafrost physics of the CMIP6 multi-model ensemble is very similar to that of the CMIP5 multi-model ensemble. The main differences are that a small number of models have demonstrably better snow insulation in CMIP6 than in CMIP5 and a small number have a deeper soil profile. These changes lead to a small overall improvement in the representation of the permafrost extent. There is little improvement in the simulation of maximum summer thaw depth between CMIP5 and CMIP6. We suggest that more models should include a better-resolved and deeper soil profile as a first step towards addressing this. We use the annual mean thawed volume of the top 2 m of the soil defined from the model soil profiles for the permafrost region to quantify changes in permafrost dynamics. The CMIP6 models project that the annual mean frozen volume in the top 2 m of the soil could decrease by 10 %–40 %∘C-1 of global mean surface air temperature increase.

scholarly journals Causes of the Arctic’s Lower-Tropospheric Warming Structure

2021 ◽  
pp. 1-52
Keyword(s):  
Climate Change ◽  
Boundary Layer ◽  
Sea Ice ◽  
Temperature Response ◽  
Lapse Rate ◽  
The Arctic ◽  
Turbulent Heat ◽  
Temperature Structure ◽  
Arctic Temperature ◽  
Temperature Inversions

Abstract Arctic amplification has been attributed predominantly to a positive lapse rate feedback in winter, when boundary-layer temperature inversions focus warming near the surface. Predicting high-latitude climate change effectively thus requires identifying the local and remote physical processes that set the Arctic’s vertical warming structure. In this study, we analyze output from the CESM Large Ensemble’s 21st century climate change projection to diagnose the relative influence of two Arctic heating sources, local sea-ice loss and remote changes in atmospheric heat transport. Causal effects are quantified with a statistical inference method, allowing us to assess the energetic pathways mediating the Arctic temperature response and the role of internal variability across the ensemble. We find that a step-increase in latent heat flux convergence causes Arctic lower-tropospheric warming in all seasons, while additionally reducing net longwave cooling at the surface. However, these effects only lead to small and short-lived changes in boundary layer inversion strength. By contrast, a step-decrease in sea-ice extent in the melt season causes, in fall and winter, surface-amplified warming and weakened boundary-layer temperature inversions. Sea-ice loss also enhances surface turbulent heat fluxes and cloud-driven condensational heating, which mediate the atmospheric temperature response. While the aggregate effect of many moist transport events and seasons of sea-ice loss will be different than the response to hypothetical perturbations, our results nonetheless highlight the mechanisms that alter the Arctic temperature inversion in response to CO2 forcing. As sea ice declines, the atmosphere’s boundary-layer temperature structure is weakened, static stability decreases, and a thermodynamic coupling emerges between the Arctic surface and the overlying troposphere.

scholarly journals Causes of the Arctic's Lower-Tropospheric Warming Structure

2021 ◽  
Author(s):  
Zachary Kaufman ◽  
Nicole Feldl
Keyword(s):  
Climate Change ◽  
Boundary Layer ◽  
Sea Ice ◽  
Temperature Response ◽  
Lapse Rate ◽  
The Arctic ◽  
Turbulent Heat ◽  
Temperature Structure ◽  
Arctic Temperature ◽  
Temperature Inversions

Arctic amplification has been attributed predominantly to a positive lapse rate feedback in winter, when boundary-layer temperature inversions focus warming near the surface. Predicting high-latitude climate change effectively thus requires identifying the local and remote physical processes that set the Arctic’s vertical warming structure. In this study, we analyze output from the CESM Large Ensemble's 21st century climate change projection to diagnose the relative influence of two Arctic heating sources, local sea-ice loss and remote changes in atmospheric heat transport. Causal effects are quantified with a statistical inference method, allowing us to assess the energetic pathways mediating the Arctic temperature response and the role of internal variability across the ensemble. We find that a step-increase in latent heat flux convergence causes Arctic lower-tropospheric warming in all seasons, while additionally reducing net longwave cooling at the surface. However, these effects only lead to small and short-lived changes in boundary layer inversion strength. By contrast, a step-decrease in sea-ice extent in the melt season causes, in fall and winter, surface-amplified warming and weakened boundary-layer temperature inversions. Sea-ice loss also enhances surface turbulent heat fluxes and cloud-driven condensational heating, which mediate the atmospheric temperature response. While the aggregate effect of many moist transport events and seasons of sea-ice loss will be different than the response to hypothetical perturbations, our results nonetheless highlight the mechanisms that alter the Arctic temperature inversion in response to CO2 forcing. As sea ice declines, the atmosphere’s boundary-layer temperature structure is weakened, static stability decreases, and a thermodynamic coupling emerges between the Arctic surface and the overlying troposphere.

scholarly journals Large-scale features of Last Interglacial climate: results from evaluating the <i>lig127k</i> simulations for the Coupled Model Intercomparison Project (CMIP6)–Paleoclimate Modeling Intercomparison Project (PMIP4)

2021 ◽  
Vol 17 (1) ◽  
pp. 63-94 ◽  
Author(s):  
Bette L. Otto-Bliesner ◽  
Esther C. Brady ◽  
Anni Zhao ◽  
Chris M. Brierley ◽  
Yarrow Axford ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Northern Hemisphere ◽  
Coupled Model ◽  
The Arctic ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Last Interglacial ◽  
Paleoclimate Modeling ◽  
Intercomparison Project

Abstract. The modeling of paleoclimate, using physically based tools, is increasingly seen as a strong out-of-sample test of the models that are used for the projection of future climate changes. New to the Coupled Model Intercomparison Project (CMIP6) is the Tier 1 Last Interglacial experiment for 127 000 years ago (lig127k), designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models as for the future and following a common experimental protocol. Here we present a first analysis of a multi-model ensemble of 17 climate models, all of which have completed the CMIP6 DECK (Diagnostic, Evaluation and Characterization of Klima) experiments. The equilibrium climate sensitivity (ECS) of these models varies from 1.8 to 5.6 ∘C. The seasonal character of the insolation anomalies results in strong summer warming over the Northern Hemisphere continents in the lig127k ensemble as compared to the CMIP6 piControl and much-reduced minimum sea ice in the Arctic. The multi-model results indicate enhanced summer monsoonal precipitation in the Northern Hemisphere and reductions in the Southern Hemisphere. These responses are greater in the lig127k than the CMIP6 midHolocene simulations as expected from the larger insolation anomalies at 127 than 6 ka. New synthesis for surface temperature and precipitation, targeted for 127 ka, have been developed for comparison to the multi-model ensemble. The lig127k model ensemble and data reconstructions are in good agreement for summer temperature anomalies over Canada, Scandinavia, and the North Atlantic and for precipitation over the Northern Hemisphere continents. The model–data comparisons and mismatches point to further study of the sensitivity of the simulations to uncertainties in the boundary conditions and of the uncertainties and sparse coverage in current proxy reconstructions. The CMIP6–Paleoclimate Modeling Intercomparison Project (PMIP4) lig127k simulations, in combination with the proxy record, improve our confidence in future projections of monsoons, surface temperature, and Arctic sea ice, thus providing a key target for model evaluation and optimization.

Greenhouse-gas contribution to Arctic sea-ice loss

2021 ◽  
Author(s):  
Yeon-Hee Kim ◽  
Seung-Ki Min
Keyword(s):  
Sea Ice ◽  
Greenhouse Gas ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Detection Analysis ◽  
External Forcings ◽  
Arctic Sea ◽  
Intercomparison Project ◽  
Arctic Sea Ice Loss

&lt;p&gt;Arctic sea-ice area (ASIA) has been declining rapidly throughout the year during recent decades, but a formal quantification of greenhouse gas (GHG) contribution remains limited. This study conducts an attribution analysis of the observed ASIA changes from 1979 to 2017 by comparing three satellite observations with the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations using an optimal fingerprint method. The observed ASIA exhibits overall decreasing trends across all months with stronger trends in warm seasons. CMIP6 anthropogenic plus natural forcing (ALL) simulations and GHG-only forcing simulations successfully capture the observed temporal trend patterns. Results from detection analysis show that ALL signals are detected robustly for all calendar months for three observations. It is found that GHG signals are detectable in the observed ASIA decrease throughout the year, explaining most of the ASIA reduction, with a much weaker contribution by other external forcings. We additionally find that the Arctic Ocean will occur ice-free in September around the 2040s regardless of the emission scenario.&lt;/p&gt;

scholarly journals Intercomparison of temperature trends in IPCC CMIP5 simulations with observations, reanalyses and CMIP3 models

2013 ◽  
Vol 6 (5) ◽  
pp. 1705-1714 ◽  
Author(s):  
J. Xu ◽  
L. Zhao ◽  
Keyword(s):  
Coupled Model ◽  
Temperature Trend ◽  
The Arctic ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Model Simulations ◽  
Temperature Trends ◽  
Intercomparison Project ◽  
Stratospheric Cooling ◽  
The Tropics

Abstract. On the basis of the fifth Coupled Model Intercomparison Project (CMIP5) and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3) simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (radiosonde, reanalysis, CMIP3 and CMIP5), although similarities can be observed. Compared to the CMIP3 model simulations, the simulations in some of the CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR) exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the uncertainty in the Antarctic is the largest. In addition, note that the reanalyses show the largest uncertainty in the lower tropical stratosphere, and the CMIP3 simulations show the largest uncertainty in both the south and north polar regions.

Short-Lived Climate Forcers over the Arctic between 1995 and 2015 as simulated by the GISS modelE2.1

2020 ◽  
Author(s):  
Ulas Im ◽  
Kostas Tsigaridis ◽  
Cynthia H. Whaley ◽  
Gregory S. Faluvegi ◽  
Zbigniew Klimont ◽  
...  
Keyword(s):  
Coupled Model ◽  
The Arctic ◽  
Mixing State ◽  
State Matrix ◽  
Microphysics Scheme ◽  
Natural Emissions ◽  
Intercomparison Project ◽  
The One ◽  
Goddard Institute ◽  
Space Sciences

&lt;p&gt;The Arctic Monitoring and Assessment Programme (AMAP) is currently assessing the impacts of Short-Lived Climate Forcers (SLCF) on Arctic climate and air quality. In support of the assessment, we used the NASA Goddard Institute of Space Sciences (GISS) Earth System Model (modelE2.1), with prescribed sea surface temperature and sea-ice fraction, to simulate SLCF concentrations globally between 1995 and 2015. Two simulations were conducted, using the One-Moment Aerosol (OMA) and the Multiconfiguration Aerosol TRacker of mIXing state (MATRIX) aerosol modules. OMA is a mass-based scheme in which aerosols are assumed to remain externally mixed and have a prescribed and constant size distribution, while MATRIX is an aerosol microphysics scheme based on the quadrature method of moments, which is able to explicitly simulate the mixing state of aerosols. Anthropogenic emissions from the ECLIPSE v6b emissions database were used, along with emissions from aircrafts and open biomass burning from the Coupled Model Intercomparison Project Phase 6 (CMIP6), while the natural emissions of sea salt, DMS, isoprene and dust are calculated interactively. The simulated monthly surface concentrations of sulfate (SO&lt;sub&gt;4&lt;/sub&gt;), black carbon (BC), organic carbon (OA), and ozone (O&lt;sub&gt;3&lt;/sub&gt;) are compared with observations from a set of Arctic stations, extracted from the EBAS and IMPROVE databases, as well as a few additional locations. Simulated aerosol optical depths (AOD) are also compared with Advanced Very-High Resolution Radiometer (AVHRR). The study will present the evaluation of the modelE2.1 in simulating SLCF levels over the Arctic using different aerosol schemes, along with observed and simulated trends of SLCFs over the Arctic between 1995 and 2015.&lt;/p&gt;&lt;div&gt; &lt;div&gt; &lt;div&gt;&amp;#160;&lt;/div&gt; &lt;/div&gt; &lt;/div&gt;

Reply to “Comments on ‘Current GCMs' Unrealistic Negative Feedback in the Arctic'”

2013 ◽  
Vol 26 (19) ◽  
pp. 7789-7792
Author(s):  
Julien Boé ◽  
Alex Hall ◽  
Xin Qu
Keyword(s):  
Negative Feedback ◽  
Current Understanding ◽  
Lapse Rate ◽  
The Arctic ◽  
Rate Feedback

Abstract Pithan and Mauritsen argue that the 2009 results of Boé et al. are not consistent with current understanding of the lapse-rate feedback in the Arctic. They also argue that these results arise to an important extent from self-correlation issues. In this response, the authors argue that their results are not inconsistent with current understanding of lapse-rate feedback and demonstrate that the conclusions remain unchanged when all possibilities of self-correlation are excluded.

scholarly journals Recurrence Spectra of European Temperature in Historical Climate Simulations

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 166 ◽  
Author(s):  
M. Alvarez-Castro ◽  
Davide Faranda ◽  
Thomas Noël ◽  
Pascal Yiou
Keyword(s):  
Coupled Model ◽  
Temperature Extremes ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Climate Simulations ◽  
Ensemble Mean ◽  
Order Of Magnitude ◽  
Intercomparison Project ◽  
European Temperature Extremes ◽  
Cold Extremes

We analyse and quantify the recurrences of European temperature extremes using 32 historical simulations (1900–1999) of the fifth Coupled Model Intercomparison Project (CMIP5) and 8 historical simulations (1971–2005) from the EUROCORDEX experiment. We compare the former simulations to the 20th Century Reanalysis (20CRv2c) dataset to compute recurrence spectra of temperature in Europe. We find that, (1) the spectra obtained by the model ensemble mean are generally consistent with those of 20CR; (2) spectra biases have a strong regional dependence; (3) the resolution does not change the order of magnitude of spectral biases between models and reanalysis, (4) the spread in recurrence biases is larger for cold extremes. Our analysis of biases provides a new way of selecting a subset of the CMIP5 ensemble to obtain an optimal estimate of temperature recurrences for a range of time-scales.

scholarly journals Ozone sensitivity to varying greenhouse gases and ozone-depleting substances in CCMI-1 simulations

2018 ◽  
Vol 18 (2) ◽  
pp. 1091-1114 ◽  
Author(s):  
Olaf Morgenstern ◽  
Kane A. Stone ◽  
Robyn Schofield ◽  
Hideharu Akiyoshi ◽  
Yousuke Yamashita ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Climate Model ◽  
Coupled Model ◽  
Model Ensemble ◽  
Total Column Ozone ◽  
Ozone Sensitivity ◽  
Intercomparison Project ◽  
Ozone Depleting Substances ◽  
Column Ozone ◽  
Total Column

Abstract. Ozone fields simulated for the first phase of the Chemistry-Climate Model Initiative (CCMI-1) will be used as forcing data in the 6th Coupled Model Intercomparison Project. Here we assess, using reference and sensitivity simulations produced for CCMI-1, 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, and a combination of carbon dioxide and other greenhouse gases. 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. We analyse how stratospheric age of air, a commonly used diagnostic of stratospheric transport, responds to the forcings. For this diagnostic we find some salient differences in model behaviour, which may explain some of the findings for ozone. The findings imply that the ozone fields derived from CCMI-1 are subject to considerable uncertainties regarding the impacts of these anthropogenic forcings. We offer some thoughts on how to best approach the problem of generating a consensus ozone database from a multi-model ensemble such as CCMI-1.

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