Quantifying global climate feedbacks, responses and forcing under abrupt and gradual CO2 forcing

2013 ◽  
Vol 41 (9-10) ◽  
pp. 2471-2479 ◽  
Author(s):  
David J. Long ◽  
Matthew Collins
Keyword(s):  
Global Climate ◽  
Climate Feedbacks ◽  
Co2 Forcing

scholarly journals Time-Varying Climate Sensitivity from Regional Feedbacks

2013 ◽  
Vol 26 (13) ◽  
pp. 4518-4534 ◽  
Author(s):  
Kyle C. Armour ◽  
Cecilia M. Bitz ◽  
Gerard H. Roe
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Time Variation ◽  
Radiative Forcing ◽  
Global Climate ◽  
Regional Climate ◽  
Climate Feedback ◽  
Climate Feedbacks ◽  
Geographic Pattern ◽  
Surface Warming

Abstract The sensitivity of global climate with respect to forcing is generally described in terms of the global climate feedback—the global radiative response per degree of global annual mean surface temperature change. While the global climate feedback is often assumed to be constant, its value—diagnosed from global climate models—shows substantial time variation under transient warming. Here a reformulation of the global climate feedback in terms of its contributions from regional climate feedbacks is proposed, providing a clear physical insight into this behavior. Using (i) a state-of-the-art global climate model and (ii) a low-order energy balance model, it is shown that the global climate feedback is fundamentally linked to the geographic pattern of regional climate feedbacks and the geographic pattern of surface warming at any given time. Time variation of the global climate feedback arises naturally when the pattern of surface warming evolves, actuating feedbacks of different strengths in different regions. This result has substantial implications for the ability to constrain future climate changes from observations of past and present climate states. The regional climate feedbacks formulation also reveals fundamental biases in a widely used method for diagnosing climate sensitivity, feedbacks, and radiative forcing—the regression of the global top-of-atmosphere radiation flux on global surface temperature. Further, it suggests a clear mechanism for the “efficacies” of both ocean heat uptake and radiative forcing.

scholarly journals Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

2020 ◽  
Vol 17 (16) ◽  
pp. 4173-4222 ◽  
Author(s):  
Vivek K. Arora ◽  
Anna Katavouta ◽  
Richard G. Williams ◽  
Chris D. Jones ◽  
Victor Brovkin ◽  
...  
Keyword(s):  
Carbon Concentration ◽  
Global Climate ◽  
Coupled Model ◽  
Co2 Concentration ◽  
Climate Feedback ◽  
Cmip5 Models ◽  
Climate Feedbacks ◽  
Transient Climate Response ◽  
Order Of Magnitude ◽  
Intercomparison Project

Abstract. Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1 % yr−1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm−1) and ocean (0.79 ± 0.07 PgC ppm−1), while the carbon–climate feedback over land (−45.1 ± 50.6 PgC ∘C−1) is about 3 times larger than over ocean (−17.2 ± 5.0 PgC ∘C−1). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 ∘C EgC−1 and is similar to that found in CMIP5 models (1.63 ± 0.48 ∘C EgC−1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

Attributing the global increase in permafrost temperatures to human induced climate change

2021 ◽  
Author(s):  
Lukas Gudmundsson ◽  
Josefine Kirchner ◽  
Anne Gädeke ◽  
Eleanor Burke ◽  
Boris K. Biskaborn ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Volcanic Eruptions ◽  
Global Climate Models ◽  
Anthropogenic Climate Change ◽  
Permafrost Degradation ◽  
Climate Feedbacks ◽  
Permafrost Temperature

<p>Permafrost temperatures are increasing at the global scale, resulting in permafrost degradation. Besides substantial impacts on Arctic and Alpine hydrology and the stability of landscapes and infrastructure, permafrost degradation can trigger a large-scale release of carbon to the atmosphere with possible global climate feedbacks. Although increasing global air temperature is unanimously linked to human emissions into the atmosphere, the attribution of observed permafrost warming to anthropogenic climate change has so far mostly relied on anecdotal evidence. Here we apply a climate change detection and attribution approach to long permafrost temperature records from 15 boreholes located in the northern Hemisphere and simulated soil temperatures obtained from global climate models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We show that observed and simulated trends in permafrost temperature are only consistent if the effect of human emissions on the climate system is considered in the simulations. Moreover, the analysis also reveals that neither simulated pre-industrial climate variability nor the effects natural drivers of climate change (e.g. impacts of large volcanic eruptions) suffice to explain the observed trends. While these results are most significant for a global mean assessment, our analysis also reveals that simulated effects of anthropogenic climate change on permafrost temperature are also consistent with the observed record at the station scale. In summary, the quantitative combination of observed and simulated evidence supports the conclusion that anthropogenic climate change is the key driver of increasing permafrost temperatures with implications for carbon cycle-climate feedbacks at the planetary scale.</p>

scholarly journals ESD Reviews: mechanisms, evidence, and impacts of climate tipping elements

2020 ◽  
Author(s):  
Seaver Wang ◽  
Zeke Hausfather
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Large Scale ◽  
Global Climate ◽  
Climate Feedbacks ◽  
Positive Feedbacks ◽  
Climate Risks ◽  
Earth Systems ◽  
Current Century ◽  
Emissions Scenarios

Abstract. Increasing attention is focusing upon climate tipping elements – large-scale earth systems anticipated to respond through positive feedbacks to anthropogenic climate change by shifting towards new long-term states. In some but not all cases, such changes could produce additional greenhouse gas emissions or radiative forcing that could compound global warming. Developing greater understanding of tipping elements is important for predicting future climate risks. Here we review mechanisms, predictions, impacts, and knowledge gaps associated with ten notable climate tipping elements. We also evaluate which tipping elements are more imminent and whether shifts will likely manifest rapidly or over longer timescales. Some tipping elements are significant to future global climate and will likely affect major ecosystems, climate patterns, and/or carbon cycling within the current century. However, assessments under different emissions scenarios indicate a strong potential to reduce or avoid impacts associated with many tipping elements through climate change mitigation. Most tipping elements do not possess the potential for abrupt future change within years, and some tipping elements are perhaps more accurately termed climate feedbacks. Nevertheless, significant uncertainties remain associated with many tipping elements, highlighting an acute need for further research and modeling to better constrain risks.

scholarly journals Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme

2021 ◽  
Author(s):  
Alexandra Pongracz ◽  
David Wårlind ◽  
Paul A. Miller ◽  
Frans-Jan W. Parmentier
Keyword(s):  
Snow Cover ◽  
Large Scale ◽  
Global Climate ◽  
Single Layer ◽  
Physical Factor ◽  
Cold Season ◽  
The Arctic ◽  
Soil Conditions ◽  
Cold Bias ◽  
Climate Feedbacks

Abstract. The Arctic is warming rapidly, especially in winter, which is causing large-scale reductions in snow cover. Snow is one of the main controls on soil thermodynamics, and changes in its thickness and extent affect both permafrost thaw and soil biogeochemistry. Since soil respiration during the cold season potentially offsets carbon uptake during the growing season, it is essential to achieve a realistic simulation of the effect of snow cover on soil conditions to more accurately project the direction of arctic carbon-climate feedbacks under continued winter warming. The Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model has used – up until now – a single layer snow scheme, which underestimated the insulation effect of snow, leading to a cold bias in soil temperature. To address this shortcoming, we developed and integrated a dynamic, multi-layer snow scheme in LPJ-GUESS. The new snow scheme performs well in simulating the insulation of snow at hundreds of locations across Russia compared to observations. We show that improving this single physical factor enhanced simulations of permafrost extent compared to an advanced permafrost product. Besides soil thermodynamics, the new snow scheme resulted in increased winter respiration and an overall lower soil carbon content due to warmer soil conditions. The Dynamic scheme also influenced vegetation dynamics, resulting in an improved vegetation distribution and tundra-taiga boundary simulation. This study highlights the importance of a correct representation of snow in ecosystem models to project biogeochemical processes that govern climate feedbacks. The new dynamic snow scheme is an essential improvement in the simulation of cold season processes, which reduces the uncertainty of model projections. These developments contribute to a better understanding of the Arctic's role in the global climate system.

Long-term variability of shortwave absorption under abrupt-4xCO2 climate forcing and its visible and near-IR contributions

2020 ◽  
Author(s):  
Maria Z. Hakuba ◽  
Alejandro Bodas-Salcedo ◽  
Graeme Stephens
Keyword(s):  
Global Warming ◽  
Near Infrared ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Forcing ◽  
Shortwave Radiation ◽  
Hemispheric Differences ◽  
Climate Feedbacks

<p>While ongoing global warming is largely the result of reduced outgoing longwave radiation (OLR), climate feedbacks associated with changes in atmospheric water vapor and surface albedo are expected to enhance the absorption of shortwave radiation (ASR) and to sustain global warming on centennial time scales beyond the OLR modulations. These feedbacks as well as positive cloud feedbacks reduce the reflected shortwave (SW) flux at the top-of-atmosphere (TOA) and are a result of scattering and absorbing processes that differ by their near-infrared (NIR) and visible (VIS) contributions. Since direct measurements of broadband NIR (~0.7-5 mm) and VIS (~0.2-0.7 mm) radiation flux do not exist, we utilize UKESM1 simulations to study SW, NIR, and VIS climate feedbacks under preindustrial and abrupt-4xCO<sub>2</sub> climate forcing.</p><p>Besides its global long-term behavior, the spatial variability and key physical controls of ASR are not well characterized either. A prominent example is the unexplained hemispheric symmetry in planetary albedo that is consistently missed by current global climate models yielding unrealistic precipitation and circulation patterns. Although energetically equivalent, the observed hemispheric albedos differ spectrally, reflecting the uneven distribution of clouds and land masses. We use the same UKESM1 simulations to contrast inter-hemispheric differences in SW, NIR and VIS, and their relation to changes in clouds, the gaseous atmosphere and surface properties to shed light on processes relevant to the present-day symmetry, model biases, and potential future changes.</p>

scholarly journals Seasonal Variations of Climate Feedbacks in the NCAR CCSM3

2011 ◽  
Vol 24 (13) ◽  
pp. 3433-3444 ◽  
Author(s):  
Patrick C. Taylor ◽  
Robert G. Ellingson ◽  
Ming Cai
Keyword(s):  
Surface Temperature ◽  
Surface Albedo ◽  
Global Climate ◽  
Temperature Response ◽  
Lapse Rate ◽  
Climate Feedback ◽  
Climate Simulation ◽  
Climate Feedbacks ◽  
Climate System Model ◽  
Global Mean

Abstract This study investigates the annual cycle of radiative contributions to global climate feedbacks. A partial radiative perturbation (PRP) technique is used to diagnose monthly radiative perturbations at the top of atmosphere (TOA) due to CO2 forcing; surface temperature response; and water vapor, cloud, lapse rate, and surface albedo feedbacks using NCAR Community Climate System Model, version 3 (CCSM3) output from a Special Report on Emissions Scenarios (SRES) A1B emissions-scenario-forced climate simulation. The seasonal global mean longwave TOA radiative feedback was found to be minimal. However, the global mean shortwave (SW) TOA cloud and surface albedo radiative perturbations exhibit large seasonality. The largest contributions to the negative SW cloud feedback occur during summer in each hemisphere, marking the largest differences with previous results. Results suggest that intermodel spread in climate sensitivity may occur, partially from cloud and surface albedo feedback seasonality differences. Further, links between the climate feedback and surface temperature response seasonality are investigated, showing a strong relationship between the seasonal climate feedback distribution and the seasonal surface temperature response.

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