Values Of Climate Sensitivity Research Articles

Temperature reconstruction from the length fluctuations of small glaciers in the eastern Alps (northeastern Italy)

In this study, a linear model computing the air temperature fluctuations from the measured glacier snout fluctuations has been applied, for the first time, to three small glaciers in the western Tauern Alps (eastern Alps) in the period 1929–2011. The considered glaciers, with areas between 0.2 and $$1.3\,\hbox {km}^2$$ , are characterized by relevant time variations of their morphology, length and slope. The model requires the knowledge of two parameters: the glacier climate sensitivity $$C_s$$ and the glacier response time $$\tau$$ , both depending on the glacier morphological characteristics and on the precipitation. Applied to the glaciers assuming $$C_s$$ and $$\tau$$ as in the original formulation, it underestimates the temperature increase of $${\approx } 1.8\,^{\circ }\hbox {C}$$ derived over the whole period from the in situ data. Given the characteristics of these small glaciers, these parameters have been recalibrated by means of a non-linear least-square regression using an independent set of glaciers. Their mean value is of about 210 m/K and 3.8 years respectively. With the recalibrated values of the new glacier climate sensitivity $$C^*_s$$ and response time $$\tau ^*$$ , the temperature fluctuations derived by the model reproduce well those obtained from the observed temperatures computed over the hydrological year, with linear correlations between 0.8 and 0.9. The increase of the modeled mean temperature over the whole period fits in with that derived from observed temperature. Considering that the length fluctuations of these small glaciers affect significantly their slope and length, we tested the impact in the model of a time dependent formulation of $$C_s$$ and $$\tau$$ : the results indicate slight improvements both in the values of the correlation between the reconstructed and the observed temperature fluctuations and in the global temperature increase. Given the above value of climate sensitivity, the large retreat of the small alpine glaciers threatens their survival within a few decades, but the morphological changes in progress may counteract the glacier disappearance.

Adapting to Climate Change: An Analysis Under Uncertainty

Climate change is a phenomenon beset with major uncertainties and researchers should include them in Integrated Assessment Models. However, including further dimensions in IAM models comes at a cost. In particular, it makes most of these models suffer from the curse of dimensionality. In this study we benefit from a state-reduced framework to overcome those problems. In an attempt to advance in the modelling of adaptation within IAM models, we apply this methodology to shed some light on how the optimal balance between mitigation and adaptation changes under different stochastic scenarios. We find that stochastic technology growth hardly affects the optimal bundle of mitigation and adaptation whereas uncertainty about the value of climate sensitivity and the possibility of tipping points hitting the system change substantially the composition of the optimal mix as both persuade the risk-averse social planner to invest more in mitigation. Overall, we identify that including uncertainty into the model tends to favour (long-lasting) mitigation with respect to (instantaneous) adaptation. Further research should address the properties of the optimal mix when a stock of adaptation can be built.

Climate change impacts on extreme events in the United States: an uncertainty analysis

In this study, we analyze changes in extreme temperature and precipitation over the US in a 60-member ensemble simulation of the 21st century with the Massachusetts Institute of Technology (MIT) Integrated Global System Model–Community Atmosphere Model (IGSM-CAM). Four values of climate sensitivity, three emissions scenarios and five initial conditions are considered. The results show a general intensification and an increase in the frequency of extreme hot temperatures and extreme precipitation events over most of the US. Extreme cold temperatures are projected to decrease in intensity and frequency, especially over the northern parts of the US. This study displays a wide range of future changes in extreme events in the US, even simulated by a single climate model. Results clearly show that the choice of policy is the largest source of uncertainty in the magnitude of the changes. The impact of the climate sensitivity is largest for the unconstrained emissions scenario and the implementation of a stabilization scenario drastically reduces the changes in extremes, even for the highest climate sensitivity considered. Finally, simulations with different initial conditions show conspicuously different patterns and magnitudes of changes in extreme events, underlining the role of natural variability in projections of changes in extreme events.

Assessing the effects of ocean diffusivity and climate sensitivity on the rate of global climate change

ABSTRACTThe range in the projections of future climate warming can be attributed to the inherent uncertainty in the representation of climate model parameters and processes. In this study, we assess the effect of uncertainty in climate sensitivity and ocean heat uptake on the rate of future climate change. We apply a range of values for climate sensitivity and ocean diapycnal diffusivity in an ensemble of simulations using an intermediate-complexity climate model. We further use probability density functions to estimate the likelihood of each model outcome; using this framework, we calculate a range of likely rates of temperature change in response to a given future CO2 emissions scenario. From this analysis, the most probable maximum rate of temperature change lies between 0.3 and 0.5 °C/decade, with a most likely value of 0.36 °C/decade, which is more than twice the observed rate in the late twentieth century. We show that changes in ocean diffusivity have a significant effect on the rate of transient climate change for high values of climate sensitivity, while they have little influence when climate sensitivity is low. The highest rates of warming occur with high values of climate sensitivity and low values of ocean diffusivity. Such high rates of change could adversely affect the adaptive capacity of healthy functional ecosystems.

Climate Sensitivity Distributions Dependence on the Possibility that Models Share Biases

Abstract Uncertainty about biases common across models and about unknown and unmodeled feedbacks is important for the tails of temperature change distributions and thus for climate risk assessments. This paper develops a hierarchical Bayes framework that explicitly represents these and other sources of uncertainty. It then uses models’ estimates of albedo, carbon cycle, cloud, and water vapor–lapse rate feedbacks to generate posterior probability distributions for feedback strength and equilibrium temperature change. The posterior distributions are especially sensitive to prior beliefs about models’ shared structural biases: nonzero probability of shared bias moves some probability mass toward lower values for climate sensitivity even as it thickens the distribution’s positive tail. Obtaining additional models of these feedbacks would not constrain the posterior distributions as much as narrowing prior beliefs about shared biases or, potentially, obtaining feedback estimates having biases uncorrelated with those impacting climate models. Carbon dioxide concentrations may need to fall below current levels to maintain only a 10% chance of exceeding official 2°C limits on global average temperature change.

Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes

Abstract A climate model emulator is developed using neural network techniques and trained with the data from the multithousand-member climateprediction.net perturbed physics GCM ensemble. The method recreates nonlinear interactions between model parameters, allowing a simulation of a much larger ensemble that explores model parameter space more fully. The emulated ensemble is used to search for models closest to observations over a wide range of equilibrium response to greenhouse gas forcing. The relative discrepancies of these models from observations could be used to provide a constraint on climate sensitivity. The use of annual mean or seasonal differences on top-of-atmosphere radiative fluxes as an observational error metric results in the most clearly defined minimum in error as a function of sensitivity, with consistent but less well-defined results when using the seasonal cycles of surface temperature or total precipitation. The model parameter changes necessary to achieve different values of climate sensitivity while minimizing discrepancy from observation are also considered and compared with previous studies. This information is used to propose more efficient parameter sampling strategies for future ensembles.

Subjective elements in climate policy advice

Subjective elements are an inevitable component of scientific advice on climate policies. Good practice warrants that the level of assumption underlying subjective elements be parsimonious, that their effects on policy decisions be identified, and that policy relevant variables be communicated with appropriate levels of precision. In the case of climate sensitivity, the level of precision is intrinsically difficult to quantify. There is no ‘true’ value of climate sensitivity to be discovered. Rather, best practice consists of the application of multiple methods to estimate the quantity. Best practice provides confidence that some values of climate sensitivity are more likely than others. This lends support to the notion of weighting climate sensitivity values, though the appropriate precision appears to be less than that implied by use of a probability density function (pdf) and greater than that implied by use of a simple range. Use of both a pdf and a range in this case can provide information about likely outcomes and possible extremes.

Probabilities in climate policy advice: a critical comment

This essay explores as to whether probabilistic climate forecasting is consistent with the prerequisites of democratic scientific policy advice. It argues that, given the boundaries of our current knowledge, it is highly problematic to assign exact, unconditional probabilities to possible values of climate sensitivity. The range of possible–instead of probable–future climate scenarios is what climate policy should be based on.

Hotter Than Ever

“Climate sensitivity” is a parameter used by climatologists to specify the increase in average global surface temperature in degrees celsius as a consequence of doubling the concentration of atmospheric carbon dioxide. This value is difficult to calculate, though, because there are large uncertainties in the responses of clouds and water vapor to the resulting warming, and in how those responses would modify Earth's radiation balance. The Intergovernmental Panel on Climate Change range of likely values for climate sensitivity is 1.4 to 5.8°C, although the full range varies from 0.1 to 10.0°C, and the derivations of these estimates make it hard to assign probabilities.