observed temperature change

We present revised probability density functions for climate model parameters (effective climate sensitivity, the rate of deep-ocean heat uptake, and the strength of the net aerosol forcing) that are based on climate change observations from the 20th century. First, we compare observed changes in surface, upper-air, and deep-ocean temperature changes against simulations of 20th century climate in which the climate model parameters were systematically varied. The estimated 90 % range of effective climate sensitivity is 2–5 K but no corresponding upper bound can be placed on the equilibrium climate sensitivity. The net aerosol forcing strength for the 1980s has 90 % bounds of −0.70 to −0.27 W m−2. The rate of deep-ocean heat uptake corresponds to an effective diffusivity, Kv, with a 90 % range of 0.04–4.1 cm2 s−1. Second, we estimate the effective climate sensitivity and rate of deep-ocean heat uptake for 11 of the IPCC AR4 AOGCMs. By comparing against the acceptable combinations inferred from the observations, we conclude that the rates of deep-ocean heat uptake for the majority of AOGCMs lie above the observationally based median value. This implies a bias in the predictions inferred from the IPCC models alone. 1.

[1] Schwartz [2007] (hereinafter referred to as SES) recently suggested a method to calculate equilibrium climate sensitivity (the equilibrium global surface warming for a doubling of the atmospheric CO2 concentration), the

[1] Positive carbon-cycle feedbacks have the potential to reduce natural carbon uptake and accelerate future climate change. In this paper, we introduce a novel approach to incorporating carbon-cycle feedbacks into probabilistic assessments of future warming. Using a coupled climatecarbon model, we show that including carbon-cycle feedbacks leads to large increases in extreme warming probabilities. For example, for a scenario of CO2 stabilization at 550 ppm, the probability of exceeding 2°C warming at 2100 increased by a factor of between 1.7 and 3, while the probability of exceeding 3°C warming increased from a few percent to as much as 22%. CO2 fertilization was found to exert little influence on the amount of future warming, since increased carbon uptake was partially offset by fertilization-induced surface albedo changes. The effect of positive carbon-cycle feedbacks on the likelihood of extreme future warming must be incorporated into climate policy-related decision making. Citation: Matthews, H. D., and D. W. Keith (2007), Carbon-cycle feedbacks increase the likelihood of a warmer future, Geophys. Res. Lett., 34, L09702, doi:10.1029/2006GL028685. 1.

The framework of feedback analysis is used to explore the controls on the shape of the probability distribution of global mean surface temperature response to climate forcing. It is shown that ocean heat uptake, which delays and damps the temperature rise, can be represented as a transient negative feedback. This transient negative feedback causes the transient climate change to have a nar-rower probability distribution than that of the equilibrium climate response (the climate sensitivity). In this sense, climate change is much more predictable than climate sensitivity. The width of the distribution grows gradually over time, a consequence of which is that the larger the climate change being contemplated, the greater the uncertainty is about when that change will be realized. Another consequence of this slow growth is that further efforts to constrain climate sensi-tivity will be of very limited value for climate projections on societally-relevant time scales. Finally, it is demonstrated that the effect on climate predictability of reducing uncertainty in the atmospheric feedbacks is greater than the effect of reducing uncertainty in ocean feedbacks by the same proportion. However, at least at the global scale, the total impact of uncertainty in climate feedbacks is dwarfed by the impact of uncertainty in climate forcing, which in turn is contingent on choices made about future anthropogenic emissions. 2 1

A persistent feature of empirical climate sensitivity estimates is their heavy tailed probability distribution indicating a sizeable probability of high sensitivities. Previous studies make general claims that this upper heavy tail is an unavoidable feature of (i) the Earth system, or of (ii) limitations in our observational capabilities. Here we show that reducing the uncertainty about (i) oceanic heat uptake and (ii) aerosol climate forcing can — in principle — cut off this heavy upper tail of climate sensitivity estimates. Observations of oceanic heat uptake result in a negatively correlated joint likelihood function of climate sensitivity and ocean vertical diffusivity. This correlation is opposite to the positive correlation resulting from observations of surface air temperatures. As a result, the two observational constraints can rule out complementary regions in the climate sensitivity-vertical diffusivity space, and cut off the heavy upper tail of the marginal climate sensitivity estimate. 1.

research with independent policy analysis to provide a solid foundation for the public and private decisions needed to mitigate and adapt to unavoidable global environmental changes. Being data-driven, the Program uses extensive Earth system and economic data and models to produce quantitative analysis and predictions of the risks of climate change and the challenges of limiting human influence on the environment—essential knowledge for the international dialogue toward a global response to climate change. To this end, the Program brings together an interdisciplinary group from two established MIT research centers: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers—along with collaborators from the Marine Biology Laboratory (MBL) at Woods Hole and short- and long-term visitors—provide the united vision needed to solve global challenges. At the heart of much of the Program’s work lies MIT’s Integrated Global System Model. Through this integrated model, the Program seeks to: discover new interactions among natural and human climate system components; objectively assess uncertainty in economic and climate projections; critically and quantitatively analyze environmental management and policy proposals; understand complex connections among the many forces that will shape our future; and improve methods to model, monitor and verify greenhouse gas emissions and climatic impacts. This reprint is one of a series intended to communicate research results and improve public understanding of global environment and energy challenges, thereby contributing to informed debate about climate change and the economic and social implications of policy alternatives.

Nonlinear regression is a useful statistical tool, relating observed data and a nonlinear function of unknown parameters. When the parameter-dependent nonlinear function is computationally intensive, a straightforward regression analysis by maximum likelihood is not feasible. The method presented in this paper proposes to construct a faster running surrogate for such a computationally intensive nonlinear function, and to use it in a related nonlinear statistical model that accounts for the uncertainty associated with this surrogate. A pivotal quantity in the Earth’s climate system is the climate sensitivity: the change in global temperature due to doubling of atmospheric CO2 concentrations. This, along with other climate parameters, are estimated by applying the statistical method developed in this paper, where the computationally intensive nonlinear function is the MIT 2D climate model. 1. Introduction. A fundamental question in understanding the Earth’s

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[1] Regional multi-model ensembles are used to both increase the spatial resolution of the global simulations and to palliate uncertainties arising from different parameter-izations and dynamical cores. Here, we present the simulated current (1960–1990) and future (2070–2100) precipitation climatologies using eight Regional Climate Models (RCM) over Europe for an increased greenhouse gases scenario. Analysis of the current climate simulations in terms of the Probability Distribution Functions (PDF) shows noticeable regional differences in the type of precipitation which are in agreement with known precipitation climatologies. For future climate we observe an overall decrease of mean precipitation in most of the Mediterranean regions. A rise in high monthly precipitation amounts appear for all the regions, except for the Iberian peninsula and the Alps. As our analysis embed both spatial and temporal uncertainties in the modeling, our results provide further evidence of a variety of regional patterns within Europe under an increased greenhouse emissions