The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
Jonathan Gregory, from the Hadley Centre for Climate Prediction and Research, explained that surface albedo, water vapor, and cloud feedbacks are among the factors that affect climate sensitivity. It is useful to know how climate will respond in time to forcings that are themselves time dependent. Because of its large heat capacity and thermal inertia, the ocean plays a major role in determining the timing of the climate system’s response to exogenous forcing. Research into the climate system’s transient response therefore almost always involves an atmospheric model coupled to a model of ocean heat uptake.
Gregory described a very simple approach that he and his colleagues used to estimate a probability distribution for effective climate sensitivity of the real-world climate system (Gregory et al., 2002). It is based on the simple theory that during time-dependent climate change, the imbalance between imposed radiative forcing and radiative response is absorbed by the ocean, which contains most of the heat capacity of the system. Hence, F=Q-λΔT where F is the heat flux into the ocean, Q is the radiative forcing, and ΔT is the temperature change. One can solve the equation for the climate response parameter (λ), by estimating the other parameters as follows:
Ocean heat uptake (F) was estimated from the 5 year running means of observed interior ocean temperature changes (Levitus et al., 2000). This approach was used in lieu of relying on model estimates of ocean heat uptake, which usually involve some very uncertain assumptions. However, a model still has to be used to estimate F in the late nineteenth century, since there are no observations for that period.
It is not possible to obtain a true steady-state global average temperature, so instead, the temperature change (ΔT) is estimated as a difference in global average temperature between the current period and an earlier period, with the two periods as widely separated as possible, to maximize the climate change signal.
Radiative forcing (Q) for greenhouse gases, sulfate aerosols, solar irradiance, and volcanic aerosols is estimated by a variety of methods, since there are no direct observations of past changes in radiative forcing.
With these inputs, Gregory and colleagues calculated a PDF for sensitivity (Figure 3, pg. 29) and determined that the lower bound (5th percentile) is 1.6 K. This method of constraining the lower bound for sensitivity is objective and largely independent of climate models. Sensitivity could be further constrained if we could reduce uncertainties in the radiative forcing.
Gregory reviewed other approaches that have been used to develop estimates of climate sensitivity, including:
equilibrium “slab” experiments with atmospheric models coupled to mixed-layer ocean models;
time-dependent atmosphere-ocean climate model experiments;
simple climate models constrained by the twentieth century temperature record, which are used to develop PDF of climate sensitivity and forcing; and
paleoclimate records that are used as an observational constraint.
Reasons for differences among these various estimates include the fact that climate models have different feedbacks and sea surface temperature changes. Also, different studies employ different assumptions about natural and anthropogenic forcings, ocean heat uptake, and the “initial” (pre-industrial) state of the climate system.
Mahlman: If we had a perfect climate observing system, including aerosols, could you use these simple model approaches to better constrain sensitivity? How long would it take?
Schlesinger: It may take almost a century of observations to reduce the uncertainty, although you would learn faster if sensitivity is large (since the signal is larger).
Mahlman: It wouldn’t take another century if the measured climate variables and the climate forcings were well known. In such a case, all that would be left is global natural variability, which averages out over a few decades.
Stone: There are opportunities to reduce uncertainties in aerosol forcing through new observations.
Mahlman: However, we don’t even know how to observe the indirect effects of aerosols.
Ramaswamy: We also need to learn more about how to quantify direct radiative forcing from black carbon aerosols.