by Judith Curry
So, if you increase the longwave radiative forcing from CO2, which of the following happens?
- heating of the near surface ground temperature
- heating of the atmosphere
- heating of the deep ground temperature
- the heat is lost to radiative emission from the skin surface.
Simple analyses of climate feedback and sensitivity (ΔT = λ * ΔF) assume #2. A new paper by McNider et al. argues for a more complex (and realistic) response.
McNider, R. T., G.J. Steeneveld, B. Holtslag, R. Pielke Sr, S. Mackaro, A. Pour Biazar, J. T. Walters, U. S. Nair, and J. R. Christy (2012). Response and sensitivity of the nocturnal boundary layer over land to added longwave radiative forcing, J. Geophys. Res.,doi:10.1029/2012JD017578, in press.
Abstract. One of the most significant signals in the thermometer-observed temperature record since 1900 is the decrease in the diurnal temperature range over land, largely due to rising of the minimum temperatures. Generally, climate models have not well replicated this change in diurnal temperature range. Thus, the cause for night-time warming in the observed temperatures has been attributed to a variety of external causes. We take an alternative approach to examine the role that the internal dynamics of the stable nocturnal boundary layer (SNBL) may play in affecting the response and sensitivity of minimum temperatures to added downward longwave forcing. As indicated by previous nonlinear analyses of a truncated two-layer equation system, the SNBL can be very sensitive to changes in greenhouse gas forcing, surface roughness, heat capacity, and wind speed. A new single-column model growing out of these nonlinear studies is used to examine the SNBL. Specifically, budget analyses of the model are provided that evaluate the response of the boundary layer to forcing and sensitivity to mixing formulations. Based on these model analyses, it is likely that part of the observed long-term increase in minimum temperature is reflecting a redistribution of heat by changes in turbulence and not by an accumulation of heat in the boundary layer. Because of the sensitivity of the shelter level temperature to parameters and forcing, especially to uncertain turbulence parameterization in the SNBL, there should be caution about the use of minimum temperatures as a diagnostic global warming metric in either observations or models.
[for the complete paper, click here]
The paper has been discussed extensively at WUWT. There is a guest post by Richard McNider at Pielke Sr’s blog, that raises some broader implications of the findings, some excerpts:
Our budget calculations in the paper also showed that the ultimate fate of the added input of longwave energy was highly sensitive to boundary layer parameters and turbulent parameterizations. In our simple model, the added radiation could go to heating the atmosphere, heating the near surface ground temperature, heating the deep ground temperature or lost to radiative emission from the skin surface. The model showed that at light winds (with weak turbulence) the atmosphere was not able to effectively lift this energy off the surface and into the atmosphere. Thus, more radiation was emitted from the surface. If soil conductivity and /or heat capacity were large then more of the energy would go to heating the ground. When we tested boundary layer parameterizations of the type employed in large scale models, we found they generally added much more sensible heat to the atmosphere as opposed to being lost by radiation or to the ground.
To capture the type sensitivity we found in our model, climate models would need very fine vertical high resolution and also stable boundary layer parameterizations that don’t have large background mixing such as is often added to large scale models with coarse resolution. Our paper also showed that the stable nocturnal boundary layer was very sensitive to the turbulent parameterization and surface characteristics such as roughness and land surface heat capacity and conductivity. In fact because current coarse resolution global models do not capture the asymmetry in warming in minimum temperatures and likely do not represent the stable boundary layer very well, we further suggested that truthful replication of the night-time warming may be out of the reach of current models. Thus, it may be better for current climate models, when they test replication of past climates and to project future global warming, to only use maximum temperatures rather than the current metric of using the mean daily temperature, which contains the minimum temperature. Of course, changes in night-time temperatures represent real changes and possible impacts to the climate system (e.g., melting ice), to society (agricultural productivity) and to ecosystems. Thus, ultimately we need to develop climate models that do have the resolution and sensitivity to capture changes in minimum temperatures.
I would like to now editorialize on the implications of this work which were not explicitly stated in the peer reviewed paper. While the asymmetrical warming of the nighttime temperatures and the lack of fidelity of models in capturing the asymmetry that we discuss has also been the subject of other papers, it seems that no one has looked at the implications of this to the general ability of models to forecast climate change. But, consider the following as a thought experiment. Model credibility in the IPCC has been based on the ability to replicate the last 130 years of the global instrumental temperature record with anthropogenic forcing. But, remember that the global temperature record in such comparisons is based on the daily Tmean (the average of Tmax and Tmin). If models are replicating Tmean but are not capturing the trend in Tmin, then this must mean that the model Tmax is warming faster than the actual Tmax. Also, if most of the warming in the instrumental record is warming in the nighttime boundary then by its very nature this is warming of a very thin layer of order 200m or so. In fact, if our results are correct, we show that it is only the lowest part of the nighttime boundary layer that is being warmed or a thin layer than of no more 20-50 meters. Maximum temperature observations made in daytime boundary layers which are 1- 2 km in depth, reflect a measure of a much deeper layer temperature. Thus, the instrumental observational data when viewed in light of boundary layer theory is showing that most of the warming is occurring in a very thin layer and the deeper atmosphere as captured by Tmax is not warming as much as models.
However, one of the largest positive feedbacks in climate simulations is the accumulation of additional water vapor as the deep atmosphere warms and this adds an additional greenhouse effect. In fact, the added water vapor effect depends on a deep layer of added water vapor. If the deep atmosphere is not warming then this water vapor feedback will not be nearly as strong. Thus, models may be overstating the water vapor feedback.
In regards to the oceans (since I started my career as an ocean modeler), I think we should also be careful about similar turbulent processes connecting the atmosphere and ocean surface. Just as for the land surface, the ultimate fate of added energy may be tied to the details of how efficiently and quickly turbulence in the atmosphere and the ocean can remove this added energy from the skin surface. Any errors in this near surface turbulence will impact the fate of the added energy. I am not certain at all that coupled ocean-atmospheric models get these details right.
JC comment: This paper really clicked with me, it addresses a big issue that I have long been worried about and raised on the earlier thread CO2 no feedback sensitivity with regards to the equation (ΔT = λ * ΔF):
According to this simple model that relates radiative forcing at the tropopause to a surface temperature change, there is an equilibrium relationship between these two variables. The physical relationship between these two variables requires many many assumptions, including zero heat capacity of the surface and a convective link between the surface and the tropopause.
McNider et al. remind us of why these are poor assumptions.