In the Dark of the Night – the Problem with the Diurnal Temperature Range and Climate Change by Richard T. McNider

by Richard T. McNider
Distinguished Professor of Science
Department of Atmospheric Science
University of Alabama in Huntsville

This is a short essay on the role of nighttime temperatures in climate change from a boundary layer perspective spurred by a paper we recently had published in Geophysical Research Letters(GRL) –

Walters, J. T., R. T. McNider, X. Shi, W. B Norris, and J. R. Christy (2007): Positive surface temperature feedback in the stable nocturnal boundary layer, Geophys. Res. Lett., 34, L12709, doi:10.1029/2007GL029505

The techniques of nonlinear analysis are used to examine the behavior of the stable nocturnal boundary layer (SNBL) when it is subjected to changes in incoming radiation or in surface characteristics. A single-column model and nonlinear bifurcation techniques are used to demonstrate that any atmospheric forcing, such as weak radiative forcing (4-6 W m-2) from greenhouse gases or cloud cover, can trigger a potentially significant positive feedback. Multiple solutions occur in some parameter spaces. This analysis shows that any forcing that decreases the stability, whether by increasing greenhouse gases or surface heat capacity, can cause large increases in surface temperature as the SNBL shifts from a weak turbulent regime, which allows the surface to cool, to a turbulent regime, which mixes warm air from aloft. Positive feedback may be a key factor in interpreting the long-term observed nocturnal warming trend in the SNBL.

The essay ends with a plea to discard nighttime temperatures as a means to track heat accumulation in the atmosphere from greenhouse gases or other positive radiative forcing.

The essence of concern in greenhouse gas climate change is that heat will be trapped and accumulate in the earth system, thus altering the climate and harming ecosystems. Climate change alarmists have used the global surface temperature record as evidence of man’s impact on climate being “realâ€? yet, as discussed below, most of the warming in this record is at night and probably has little to do with the accumulation of heat in the atmosphere. Climate modelers (or their bosses) extol how well climate models replicate the past climate when greenhouse gases are included, yet they largely miss the biggest signal in the surface temperature record – the change in the diurnal temperature range.

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 warming of the minimum temperatures [Karl et al. 1993]. In a recent paper [Vose et al. 2005] it was reported that since 1980 this differential warming has been less. However, the number of stations used and their coverage is small – largely in the U.S. and Europe. Examination of specific areas using many more stations such as in East Africa have shown large differences continue with nighttime temperatures warming with much less warming in maximums.

The cause for this nighttime warming in the observed temperatures has been the subject of much debate. Investigators have attributed the change to a variety of causes, including increases in atmospheric water vapor, cloud cover, jet contrails, and changes in surface characteristics, such as land cover and land use. [e.g. Dai et al., 1999; Durre and Wallace, 2001]. Advocates of greenhouse gas theory have always been more than willing to accept this warming as part of climate change caused by man induced increases greenhouse gas emissions. But they haven’t been as keen to question whether this nighttime warming is in fact really a measure of the accumulation of heat in the atmosphere. Some papers, such as Dai et al. 1999, while positing that the warming is due to an increase in cloudiness, seem to accept the cloud-cover change as part of the rubric of greenhouse gas climate change, even though this is not a clear climate change attribute in models.

Climate models have in general not replicated the change in diurnal temperature range well. While they do get a slight differential warming, it is generally less than 30% of that observed [e.g., Stone and Weaver 2003]. In fact, in terms of model validation, most modelers have been concerned with replicating the global mean temperature (most global data sets don’t track minimum and maximums) which is known a priori. Thus, it is not surprising that the models do well with this metric. However, climate models generally fail to replicate the diurnal temperature trend, which is largely an a posteriori comparison.

Here we would like to try to distinguish between warming in the nocturnal boundary layer due to a redistribution of heat and warming due to the accumulation of heat. The temperature at night at shelter height is a result of competition between thermal stability and mechanical shear. If stability wins then turbulence is suppressed and the cooling surface becomes cutoff from the warmer air aloft, which leads to sharp decay in surface air temperature. The end result by morning is a very shallow layer with temperatures 25-35°F cooler than the previous day’s maximum temperature. On the other hand, if shear wins, then turbulence is maintained and warmer air from aloft is continually mixed to the surface, which leads to significantly lower cooling rates and warmer temperatures. Thus, warming occurs due to a redistribution of heat.

Temperatures in the nocturnal boundary layer are strongly dependent on the state of turbulence. Any study of temperature trends in minimum temperatures must be cognizant of this boundary layer behavior since nighttime temperatures can be warmer due to a redistribution of heat by turbulence rather than the accumulation of more heat in the atmosphere.

From a mathematical perspective, the nocturnal boundary layer is a complex dynamical system. It likely supports multiple solutions and is certainly sensitively dependent on atmospheric parameters, such as the rate of radiative cooling and wind speed, and to surface characteristics, such as roughness and heat capacity [e.g., McNider et al. 1995, Van de Weil 2002].

Our recent GRL paper explored the sensitivity of the nocturnal boundary to greenhouse gas forcing. We employed techniques of non-linear dynamics to develop bifurcation diagrams to determine how surface air temperatures might respond to changes in downward longwave radiation. We found that there are three distinct regimes. For low geostrophic wind speeds the surface temperature warms at a linear rate as downward longwave radiation is increased (~0.14°C/W). In this case (where stability wins the competition) the extra longwave heating is confined to a shallow layer. For the high wind speed case the surface air temperatures are relatively insensitive to increased downward radiation since any heating is distributed over a much deeper layer of the atmosphere by turbulence.

The most interesting regime, however, is the intermediate wind speed case. Here, even slight increases in downward radiation can tilt the outcome of the competition between stability and shear, so that shear becomes the winner. This causes warmer air to be mixed down from aloft so that just a few watts of added downward radiation can increase the surface temperature by up to 7-9°C. This is analogous to positive feedbacks in climate theory such as water vapor feedback or albedo feedback in which increases in greenhouse downward radiation can cause even larger increases in warming because other effects are triggered. Note that the present turbulence caused positive feedback only produces a redistribution of heat. The turbulence experiments were not in the context of a climate simulation with an increase in heat content of the atmosphere included. Depending on how often the climate is within this sensitive parameter space, there could be significant impacts on minimum surface temperature trends.

This positive temperature feedback due to triggered turbulence may in fact be part of the reason why minimum temperatures have risen more than daytime temperatures. It also may explain why climate models have not found similar warming. Climate models, with their course resolution, do not capture the type of sharp transitions discussed above as the nocturnal boundary layer transitions from a non-turbulent to a turbulent regime. Such processes are not well represented in the stable nocturnal boundary layers of climate models because ratios of stability and shear in turbulent kinetic energy models or Richardson numbers that control model mixing are direct functions of the vertical grid increment, Δz. As stated in our GRL paper –

“The coarse resolution in GCMs does not allow the direct application of the principles of turbulent kinetic energy closures. Steeneveld et al. [2006] showed that fine resolution is required to capture the behavior of the SNBL. When a large-scale model such as a GCM or even a mesoscale model attempts to run with first-principle closures, modelers must cope with its tendency to crash to a very cold solution (Viterbo et al. [1999], Derbyshire [1999]). Thus, modelers generally add processes to produce mixing, such as altering profile functions [Viterbo et al.,1999], increasing roughness or making the critical Richardson number dependent on grid spacing [McNider and Pielke, 1981]. These fixes generally damp the type of sensitive transition represented in the equations.â€?

While our GRL paper emphasized the sensitivity of the nocturnal temperatures to greenhouse-gas forcing, the winner of the stability and shear contest is perhaps more sensitive to changes in surface roughness, cloudiness, surface heat capacity (including soil moisture), etc. Thus, if we use minimum temperatures to track climate change, we unfortunately have a measurement that is extremely sensitive to changes in land use such as urbanization or irrigation or station movements (see Runnals and Oke 2006).

Most importantly, these changes in minimum temperature, whether caused by additional greenhouse forcing (as in our GRL paper) or land use changes or other land surface dynamics, are reflecting a redistribution of heat by turbulence—not an accumulation of heat. Further, the minimum temperatures measured in the nocturnal boundary layer represent only a very shallow layer of the atmosphere which is usually only a few hundred meters thick.

Because of the redistribution phenomena and the shallow layer affected, observed minimum temperatures are a very poor measure of the accumulation of heat in the atmosphere. Also, since climate models with their course resolution cannot accurately simulate the physics important to heat redistribution, their minimum temperatures are suspect. On the other hand, surface maximum temperatures would seem to represent a more robust measure of the heat content of the atmosphere since daytime boundary layers connect the surface to a depth of one to two kilometers or more and are well mixed. Climate models more accurately simulate daytime mixing which, by its non-local nature, is not as dependent on grid resolution. Greenhouse gas theory says warming should be largest in the middle to upper troposphere. Thus, daytime maximum temperatures should be much better for detecting and simulating this heat accumulation in the atmosphere.

What would be the implications of using only maximum temperatures to detect greenhouse-gas heat accumulation? First, since nighttime temperatures have risen almost twice as much as daytime temperatures over land since 1900, it would significantly reduce the rate of warming that has concerned greenhouse gas alarmists (and certainly a larger impact on our perceived climate than would arise from an implementation of a Kyoto type protocol). Second, if models have replicated the mean temperature and yet underestimated the nighttime warming this means their maximum temperature trends have been larger than observed trends in maximum temperatures. Thus, it potentially represents a factor of two or four problem for climate models validation if they were only to be evaluated against maximum temperatures.

In summary, it is my hope that more boundary layer scientists will jump into the fray of debate on the science of climate change. The GEWEX GABLS program has been a start but more attention is needed. Until we further our understanding of how boundary layer processes, in the context of climate and land-use change impact minimum temperature trends, we cannot trust our interpretation of trends. Further, until we equip climate models to capture the sensitive dynamics of the nocturnal boundary layer, we cannot use these models to accurately represent minimum temperature trends or fully understand climate sensitivity. Thus, if policymakers insist on constructing a global average near-surface air temperature trend as a diagnostic of greenhouse gas climate change, I encourage them to use only daily maximum surface temperatures.

Dai, A., K. E. Trenberth, and T. R. Karl (1999), Effects of clouds, soil moisture, precipitation and water vapor on diurnal temperature range, J. Climate, 12, 2451-2473.

Derbyshire, S. H. (1999), Boundary-layer decoupling over cold surfaces as a physical boundary instability, Boundary-layer Meteorology, 90, 297-325.

Durre, I. and J. Wallace (2001), Factors influencing the cold season diurnal temperature range in the United States, J. Climate, 14, 3263-3278.

IPCC 2001: International Panel on Climate Change (IPCC) Third Assessment Report – Climate Change 2001 Watson, R.T. and Writing Team (Eds.) IPCC, Geneva, Switzerland. pp 184

Karl, T. R., P. D. Jones, R. W. Knight, G. Kukla, N. Plummer, V. Razvayev, K. P. Gallo, J. Lindseay, J. R. J. Charlson, T. C. Peterson (1993), Asymmetric trends in surface temperature, Bull. Am. Meteorol. Soc., 74, 1007-1023.

McNider, R. T. and R. A. Pielke, (1981), Diurnal boundary layer development over sloping terrain, J. Atmos. Sci., 38, 2198-2212.

Runnals, K.E, and T.R. Oke( 2006), A technique to detect microclimatic inhomogeneities in Historical records of screen-level air temperature. J. Climate, 19, No. 6, pp. 959–978

Steeneveld, G. J., B. J. H. van de Wiel, and A. A. M. Holtslag (2006), Modeling the evolution of the atmospheric boundary layer coupled to the land surface for three contrasting nights in CASES-99, J. Atmos. Sci., 63, 920–935.

Stone, D. A. and A. J. Weaver (2003), Factors contributing to diurnal temperature range trends in the twentieth and twenty first century simulations of the CCAma coupled model, Climate Dynamics, 20, 435-445.

Van de Wiel, B. J. H., R. J. Ronda, A. F. Moene, H. A. R. De Bruin, and A. A. M. Holtslag (2002), Intermittent turbulence and oscillations in the stable boundary layer over land. Part I: A bulk model, J. Atmos. Sci., 59, 942-958.

Viterbo, Pedro, A. Beljaars, J. F. Mauhoff, and J. Teixeira (1999), The representation of soil moisture freezing and its impact on the SBL, Quart. J. Roy. Met. Soc., 125, 2401-2426.

Vose, R. S., D. R. Easterling, and B. Gleason (2005), Maximum and minimum temperature trends for the globe: An update through 2004, Geophys. Res. Lett., 32, L23822

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