The slogan “Think Global, Act Local” began with multinational corporations realizing that national and regional markets around the world had distinct needs and preferences requiring accommodations. As the name implies it refers to the corporate strategy by which a global viewpoint is adopted in terms of formulating company vision, long-term aims and objectives and devising effective programs to achieve these aims and objectives, however, adaptations are made in each market according to the culture and specifications of any specific market.
Environmental activists took the notion on board during the first wave becoming aware of globalization. Early bearers of the catchphrase were for the most part supporters of an environmental movement that supported individual activism. The theory behind the saying was that in order to make large-scale global movements stick, the responsibility lay on individuals to carry out progressive practices – like environmental stewardship – in their own homes. The globe had become the new frame of reference for some far-thinking activists.
Clearly “climate change” activism operates in this mode. But as we shall see in this essay, the top-down, Global-Local approach to understanding climate and weather leads to distortions and misconceptions. In fact, climate science itself is best served by observing and establishing principles from the bottom up.
It turns out that the climate system is one of those things where averages do not tell very much, and can be misleading. For example:
Look at precipitation around the world
About 1 meter a year is the nominal average of all rain over all surfaces. Some places get up to 10 meters of rain (about 400 inches ) and others get near none. 47% of the earth is considered dryland, defined as anyplace where the rate of evaporation/transpiration exceeds the rate of precipitation. A desert is defined as a dryland with less than 25 cm of precipitation. In the image above, polar deserts are remarkably defined. It just does not have much hope of precipitation as there is little heat to move the water. More heat in, more water movement. Less heat in, less water movement.
Then there’s the seasonal patterns. The band of maximum rains moves with the sun: More north in June, more south in December. More sun, more heating, more rain. Movement in sync with the sun, little time delay. Equatorial max solar heat has max rains. Polar zones minimal heating, minimal precipitation. It’s a very tightly coupled system with low time lags.
Then Look at Temperatures
- latitudinal change
- N.H. – difference between land and ocean
- U.S. west coast (upwelling and cold current) v.s. U.S. east coast (gulf stream)
- influence of gulf stream in north Atlantic
- highest temps are in the subtropical N.H. desert regions
- west coast of S. America is cool while the east coast is warm, due to the ocean currents
- much less variability in the zonal direction in the S. H.
And look at clouds and their radiative effects
The large opacity of cloud increases the optical depth of the atmosphere, introducing warming in the LW energy budget. This warming varies with cloud-top temperature and height. For SW radiation, the high reflectivity of cloud decreases the incoming solar flux, favoring reduced surface temperature. Cloud routinely covers about 50% of the Earth. It accounts for about half of the Earth’s albedo (eg, for ∼0.15). Source: Salby 2012 pg315
Globally averaged values of CLW and CSW are about 30 and −45 W m−2, respectively. Net cloud forcing is then −15 W m−2. It represents radiative cooling of the Earth-atmosphere system. This is four times as great as the additional warming of the Earth’s surface that would be introduced by a doubling of CO2.
But clearly during Northern winter (diagrams above), that net cooling occurs largely over the Southern Ocean around Antarctica.
What happens when you average all this diversity?
We have all seen graphs showing how climate models project unrealistic global mean temperatures higher than those measured by stations or satellites. But Dr. Salby in his textbook points to a more fundamental failing of the climate simulations. By construction they balance global average energy budgets, but regional realities are grossly distorted.
Figure 8.4. Root-mean-square (RMS) model error, as a function of latitude, in simulation of (a) outgoing SW radiation reflected to space and (b) outgoing LW radiation. The RMS error is calculated over all longitudes and over all 12 months of a climatology formed from several years of data. . .The Earth Radiation Budget Experiment (ERBE; Barkstrom et al., 1989) observational estimates used here are for the period 1985 to 1989 from satellite-based radiometers, and the model results are for the same period in the 20th-century simulations in the MMD at PCMDI.
Symbolizing the local energy budget is net radiation (Fig. 1.34c), which represents the local imbalance between the SW and LW fluxes F0 and F ↑(0) in the TOA energy budget (8.82). Local values of those fluxes have been measured around the Earth by the three satellites of ERBE. The observed fluxes, averaged over time, have then been compared against coincident fluxes from climate simulations, likewise averaged. Figure 8.34 plots, for several GCMs, the rms error in simulated fluxes, which have been referenced against those observed by ERBE.
Values represent the regional error in the (time-mean) TOA energy budget. The error in reflected SW flux, Fs 4 − F0 in the global mean (8.82), is of order 20 Wm−2 (Fig. 8.34a). Such error prevails at most latitudes. Differences in error between models (an indication of intermodel discrepancies) are almost as large, 10–20 Wm−2. The picture is much the same for outgoing LW flux (Fig. 8.34b). For F ↑(0), the rms error is of order 10–15 Wm−2. It is larger for all models in the tropics, where the error exceeds 20 Wm−2. . .Consequently, the simulated change introduced by increased CO2 (2–4 Wm−2), even inclusive of feedback, is overshadowed by error in the simulated change of major absorbers.
By construction, GCMs achieve global-mean energy balance. How faithfully the energy budget is represented locally, however, is another matter. The local energy budget forces regional climate, along with the gamut of weather phenomena that derive from it. This driver of regional conditions is determined internally – through the simulation of local heat flux, water vapor, and cloud. (My bold)
A common expression is: “The devil is in the details.” When it comes to climate, it is truer to say that we humans are bedeviled (thwarted) by nature’s details refusing to fit into our global generalities. The proper role of science is to investigate those details and revise our mental constructs.
“Global Climate” is an oxymoron.
(oxymoron: A figure of speech in which two words with opposing meanings are used together intentionally for effect; IOW a contradiction in terms. From the Greek: pointed foolishness).
Climates are plural, not global, as the Koppen system makes clear. There are hundreds of regional climate zones defined empirically by temperature and precipitation patterns. And the observational data shows those zones are highly stable; that is, fears of climate change rarely appear in any actual climates showing shifting boundaries. See post: Data vs. Models 4: Climates Changing
Thus, we are better advised to:
Think Local Climates,
Prepare Local Adaptations
for the range of future weather consequences.
Off topic but from Neven’s blog today.
Is this the same commentator who rubbished MAISIE some months ago.
Perhaps you could put this by his statements back then for a little harmless fun.
“The good thing about MASIE is that it’s one of the most precise data sets out there as it is based on the IMS sea ice product, which is updated daily by analysts from the National Ice Center – people, not automated scripts – who look at a plethora of satellite imagery to determine where the exact ice edge is. The downside of that is that there’s a subjective component (different analysts) and changes in available satellite data over time. That’s why in principle MASIE isn’t deemed consistent enough for long-term trends.”
Thanks angech for the heads up. Neven has moved a long way toward my position on MASIE. But he has yet to acknowledge that NSIDC decided the MASIE dataset is consistent since 2006, and it was suitable enough for Dr. Meier to publish a study in 2015 analyzing it in comparison with his own SII. Suspect Neven and friends don’t like MASIE showing more ice than SII, a pattern which continues now that SII is back on line.
Formerly climatology was regional, as defined by Koppen and others, notably Trewartha.
The paper by Belda et Al (2014) is probably the best to date in reconstructing the Koppen-Trewartha climate classification map from modern datasets.
Belda confirms what H.H. Lamb said about climate chant between the beginning and end of the 20th century: there was not much change. Lamb wrote, “In fact, from about the beginning of this century up to 1940 a substantial climatic change was in progress, but it was in a direction which tended to make life easier and to reduce stresses for most activities and most people in most parts of the world. Average temperatures were rising, though without too many hot extremes, and they were rising most of all in the Arctic where the sea ice was receding. Europe enjoyed several decades of near-immunity from severe winters, and the variability of temperature from year to year was reduced. More rainfall was reaching the dry places in the interiors of the great continents (except in the Americas where the lee effect, or ‘rain-shadow’, of the Rocky Mountains and the Andes became more marked as the prevalence of westerly winds in middle latitudes increased).” (end of quote) Climate,
H. H. Lamb, History and the Modern World Edition 2, Routledge, 1995
The Belda maps show the climate regions of the world (except Antarctica) for two periods, 1901-1931 and 1975-2005, based on a 30 minute grid, average area about 2500 km2, (About 50,000 grid cells cover 135 million km2, the land area of the Earth except Antarctica.)
Between the two periods separated by 75 years, 8% of the cells changed climate type. When you plot a scatter diagram of distributions for the two periods, you will find there is little divergence from the straight line passing through the origin and with slope unity. R-squared is 99.5.
The paper does not discuss error bars. However, the CRU (UK) has revised the climate data to remove wet bias, an adjustment that would increase R2, indicating even less change than these maps show.
In any other field of Earth science, using data with similar precision, we would claim confirmation of the null hypothesis that the two data sets separated by 75 years are not significantly different.
So yes, the Earth has warmed a little and most people worldwide are better off than their parents and grandparents. The people benefiting the most are those on the margins of steppe to desert and those on the margins between ice and tundra.
Climate classification revisited from Köppen to Trewartha, Belda, M. et al, Climate Research, 2014
Click to access c059p001.pdf
Thanks Frederick. Very informative and you make the situation very clear. A great link also.
Much the same finding resulted from a comparable study referenced in my post: The paper is: Using the Köppen classiﬁcation to quantify climate variation and change: An example for 1901–2010
By Deliang Chen and Hans Weiteng Chen
Department of Earth Sciences, University of Gothenburg, Sweden
Click to access Chen_and_Chen_2013_envdev.pdf
Belda doesn’t reference Chen & Chen, perhaps because it was accepted for publication 2 months after Belda submitted his own paper.