Category Archives: Climate Change Forcings & Feedbacks

The Importance of Land Use/Land Practices On Climate – A Perspective From Jon Foley

In 2007, I posted

Presentation On Global Change and Climate Change By Jon Foley At The April 4-6, 2007 NASA Land-Cover and Land-Use Change Meeting

From his website

Jonathan Foley is the director of the Institute on the Environment (IonE) at the University of Minnesota, where he is a professor and McKnight Presidential Chair in the Department of Ecology, Evolution and Behavior. He also leads the IonE’s Global Landscapes Initiative.

The title of Jon’s talk was

Planet Against the Grain

As a result of a committee I am on [which I will have more to say about at a later date], there remains the misunderstanding with respect to the role of human land management on the climate system. Jon Foley’s powerpoint presentation is among the very best at documenting the role of human land management as a first order climate system (and environmental) forcing.

Examples of his view (which I agree with) are summarized in his talk. These include

  • massive changes to Earth’s land ~40% of land converted to agriculture [from slide 7]
  • massive increases in water use – water use tripled in 50 years – mostly due to agriculture – 70% irrigation, 20% industry, 10% domestic [from slide 8]
  • massive release of excess nutrients doubling natural nitrogen, phosphorus flows polluted lakes and rivers coastal “dead zones” [from slide 9]

Jon introduces a key finding that

land use practices are changing quickly; much more than changing land cover [from slide 30]

With respect to greenhouse emissions, he states that

wow! global land use & agriculture, taken together, contribute more greenhouse gases than any single societal activity; altogether, agriculture and deforestation appear to contribute at least 1/3 of all GHG forcing

In regards to using a global average metric to characterize changes in the climate system, he wrote that land use

often get “washed out” in outdated climate metrics of radiative forcing and global mean temperature [from slide 44]

He writes the overarching theme of his talk includes that

Bottom Line Global Change is Much More Than CO2 and Global Warming [from slide 46]

and that

Current Focus on CO2 / Climate Connection is Very Short Sighted [from slide 54]

Jon’s perspective can be read in his papers, such as

Foley et al 2005: Global Consequences of Land Use. Science. Science 22 July 2005: 570-574. [DOI:10.1126/science.1111772]

and

Foley et al, 2011: Solutions for a cultivated planet. Nature478, 337-342 doi:10.1038/nature10452

In his 2011 article he wrote

Agriculture is now a dominant force behind many environmental threats, including climate change, biodiversity loss and degradation of land and freshwater…. In fact, agriculture is a major force driving the environment beyond the ‘‘planetary boundaries’’ ….

I also highly recommend Jon’s talk

“The Other Inconvenient Truth” – Jon Foley, TECxTC presentation. Watch the video on YouTube

Clearly,  climate assessments that focus primarily on CO2 and a few other greenhouse gases are inappropriately too narrow.

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New Paper “Climatic Variability Over Time Scales Spanning Nine Orders of Magnitude: Connecting Milankovitch Cycles With Hurst–Kolmogorov Dynamics” By Markonis And Koutsoyiannis

There is another excellent paper that documents a larger magnitude of natural climate variation than has been assumed by the IPCC climate community [see also “The Climate Is Not What You Expect” By S. Lovejoy and D. Schertzer 2012 which I discussed in my post

Excellent New Paper “The Climate Is Not What You Expect” By Lovejoy and Schertzer 2012

This paper is

Yannis Markonis • Demetris Koutsoyiannis, 2012: Climatic Variability Over Time Scales Spanning Nine Orders of Magnitude: Connecting Milankovitch Cycles with Hurst–Kolmogorov Dynamics. Surv Geophy DOI 10.1007/s10712-012-9208-

The abstract reads [highlight added]

We overview studies of the natural variability of past climate, as seen from available proxy information, and its attribution to deterministic or stochastic controls. Furthermore, we characterize this variability over the widest possible range of scales that the available information allows, and we try to connect the deterministic Milankovitch cycles with the Hurst–Kolmogorov (HK) stochastic dynamics. To this aim, we analyse two instrumental series of global temperature and eight proxy series with varying lengths from 2 thousand to 500 million years. In our analysis, we use a simple tool, the climacogram, which is the logarithmic plot of standard deviation versus time scale, and its slope can be used to identify the presence of HK dynamics. By superimposing the climacograms of the different series, we obtain an impressive overview of the variability for time scales spanning almost nine orders of magnitude—from 1 month to 50 million years. An overall climacogram slope of -0.08 supports the presence of HK dynamics with Hurst coefficient of at least 0.92. The orbital forcing (Milankovitch cycles) is also evident in the combined climacogram at time scales between 10 and 100 thousand years. While orbital forcing favours predictability at the scales it acts, the overview of climate variability at all scales suggests a big picture of irregular change and uncertainty of Earth’s climate.

The conclusion includes the text

The available instrumental data of the last 160 years allow us to see that there occurred climatic fluctuations with a prevailing warming trend in the most recent past. However, when this period is examined in the light of the evidence provided by palaeoclimate reconstructions, it appears to be a part of more systematic fluctuations; specifically, it is a warming period after the 200-year ‘Little Ice Age’ cold period, during a 12,000-year interglacial, which is located in the third major icehouse period of the Phanerozoic Eon. The variability implied by these multi-scale fluctuations, typical for Earth’s climate, can be investigated by combining the empirical climacograms of different palaeoclimatic reconstructions of temperature. By superimposing the different climacograms, we obtain an impressive overview of the variability for time scales spanning almost nine orders of magnitude—from 1 month to 50 million years.

Two prominent features of this overview are (a) an overall climacogram slope of -0.08, supporting the presence of HK dynamics with Hurst coefficient of at least 0.92 and (b) strong evidence of the presence of orbital forcing (Milankovitch cycles) at time scales between 10 and 100 thousand years. While orbital forcing favours predictability at the scales it acts, the overview of climate variability at all scales clearly suggests a big picture of enhanced change and enhanced unpredictability of Earth’s climate, which could be also the cause of our difficulties to formulate a purely deterministic, solid orbital theory (either obliquity or precession dominated). Endeavours to describe the climatic variability in deterministic terms are equally misleading as those to describe it using classical statistics. Connecting deterministic controls, such as the Milankovitch cycles, with the Hurst–Kolmogorov stochastic dynamics seems to provide a promising path for understanding and modelling climate.

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New Paper “Indian Ocean Warming Modulates Pacific Climate Change” By Luo Et Al 2012

Jing-Jia Luoa,Wataru Sasaki, and Yukio Masumoto, 2012: Indian Ocean warming modulates Pacific climate change. Published online before print October 29, 2012, doi: 10.1073/pnas.1210239109 PNAS October 29, 2012

The abstract reads [highlight added]

It has been widely believed that the tropical Pacific trade winds weakened in the last century and would further decrease under a warmer climate in the 21st century. Recent high-quality observations, however, suggest that the tropical Pacific winds have actually strengthened in the past two decades. Precise causes of the recent Pacific climate shift are uncertain. Here we explore how the enhanced tropical Indian Ocean warming in recent decades favors stronger trade winds in the western Pacific via the atmosphere and hence is likely to have contributed to the La Niña-like state (with enhanced east–west Walker circulation) through the Pacific ocean–atmosphere interactions. Further analysis, based on 163 climate model simulations with centennial historical and projected external radiative forcing, suggests that the Indian Ocean warming relative to the Pacific’s could play an important role in modulating the Pacific climate changes in the 20th and 21st centuries.

The conclusions include the text

“It is suggested that the multidecadal variability could be modulated or partly forced by anthropogenic radiative forcing, particularl the offset effects between GHGs and aerosol (31, 32). However, the signal-to-noise ratio (i.e., the ratio of the variance of multimodel ensemble mean to the variance of intermodel spreads) is small; this indicates uncertainties in attributing the multidecadal changes to external forcing. Besides, understanding exact mechanisms responsible for the multidecadal fluctuations and how global warming might modulate the multidecadal changes remains a challenge…..our results suggest that differences in the response to anthropogenic forcing over individual ocean basins, together with the interinfluence between the tropical IO and the Pacific, may affect not only the centennial trends but also multidecadal changes of the Pacific climate.”

This is yet another paper that highlights the complexity of the climate system and the difficulty skillful multi-decadal climate predictions and in seeking to attribute regional climate to particular climate forcings.

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Wind Turbines and Clouds – Another Human Climate Forcing

Figure caption: Normally invisible, wind wakes take shape in the clouds behind the Horns Rev offshore wind farm west of Denmark. (Credit: Photo courtesy of Vattenfall)

In my posts

Comments On A Study By Ron Prinn and Chien Wang On The Effect Of Wind Turbines On Climate

Significance And Correction Of Misinterpretation By The Media Of The Zhou Et Al 2012 Paper “Impacts Wind Farms On Land Surface Temperature”

New Paper “Impacts Of Wind Farms On Land Surface Temperature” By Zhou Et Al 2012 Documents An Effect Of Local And Regional Landscape Change On Long Term Surface Air Temperature Trends

I presented a discussion of how local near surface temperatures can be affected by wind turbines, but not larger scale weather. However, a photograph  provides evidence of a larger scale effect (due the creation of clouds). Such clouds could travel large distances from where they are generated. These clouds are formed when the mixing layer height is reached as a result of vertical mixing; i.e. see Section 4.2.7 in

Pielke Sr., R.A. 2002: Synoptic Weather Lab Notes. Colorado State University, Department of Atmospheric Science Class Report #1, Final Version, August 20, 2002.

The figure at the top of this post from the article

Wind Turbines: In the Wake of the Wind 

This is an example of how wind turbines can feedback and directly affect at least local and nearby regional weather (and, therefore, climate).

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“Understanding The Impact Of Dam-Triggered Land Use/Land Cover Change On The Modification Of Extreme Precipitation” By Woldemichael Et Al 2012

We have a new paper accepted (in press) on the role of landscape processes on climate. It is

Woldemichael, A. T., F. Hossain, R. Pielke Sr., and A. Beltrán-Przekurat (2012), Understanding the impact of dam-triggered land use/land cover change on the modification of extreme precipitation, Water Resour. Res., 48, WXXXXX, doi:10.1029/2011WR011684.

The abstract reads [highlight added]

Two specific questions are addressed in this study regarding dams (artificial reservoirs). (1) Can a dam (artificial reservoir) and the land use/land cover (LULC) changes triggered by it physically alter extreme precipitation? The term extreme precipitation (EP) is used as a way of representing the model-derived upper bound of precipitation that pertains to the engineering definition of the standard probable maximum precipitation (PMP) used in design of dams. (2) Among the commonly experienced LULC changes due to dams, which type of change leads to the most detectable alteration of extreme precipitation? The American River Basin (ARW) and the Folsom dam were selected as a study region. Four scenarios of LULC change (comprising also various reservoir surface areas) were analyzed in a step by step fashion to elucidate the scenario leading to most significant impact on EP. The Regional Atmospheric Modeling System (RAMS, version 6.2) was used to analyze the impact of these LULC scenarios in two modes. In the first mode (called normal), the probable precipitation pattern due to each LULC scenario was identified. The second mode (called moisture-maximized), the PMP pattern represented from a 100% relative humidity profile was generated as an indicator of extreme precipitation (EP). For the particular case of ARW and Folsom dam, irrigation was found as having the most detectable impact on EP (a 5% increase in 72 h total for the normal mode and a 3% increase for the moisture-maximized mode) in and around the ARW watershed. Doubling the reservoir size, on the other hand, brought only a small change in EP. Our RAMS-simulated results demonstrate that LULC changes driven by dams can, in fact, alter the local to regional hydrometeorology as well as extreme precipitation. There is a strong possibility of a positive feedback mechanism initiated by irrigated landscapes located upwind of orographic rain producing watersheds that are impounded by large dams.

In the conclusions we wrote

The key goal of our study was to seek answers to two specific science questions: (1) Can a dam (artificial reservoir) and the land use/land cover (LULC) changes triggered by it physically contribute to the modification of extreme precipitation? (2) Among the commonly experienced LULC change due to dams, which type of change leads to the most detectable alteration of extreme precipitation? The answer to our first question is a “yes” while for the second question, we observed that for a dam in which the irrigated land is down- stream and upwind, the irrigation impact is much more superior from the two examined impacts in modifying the extreme precipitation patterns.

This is another in the continuing series of papers by ourselves and other colleagues that document the first-order climate forcing of land use/land cover change.

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News Article “Cool Roofs May Have Side Effects On Regional Rainfall” By Umair Irfan, E&E reporter

Figure from Irfan 2012

There is an E & E Publishing, LLC news article that has appeared. It is by Umair Irfan, E&E reporter titled

Cool roofs may have side effects on regional rainfall

It reads [higlight added]

ClimateWire: Wednesday, October 3, 2012

As desert sands yield to asphalt and concrete, the climate is shifting in Arizona’s “Sun Corridor,” an expanding urban region that includes Phoenix, Tucson, Prescott and Nogales. Researchers are now finding that efforts to offset the climate shift may carry side effects of their own.

Towering buildings, dark roads and sparse vegetation combine to trap heat, making cities warmer than surrounding areas. Previous studies showed that these effects are profound. “What we saw was that urbanization-induced warming is just as important as greenhouse gas-induced climate change,” said Matei Georgescu, an assistant professor in the School of Geographical Sciences and Urban Planning at Arizona State University.

State planners expect the cities in Arizona’s “Sun Corridor” to fuse into a megalopolis by 2050. Click the map for a larger version. Photo courtesy of the University of Arizona.

In a study published last month in the journal Environmental Research Letters, Georgescu demonstrated that these effects change with the seasons and have consequences for regional hydrology, as well. “There’s more to it than just average temperature.”

The Sun Corridor is a good test case, according to Georgescu; it is the fastest-growing “megapolitan” region in the United States. How much population and development growth there will be is uncertain, so Georgescu and his team set a floor and a ceiling for urbanization projections up to the year 2050 based on available data from the Maricopa Association of Governments, the regional agency in charge of long-term planning.

The researchers found that cities would generate the most warming during the summers under the maximum development scenario, with warming exceeding 1 degree Celsius. Under the minimum development projections, warming ranged from 0.1 to 0.3 degrees Celsius for most of the year outside winter.

The models also showed another curious development: Cool roofs — created when developers use reflective paint on rooftops — do perform their intended task of reducing temperatures in urban areas while cutting building energy costs. However, they shift rainfall patterns by reducing evapotranspiration, the process by which water evaporates from the ground and enters the atmosphere. In the maximum expansion scenario, cool roofs led to a 4 percent decline in rainfall.

Modifying CO2 footprint can modify the weather

“Does that suggest that cool roofs are a negative? I think what this leads to is future research to see how they should place cool roofs to minimize impacts,” Georgescu said. “Certain regions might be more appropriate for cool roofs than others.”

Some changes in rain patterns also stem from development itself. “When you put this carpet of urban land use, you’re forbidding the land from capturing and storing the water,” Georgescu said. “We’ve shown in some of our previous work that locally recycled water is very important for regional rainfall.”

Roger Pielke [Sr.], a senior research scientist at the Cooperative Institute for Research in Environmental Sciences at the University of Colorado, Boulder, noted that offsetting or mitigating humanity’s impacts on the world often carries unintended consequences. “Any geoengineering approach will have other effects as well as for the one it is designed to respond to,” he said in an email.

He pointed to research that showed how wind turbines alter regional temperatures even as they reduce carbon emissions that contribute to global climate change. Such trends mean scientists and policymakers will have to factor in how synthetic climate forcers other than greenhouse gases will change temperature, rainfall and weather extremes.

To solve this problem, Pielke suggested measuring environmental variables from a regional scale up to a global scale as a more inclusive way to assess environmental risks than the top-down approach used by the Intergovernmental Panel on Climate Change.

“This vulnerability concept requires the determination of the major threats to local and regional water, food, energy, human health, and ecosystem function resources from extreme events including climate, but also from other social and environmental issues,” he said in a book chapter he co-authored in “Extreme Events and Natural Hazards: The Complexity Perspective” earlier this year.

For now, Georgescu said, he will concentrate on regional modeling because global climate models do not yet offer enough resolution to illuminate climate trends in areas like the Sun Corridor. Conducting similar studies in multiple regions around the world could help climate modelers improve their global projections and help planners anticipate local climate shifts.

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New Article “Special Section On Climate Change And Water Resources: Climate Nonstationarity And Water Resources Management.” By Salas Et Al 2012

Jose (Pepe) Salas of Colorado State University has alerted us to an important new paper that he has authored. It is

Salas, J., Rajagopalan, B., Saito, L., and Brown, C. (2012). ”Special Section on Climate Change and Water Resources: Climate Nonstationarity and Water Resources Management.” J. Water Resour. Plann. Manage., 138(5), 385–388. doi: 10.1061/(ASCE)WR.1943-5452.0000279

The first paragraph of the article reads [highlight added]

Over the past three decades, hydrologists and water resources specialists have been concerned with the issue of nonstationarity arising from several factors. First is the effect of human intervention on the landscape that may cause changes in the precipitation–runoff relationships at various temporal and spatial scales. Second is the occurrence of natural events such as volcanic explosions or forest fires that may cause changes in the composition of the air, the soil surface, and geomorphology. Third is the low-frequency component of oceanic–atmospheric phenomena that may have significant effects on the variability of hydrological processes such as annual runoff, peak flows, and droughts. Fourth is global warming, which may cause changes to oceanic and atmospheric processes, thereby affecting the hydrological cycle at various temporal and spatial scales. There has been a significant amount of literature on the subject and thousands of research and project articles and books published in recent decades.

Among the informative text in the article, I was pleased to see their further confirmation of land use/land cover changes as a first-order climate forcing, when they wrote

Examples of human intrusion on the landscape are the changes in land use resulting from agricultural developments in semiarid and arid lands (e.g., Pielke et al. 2007, 2011), changes caused by large-scale deforestation (e.g., Gash and Nobre 1997), changes resulting from open-pit mining operations (e.g., Salas et al. 2008), and changes from increasing urbanization in watersheds (e.g., Konrad and Booth 2002,Villarini et al. 2009)…..Large-scale landscape changes such as deforestation in the tropical regions can potentially alter atmospheric circulation patterns, and consequently affect global weather and climate (e.g., Lee et al. 2008, 2009).

With respect to natural forcings and feedbacks, they write

Major natural events, such as the volcanic explosion of Mount St. Helens in 1980 or the El Chichon volcanic explosion of 1982 induce a shock to the climate system in the form of global cooling that continues for several years. These events can also affect global circulation. Low-frequency climate drivers of the oceanic– atmospheric system such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) modulate global climate at interannual and multidecadal time scales. These drivers are the main sources of nonstationarity in global climate and hydrology. Large numbers of papers documenting the effect of these drivers on global hydroclimatology continue to emerge (e.g., Dilley and Heyman 1995; Mantua et al. 1997; Enfield et al. 2001; Akintug and Rasmussen 2005; Hamlet et al. 2005).

With respect to “global warming“, they write

In addition to climate variability and change due to the previously mentioned factors, anthropogenic warming of the oceans and atmosphere because of increased greenhouse gas concentrations and the ensuing changes to the hydrologic cycle are topics of serious pursuit. The international scientific community is making strides in understanding the potential warming and its effects on all aspects of climate variability [Intergovernmental Panel on Climate Change (IPCC) 2007], but the impacts on the hydrologic cycle remain debatable and inconclusive (e.g., Cohn and Lins 2005; Legates et al. 2005; Hirsch and Ryberg 2011). Based on analyses of the global mean CO2 (GMCO2) and annual flood records in the United States, no strong statistical evidence for flood magnitudes increasing with GMCO2 increases were found (Hirsch and Ryberg 2011). Although general circulation models have had success in the attribution of warming global temperatures to anthropogenic causes, their credibility and utility in reproducing variables that are relevant to hydrology and water resources applications is less clear. For example, the IPCC Report for Latin America acknowledges that “the current GCMs do not produce projections of changes in the hydrological cycle at regional scales with confidence. In particular the uncertainty of projections of precipitation remain high….That is a great limiting factor to the practical use of such projections for guiding active adaptation or mitigation policies” (Magrin et al. 2007; Boulanger et al. 2007).

It is refreshing to see this broader perspective being adopted by the hydrology community.

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New Paper “Observations Of Increased Tropical Rainfall Preceded By Air Passage Over Forests” By Spracklen Et al 2012

Chris Taylor has alerted us to another very important paper. It is

D. V. Spracklen, S. R. Arnold, C. M. Taylor, 2012: Observations of increased tropical rainfall preceded by air passage over forests, 2012:  NatureVolume:489,Pages:282–285 (13 September 2012)DOI:doi:10.1038/nature11390

 The abstract reads [highlight added]

Vegetation affects precipitation patterns by mediating moisture, energy and trace-gas fluxes between the surface and atmosphere1. When forests are replaced by pasture or crops, evapotranspiration of moisture from soil and vegetation is often diminished, leading to reduced atmospheric humidity and potentially suppressing precipitation2, 3. Climate models predict that large-scale tropical deforestation causes reduced regional precipitation4, 5, 6, 7, 8, 9, 10, although the magnitude of the effect is model9, 11 and resolution8 dependent. In contrast, observational studies have linked deforestation to increased precipitation locally12, 13, 14 but have been unable to explore the impact of large-scale deforestation. Here we use satellite remote-sensing data of tropical precipitation and vegetation, combined with simulated atmospheric transport patterns, to assess the pan-tropical effect of forests on tropical rainfall. We find that for more than 60 per cent of the tropical land surface (latitudes 30 degrees south to 30 degrees north), air that has passed over extensive vegetation in the preceding few days produces at least twice as much rain as air that has passed over little vegetation. We demonstrate that this empirical correlation is consistent with evapotranspiration maintaining atmospheric moisture in air that passes over extensive vegetation. We combine these empirical relationships with current trends of Amazonian deforestation to estimate reductions of 12 and 21 per cent in wet-season and dry-season precipitation respectively across the Amazon basin by 2050, due to less-efficient moisture recycling. Our observation-based results complement similar estimates from climate models4, 5, 6, 7, 8, 9, 10, in which the physical mechanisms and feedbacks at work could be explored in more detail.

In their conclusuions, they write

Our analysis explores the role of regional-scale vegetation patterns on precipitation. Through evapotranspiration, forests maintain atmospheric moisture that can return to land as rainfall downwind. These processes operate on timescales of days over distances of 100–1,000km such that large-scale land-use change may alter precipitation hundreds to thousands of kilometres from the region of vegetation change. Land-use patterns and small-scale deforestation may also alter precipitation locally, through changes in the thermodynamic profile and the development of surface-induced mesoscale circulations. Natural and pyrogenic emissions from vegetation can also have a role in rainfall initiation over tropical forest regions. The impact of cloud microphysical processes on precipitation is highly uncertain, and biogenic emissions could contribute to our observed relationship between rainfall and exposed vegetation. However, our water-balance calculations imply that cumulative increases in evapotranspiration over upstream forested regions more than account for the increase in downstream rainfall.

What this paper means is that the atmosphere is enriched by water vapor and convective potential energy as it is transported across a region of transpiring vegetation. The removal of this vegetation results in an atmosphere that is less conducive to precipitation.  While the paper focuses on the effects of tropical deforestation, this would be expected to occur in locations where increases in transpiration occur such as due to irrigation. Several of our papers that have examined this issue include

Hossain, F., I. Jeyachandran, and R.A. Pielke Sr., 2010: Dam safety effects due to human alteration  of extreme precipitation. Water Resources Research, 46, W03301,   doi:10.1029/2009WR007704.

Degu, A. M., F. Hossain, D. Niyogi, R. Pielke Sr., J. M.   Shepherd, N. Voisin, and T. Chronis, 2011: The influence of large dams on surrounding climate and   precipitation patterns. Geophys. Res. Lett., 38, L04405, doi:10.1029/2010GL046482.

Woldemichael, A., F. Hossain,   R.A. Pielke Sr., and A. Beltrán-Przekurat, 2012: Understanding the impact of dam-triggered land use/land cover change on the modification of extreme precipitation, Water Resour. Res., doi:10. 1029/ 2011 WR011684.

Pielke, R.A. and X. Zeng, 1989: Influence on severe storm development  of irrigated land. Natl. Wea. Dig., 14, 16-17.

Pielke Sr., R.A., 2001: Influence of the spatial distribution of vegetation  and soils on the prediction of cumulus convective rainfall. Rev. Geophys.,  39, 151-177.

In Figure 9 in Pielke et al 2001, for example, we show the major impact on the potential for deep cumulonimbus clouds (and thus rainfall) of just an increasre in surface air dew point temperature by just one degree Celcius.

The new Sracklen et al 2012 is yet another example of why land use/land cover change is a first order human climate forcing.

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Guest Post By Robert Pollock On Global Climate Modeling

I was sent an informative e-mail by Robert Pollock on the global climate response to volacanic eruptions which I am presenting below with permission.  Robert is a retired physicist with training in radiation dosimetry.  He started a company to measure radon in the environment and sold it a few years ago.

His excellent set of information is presented below

On Tue, 11 Sep 2012, Robert Pollock wrote:

Roger, I don’t know if you have an interest in volcanic eruptions, but they are often cited as an example of the efficacy of GCMs and are very important when looking at ocean heat content.

Gleckler et al. modeled the effect of volcanic eruptions on ocean heat content. Using 12 climate models they showed that Krakatoa in 1883 made its presence felt well into the 20th century in the form of reduced sea level rise and less ocean warming (both on the surface and at depth). As stated in the AR4, including volcanic eruptions improved the model’s match to reality, and the cooling from volcanoes was offsetting a considerable fraction of anthropogenic ocean warming.

Figure 1 from Gleckler shows the difference with and without volcanic forcing between 1880 and 2000: www.nature.com/nature/journal/v439/n7077/fig_tab/439675a_F1.html

At the end of the 20th century simulations with (blue) and without (green) volcanic forcings have a difference of some 70% (18/60 10^22 J).

The authors wrote

“Inclusion of volcanic forcing from Krakatoa (and, by implication, from even earlier eruptions) is important for a reliable simulation of historical increases in ocean heat content and sea-level change due to thermal expansion.”

However, in a 2010 paper Gregory notes that ‘even earlier eruptions’ were not included in the Gleckler modeling work and if they had been, the conclusion would have been quite different. If an eruption produces a cooling and a drop of sea level rise that lasts decades (if not centuries) then each new eruption would lead to further decreases indefinitely.

Such is not the case, and Gregory modeled a steady-state condition resulting from earlier eruptions before Krakatoa. With other climate model the background natural conditions do not include volcanic eruptions. The impact of a new eruption (as part of a series) then becomes less and doesn’t lead to a long-term trend in ocean heat content.

Most GCMs overestimate the (depressive) effect of volcanoes and thus also overestimate the forcing from greenhouse gases to reproduce the climate and ocean heat content of the 20th century.

Gleckler et al. Volcanoes and climate: Krakatoa’s signature persists in the ocean www.nature.com/nature/journal/v439/n7077/abs/43975a.html

Gregory Long-term effect of volcanic forcing on ocean heat content www.agu.org/pubs/crossref/2010/2010GL045507.shtml

Driscoll et al. now have a paper in press that looks at the current generation of models used for the AR5 (13 CHIMP5 models) and their ability to model large tropical eruptions. The abstract lists a number of problems and

“raises concern for the ability of current climate models to simulate the response of a major mode of global circulation variability to external forcings. This is also of concern for the accuracy of geoengineering modeling studies that asses the atmospheric response to stratosphere-injection particles.”

Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions www.agu.org/pubs/crossref/pip/2012JD017607.shtml

Robert Pollock

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Comments On The Nature Article “Afternoon Rain More Likely Over Drier Soils” By Taylor Et Al 2012 – More Rocking Of The IPCC Boat

I have posted on the documented (in peer reviewed papers) gross inadequacies of the global climate models to provide skillful predictions in a hindcast mode; e.g.. see

More CMIP5 Regional Model Shortcomings

CMIP5 Climate Model Runs – A Scientifically Flawed Approach

I have been alerted to yet another study that documents this shortcoming [h/t Marcel Crok]. The final sentence of this study has the blunt comment regarding climate model skill that

“….the erroneous sensitivity of convection schemes demonstrated here is likely to contribute to a tendency for large-scale models to `lock-in’ dry conditions, extending droughts unrealistically, and potentially exaggerating the role of soil moisture feedbacks in the climate system.”

This new paper is

Taylor et al, 2012: Afternoon rain more likely over drier soils. Nature. doi:10.1038/nature11377. Received 19 March 2012 Accepted 29 June 2012 Published online 12 September 2012

[as a side note, I published with the senior author in the paper Taylor, C.M., R.J. Harding, R.A. Pielke, Sr., P.L. Vidale, R.L. Walko,  and J.W. Pomeroy, 1998: Snow breezes in the boreal forest. J. Geophys.  Res., 103, 23087-23101.]

The abstract reads [highlight added]

Land surface properties, such as vegetation cover and soil moisture, influence the partitioning of radiative energy between latent and sensible heat fluxes in daytime hours.During dry periods, soil-water deficit can limit evapotranspiration, leading to warmer and drier conditions in the lower atmosphere. Soil moisture can influence the development of convective storms through such modifications of low-level atmospheric temperature and humidity, which in turn feeds back on soil moisture. Yet there is considerable uncertainty in how soil moisture affects convective storms across the world, owing to a lack of observational evidence and uncertainty in large-scale models. Here we present a global-scale observational analysis of the coupling between soil moisture and precipitation. We show that across all six continents studied, afternoon rain falls preferentially over soils that are relatively dry compared to the surrounding area. The signal emerges most clearly in the observations over semi-arid regions, where surface fluxes are sensitive to soil moisture, and convective events are frequent. Mechanistically, our results are consistent with enhanced afternoon moist convection driven by increased sensible heat flux over drier soils, and/or mesoscale variability in soil moisture. We find no evidence in our analysis of a positive feedback—that is, a preference for rain over wetter soils—at the spatial scale (50–100 kilometres) studied. In contrast, we find that a positive feedback of soil moisture on simulated precipitation does dominate in six state-of-the-art global weather and climate models— a difference that may contribute to excessive simulated droughts in large-scale models.

The conclusions include the text

Finally, we repeat our analysis using 3-hourly diagnostics from six global models, ranging in horizontal resolution from 0.5 to 2.0 (degrees). Our results (Fig. 3) indicate a strong preference for rain over wet soils for large parts of the world, in contrast to the observations. Only one model (Fig. 3e) produces more than the expected 10% of grid cells with P<10, largely due to contributions at mid-latitudes. The crossmodel signal favouring precipitation over wet soil, particularly across the tropics (Supplementary Table 3), demonstrates a fundamental failing in the ability of convective parameterizations to represent land feedbacks on daytime precipitation. This is likely to be linked to the oft-reported phase lag in the diurnal cycle of precipitation; that is, simulated rainfall tends to start several hours too early, and is possibly amplified by a lack of boundary-layer clouds in some models. This weakness has been related to the crude criteria used to trigger deep convection in large-scale models. The onset of convective precipitation is overly sensitive to the daytime increase of moist convective instability, which is typically faster over wetter soils3, favouring a positive feedback. Early initiation limits the effect of other daytime processes on triggering convection in the models. In contrast, our observational analysis points to the importance of dry boundary-layer dynamics for this phenomenon over land.

The observed preference for afternoon rain over locally drier soil on scales of 50–100 km is consistent with a number of regional studies based on remotely sensed data. Our failure to find areas of positive feedback may indicate the importance of surface-induced mesoscale flows in triggering convection, although the coarse spatial resolution of our data sets prevents us from drawing firm conclusions on this issue. Equally, mixing processes in the growth stage of convective clouds before precipitation, may play an important role. Neither of these processes is captured in existing one-dimensional analyses. Furthermore, our results raise questions about the ability of models reliant on convective parameterizations to represent these processes adequately. Although the coarser-resolution models analysed here (HadGEM2, CNRM-CM5 and INMCM4) cannot resolve mesoscale soil moisture structures, nor their potential impacts on convective triggering, all the models have a strong tendency towards rain over wetter soils, for which we find no observational support. Our study does not, however, imply that the soil moisture feedback is negative at temporal and spatial scales different from those analysed here. The multi-day accumulation of moisture in the lower atmosphere from a freely transpiring land surface may provide more favourable initial (dawn) conditions for daytime convection than the equivalent accumulation over a drought-affected region. Equally, the large-scale dynamical response to soil moisture may dominate in some regions. However, the erroneous sensitivity of convection schemes demonstrated here is likely to contribute to a tendency for large-scale models to `lock-in’ dry conditions, extending droughts unrealistically, and potentially exaggerating the role of soil moisture feedbacks in the climate system.

We have also looked at this issue, for example, in our paper

Pielke Sr., R.A., 2001: Influence of the spatial distribution of vegetation  and soils on the prediction of cumulus convective rainfall. Rev. Geophys.,  39, 151-177

where we reported that

Wetzel et al. [1996], in a study in the Oklahoma area, found that cumulus clouds form first over hotter, more sparsely vegetated areas. Over areas covered with deciduous forest, clouds were observed to form 1–2 hours later due to the suppression of vertical mixing. Rabin et al. [1990] also found from satellite images that cumulus clouds form earliest over regions of large sensible heat flux and are suppressed over regions with large latent heat flux during relatively dry atmospheric conditions.

While we also reported that

Clark and Arritt [1995], however, found while the cumulus cloud precipitation was delayed when the soil moisture was higher, the total accumulation of precipitation was greater. The largest rainfall was generally predicted to occur for moist, fully vegetated surfaces…..De Ridder [1998] found that dense vegetation produces a positive feedback to precipitation.

This indicates, however, that if the delay is long enough during the day over moist soils and/or transpiring vegetation, cumulus rainfall will not ever be able to develop.

The take-home message from the Taylor et al 2012 paper is yet another demonstration of the failure of the climate models as a tool to skillfully simulate the climate system. Their use by the IPCC and others to predict droughts decades from now for the impacts communties is misleading and erroneous abuse of the current state of climate science. 

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