Monthly Archives: August 2006

NASA Press Release: “There’s a change in rain around desert cities”

An informative NASA press release by Rob Gutro appeared on July 25, 2006 entitled “There’s a change in rain around desert cities”.

A summary schematic image that is presented in the press release reads,

“This image shows warm air rising from an urban area, and subsequent cloud formation. Cities tend to be one to 10 degrees Fahrenheit (.56 to 5.6 Celsius) warmer than surrounding suburbs and rural areas and the added heat can destabilize the atmosphere and change the way air circulates around cities. Added heat creates wind circulations and rising air that can produce or enhance existing clouds. Under the right conditions, these clouds can evolve into rain-producers or storms. It is suspected that converging air due to city surfaces of varying heights, like buildings, also promotes rising air needed to produce clouds and rainfall. Winds can carry these clouds to the east of the cities.”

Other excerpts from the press release are,

“Urban areas with high concentrations of buildings, roads and other artificial surface soak up heat, lead to warmer surrounding temperatures, and create “urban heat-islands.” This increased heat may promote rising air and alter the weather around cities. Human activities such as land use, additional aerosols and irrigation in these arid urban environments also affect the entire water cycle as well.”

“A study by J. Marshall Shepherd, a climatologist at the University of Georgia, Athens, used a unique 108-year-old data record and data from NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite, to examine arid cities’ rainfall patterns. Shepherd found a 12-14 percent increase in rainfall in the northeast suburbs of Phoenix from the pre-urban (1895-1949) to post-urban (1950-2003) periods. This increase in rainfall is likely related to changes in the city and the lands within the city, such as more roadways and buildings in place of open natural area. The increase may also be related to changes in irrigation. However, the role of irrigation in changing the weather of cities in arid areas requires more study, Shepherd said.”

“We think that human activities, such as changing the landscape, can affect the flow of the winds associated with the U.S. southwest’s monsoon and rising air and building storms on the east side of mountains,” said Shepherd. The weather in Phoenix, in fact, is affected by both, and that can change where the rains fall.”

“Shepherd used satellite images from the Landsat satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer instrument aboard NASA’s Terra satellite to determine expansion characteristics. He used the TRMM satellite’s rainfall data to pinpoint precipitation areas. This study shows the importance of satellite data in regions like the Middle East, where traditional measurements are sparse or inaccessible. ‘Many of the fastest-growing urban areas are in arid regimes,’ said Marshall Shepherd, author of the report just published in the online edition of the Journal of Arid Environments. ‘Because their total rainfall is low, these areas have been largely ignored in studies on how human activities affect the water cycle. But these cities are particularly sensitive to such changes, since the water supply is so critical.'”

“‘The results showed us just how sensitive the water cycle can be to human-induced changes, even under arid or drought conditions’ Shepherd said. These findings have real implications for water resource management, agricultural efficiency and urban planning.”

The title of the December 2006 Journal of Arid Environments article is “Evidence of urban-induced precipitation variability in arid climate regimes” . The abstract reads,

“The study employs a 108-year precipitation historical data record, global climate observing network observations and satellite data to identify possible anomalies in rainfall in and around two major arid urban areas, Phoenix, Arizona and Riyadh, Saudi Arabia. The analysis reveals that during the monsoon season, locations in northeastern suburbs and exurbs of the Phoenix metropolitan area have experienced statistically significant increases in mean precipitation of 12–14% from a pre-urban (1895–1949) to post-urban (1950–2003) period. Further analysis of satellite-based rainfall rates suggests the existence of the anomaly region (AR) over a 7-year period. The anomaly cannot simply be attributed to maximum topographic relief and is hypothesized to be related to urban-topographic interactions and possibly irrigation moisture. Temperature records suggest that Riyadh has experienced an adjustment in mean temperature in response to the growth of urban surfaces (e.g. the so-called urban heat island effect). While ground-based precipitation records also indicate an upward trend in mean and total precipitation in and around Riyadh in the last 10–15 years, it is difficult to attribute the increase to urbanization because other less urbanized stations in Saudi Arabia also show a similar increase. Recent satellite-based precipitation estimates indicate an AR 50–100 km north of Riyadh, but this study is not robust enough to conclusively link it to urbanization although certain climate-regime attributes suggests that it might be.”

This study provides additional evidence as to why a focus on local and regional human climate forcings needs to be elevated in importance in the climate science community.

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Comment on the Real Climate Post on “Short and Simple Arguments For Why Climate Can Be Predicted” . Climate Science Disagrees With Their Statement

The weblog Real Climate posted text on 12 Aug 2006 entitled “Short and simple arguments for why climate can be predicted“. Today Climate Science posts a response to that post, which disagrees with this conclusion.

Excerpts from the Real Climate weblog read,

“I like to emphasi(ze) the words ‘weather’ and ‘climate’ above, because they mean different things.” [where the definition of climate in Real Climate is linked to the Wikipedia definition that reads

“The climate (from ancient Greek: κλίμα, “clime”) is commonly considered to be the weather averaged over a long period of time, typically 30 years.”].

However, this is narrow definition of “climate” has been superseded by the recognition, as reported in the 2005 National Research Council Report “Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties” (page 12), that

“Climate is conventionally defined as the long-term statistics of the weather (e.g., temperature, cloudiness, precipitation). This definition emphasizes the atmospheric and physical components of the climate system. These physical processes within the atmosphere are affected by ocean circulation, the reflectivity of the Earth’s surface, the chemical composition of the atmosphere, and vegetation patterns, among other factors. Improved understanding of how the atmosphere interacts with the oceans, the cryosphere (ice-covered regions of the world), and the terrestrial and marine biospheres has led scientists to expand the definition of climate to encompass the oceanic and terrestrial spheres as well as chemical components of the atmosphere (Figure 1-1). This expanded definition promotes an Earth system approach to studying how and why climate changes.”

“FIGURE 1-1 The climate system, consisting of the atmosphere, oceans, land, and cryosphere. Important state variables for each sphere of the climate system are listed in the boxes. For the purposes of this report, the Sun, volcanic emissions, and human-caused emissions of greenhouse gases and changes to the land surface are considered external to the climate system.”

Thus, Real Climate presented an incomplete definition of climate. They also present a very limited perspective on forecasting climate. The Real Climate text includes,

“But, still I say that I know with certainty that there is a very high probability that the temperature in 6 months will be lower than now – when winter has arrived (it’s summer on the northern hemisphere at the present). In fact, the seasonal variation in temperature and rainfall (wet and dry seasons in the tropics) tends to be highly predictable: the winters at high latitudes are cold and summers mild…”

However, we can make the same type of statements on short term weather prediction. There is a high probability that the temperature tonight will be lower than the afternoon temperature!

We need to move beyond the perspective that climate is just long term weather statistics. The challenge is not to “predict” that next summer will be warmer than the winter. We already know that! What is needed are accurate predictions of the variability (on all space and time scales) and change of climate. This includes not only weather variables, such as temperature and precipitation, but also the wide diversity of climate variables such as soil moisture, vegetation greenness and ocean phytoplankton. The time scales include daily, weekly, seasonal, yearly, multiyear and multidecadal periods. So far, skill has only been achieved for daily and weekly time scales, and for limited situations such as strong El Niños, on the seasonal time scale. There has been no skill shown on time scales longer than this for regional and smaller spatial scales, and arguably even for the global scale averages (e.g. see).

In order to provide a framework that scientists can use to discuss whether climate models have predictive skill, I recommend the following framework (adapted from the Climate Science weblog of July 15 2006):

Process studies: The application of climate models to improve our understanding of how the system works is a valuable application of these tools. In an essay, I used the term sensitivity study to characterize a process study. In a sensitivity study, a subset of the forcings and/or feedback of the climate system may be perturbed to examine its response. The model of the climate system might be incomplete and not include each of the important feedbacks and forcings (e.g. such as produced in the IPCC Reports; e.g. see).

Diagnosis: The application of climate models, in which observed data is assimilated into the model, to produce an observational analysis that is consistent with our best understanding of the climate system as represented by the manner in which the fundamental concepts and parameterizations are represented. Although not yet applied to climate models, this procedure is used for weather reanalyses (e.g see the NCEP/NCAR 40-Year Reanalysis Project).

Forecasting: The application of climate models to predict the future state of the climate system. Forecasts can be made from a single realization, or from an ensemble of forecasts which are produced by slightly perturbing the initial conditions and/or other aspects of the model.

With these definitions, since none of the climate models contain all of the important climate forcings and feedbacks (as given in the aforementioned 2005 National Research Council Report) the models results must not be interpreted as forecasts (or the equivalent term “projection”). Since they have been applied to project the decadal-averaged weather conditions in the next 50-100 years and more, they cannot be considered as diagnostic models since we do not yet have the observed data to insert into the models.

Therefore, despite the Real Climate statement to the contrary, climate has not been accurately predicted on time scales longer than a season. Multi-decadal climate model simulations, such as that reported in the IPCC and CCSP Reports should be communicated as process studies in the context that they are sensitivity studies (e.g. see also).

Real Climate is invited to comment on this Climate Science weblog posting.

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Can Multi-decadal Temperature Trends from Poorly Sited Locations Be Corrected?

An article has appeared in the August 2006 issue of the Bulletin of the American Meteorological Society entitled “Examination of Potential Biases in Air Temperature Caused by Poor Station Locations” Thomas C. Peterson, pages 1073–1089. This article was motivated by the article

Davey, C.A., and R.A. Pielke Sr., 2005: Microclimate exposures of surface-based weather stations – implications for the assessment of long-term temperature trends. Bull. Amer. Meteor. Soc., Vol. 86, No. 4, 497–504.

The headline for the Peterson paper reads,

“Analysis of a small subset of U.S. Historical Climatology Network data does not find a time-depedent bias caused by current poor station siting”.

The abstract for the paper reads,

“Questions have been raised about whether poor siting practices that have existed in recent years at some in situ weather-observing stations are causing a bias in U.S. temperature change analysis. This potential bias was examined using homogeneity-adjusted maximum, minimum, and mean temperature data from five stations in eastern Colorado—two with good current siting and three with poor current siting. No siting-induced bias was found in the homogeneity-adjusted data. Furthermore, the results indicate that homogeneity-adjusted time series from the stations with poor current siting represent the temperature variability and change in the region as a whole quite well because they are very similar to the time series from stations with excellent siting.”

Tom Peterson is an excellent climate scientist. However, his paper clearly conflicts with several peer reviewed contributions which we have recently summarized on the Climate Science weblog, including:

“Reexamination of instrument change effects in the U.S. Historical Climatology Networkâ€? by Hubbard K. and X. Lin August 12 2006 Geophysical Research Letters.

“Land use/land cover change effects on temperature trends at U.S. Climateâ€? by R. C. Hale, K. P. Gallo, T. W. Owen, and T. R. Loveland June 3 2006 Geophysical Research Letters

“The Geoprofile metadata, exposure of instruments, and measurement bias in climatic record revisited” by Rezaul Mahmood, Stuart A. Foster and David Logan June 30, 2006 International Journal of Climatology

When we first heard of the Peterson article, we assumed it would be treated as a Comment on our Davey and Pielke 2005 BAMS paper. BAMS took over 3 years to process our paper, and then required that the National Climate Data Center (NCDC) prepare a Response to the Davey and Pielke article (“Comments on ‘Microclimate Exposures of Surface-Based Weather Stations’â€? by Russell S. Vose, David R. Easterling, Thomas R. Karl, and Michael Helfert.

I assumed the Peterson article would also be published with a Reply from Christopher Davey and I. However, despite my requests to permit us to prepare a Reply to the Peterson article, it was decided that there was new information in the Peterson article. My request was refused. I was written that

“In the case of your 2005 article, Jeff Rosenfeld felt that since your work raised significant (though potentially justified) criticism of an observing network that the entire scientific community relies upon and would impact the public confidence in those networks, that a companion comment was appropriate to provide additional perspective. This does not appear to be the case with Peterson’s current article, which is simply providing scientific evidence to clarify arguments for alternative hypotheses.” [Jeff Rosenfeld is Editor-in-Chief of the Bulletin of the American Meterological Society].

Since the Peterson article claims to resolve the problem, yet we have serious issues with his contribution, it would seem that the same approach of two articles would have been permitted. Nonetheless, this was not allowed. This imbalance in the ability to present climate science viewpoints unfortunately permeates the scientific literature including that of the Bulletin of the American Meteorological Society (BAMS).

We have, therefore, written an article for BAMS in response to the Peterson article, and it is authored and titled

Pielke Sr., R.A, C. Davey, J. Angel, O. Bliss, M. Cai, N. Doesken, S. Fall, K. Gallo, R. Hale, K.G. Hubbard, H. Li, X. Lin, J. Nielsen-Gammon, D. Niyogi, and S. Raman, 2006: Documentation of bias associated with surface temperature measurement sites. Bull. Amer. Meteor. Soc., submitted. [it should not yet be cited or reproduced as it is currently under review; comments to us on the manuscript, however, are welcome].

There is a major change in the claims of the capabilities of the climate monitoring network of the National Climate Data Center, as reported in the Peterson article, regarding the use of poorly sited station data. He has concluded that specific poorly sited observation sites can be corrected. As we report in our paper,

“Davey and Pielke (2005) presented photographic documentation of poor observation sites within the U.S. Historical Climate Reference (USHCN) with respect to monitoring long term surface air temperature trends. Peterson (2006) compared the adjusted climate records of many of these stations and concluded that

‘ …homogeneity adjusted time series from stations with poor current siting represent the temperature variability and change in the region as a whole quite well as they are almost identical to the time series from stations with excellent siting.’

One of the objectives of the USHCN as stated in Easterling et al (1996),

‘…was to detect temporal changes in regional rather than local climate. Therefore, only stations not influenced to any substantial degree by artificial changes in their local environments were included in the network.’

Peterson’s claim relaxes this requirement with the assertion that poor station data can be corrected, so as to represent regional changes. There remain significant issues, however, with the methodology applied and the conclusion reached in the Peterson article.”

The Editor of BAMS has promised an expeditious processing of our paper. We will see and keep the readers of the Climate Science weblog updated.

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Article entitled “Climatology Between Science and Politics”

Recently, we presented a talk in Italy at the Spoletoscienza 2006 (see).

A short overview article version of our talk appeared in the journal Limes. The English version is titled “Climatology Between Science and Politics” and is available starting on page 59. [In Italian, it’s been published inside a volume of Limes, dedicated to environmental issues. It is not available online, but a summary and the cover is; see].

The text of the article starts with,

“It has become widely appreciated that humans have an influence on the climate system. As a consequence scientists, political advocates, and policy makers are debating what sorts of policies make sense to implement related to climate change. The timing for such debates have been motivated by the disappointing results of the Kyoto Protocol process, which in any case, is focused only on the period leading to 2012 and was not designed to serve as a comprehensive solution.

But as debate on climate policies takes place, what has not been well appreciated is that decisions related to complex scientific issues often must be based on oversimplifications of the relevant science. Such oversimplifications are of course acceptable if the scientific shorthand nonetheless manifests itself in actions that lead to desirable societal outcomes. Climate science is incredibly complex, yet is at risk of being over-simplified in policy proposals in ways that creates risks for achieving desirable societal outcomes.

Under the Framework Convention on Climate Change the term ‘climate change’ is defined as ‘a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability over comparable time periods.’ This narrow definition stands in stark contrast to the broader definition used by the Intergovernmental Panel on Climate Change (IPCC), the United Nations group tasked with assessing climate science for policy makers, which states that climate change is ‘any change in climate over time whether due to natural variability or as a result of human activity.’

A narrow view of climate change may have been appropriate when the science was less well understood and greenhouse gases were thought to be the primary (and perhaps only) significant human forcing of the climate system. However, in [the] recent decade there has been increasing recognition among many in the scientific community that the components of the Earth System are intimately connected, and that interactions extend from local to global scales. The recognition of the multiple interactions across space and time scales has led to a new interdisciplinary perspective, which promises to be an effective means to advance our understanding of the Earth System, and its human-caused and natural dynamics.”

We conclude the article with the recommendations that,

“To accommodate the perspective that the Earth System, including the climate, involves complex forcings and interactions across space and time scales requires us to be more inclusive in the involvement of the diverse communities performing climate and environmental change research, and to elevate interdisciplinary scientists [to] leadership roles in these communities.”

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Plant diversity- Another Climate Metric

A new book has appeared from Oxford University Press entitled “Measuring Plant Diversity Lessons from the Field”. The author is Thomas J. Stohlgren who is Invasive Species Science Branch Chief, U.S. Geological Survey Fort Collins Science Center, and Senior Scientist and Affiliate Faculty Natural Resource Ecology Laboratory, Colorado State University.

The information on the book reads,

“Most textbooks on measuring terrestrial vegetation have focused on the characteristics biomass, cover, and the density or frequency of dominant life forms (trees, shrubs, grasses, and forbs), or on classifying, differentiating, or evaluating and monitoring dominant plant communities based on a few common species. Sampling designs for measuring species richness and diversity, patterns of plant diversity, species-environment relationships, species distributions have received less attention. There are compelling, urgent reasons plant ecologists to do a far better job measuring plant diversity in this new century. Rapidly invading plant species from other countries are affecting rangeland condition and wildlife habitat, placing more plant species on threatened and endangered species lists, and increasing wildfire fuel loads. Attention has shifted from the classification of plant communities accurately mapping rare plant assemblages and species of management concern to them better protection. More ecologists, wildlife biologists, and local and regional planners recognize the value in understanding patterns, dynamics, and interactions of rare and common plant species and habitats to better manage grazing, fire, invasive plant species, practices, and restoration activities. Thus, revised and new sampling approaches, designs, field techniques for measuring plant diversity are needed to assess critical emerging issues facing land managers. This book offers alternatives to the approaches, designs, and techniques of the past that were chiefly designed for dominant species and other purposes. The author focuses on techniques that move beyond classifying, mapping, and measuring plant diversity for relatively homogeneous communities. This book complements methods for measuring the biomass and cover of dominant plant species. Most species are sparse, rare, and patchily distributed. It empowers the reader to take an experimental approach in the science of plant diversity to better understand the distributions of common and rare species, native and non-native species, and long-lived and short-lived species.”

This text is important in climate science for two major reasons. First, the book demonstrates further the complex dynamics of vegetation, which as recognized in the 2005 National Research Council report, is a climate variable. Secondly, in terms of what has the greater impact on vegetation, Dr. Stohlgren has articulated in his seminars that invasive species are by far the greater threat than climate variability and change. This book provides the scientific basis for this conclusion.

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Another Peer-Reviewed Paper on Problems With Using Multi-Decadal Surface Temperature Trends As a Robust Climate Change Metric

Another important paper has appeared that raises serious questions on the accuracy of the Historical Climate Network (HCN) to accurately assess multi-decadal surface air temperature trends. The article is in the August 12 2006 issue of Geophysical Research Letters and is entitled “Reexamination of instrument change effects in the U.S. Historical Climatology Network” (subscription required). The authors are K. G. Hubbard and X. Lin of the High Plains Regional Climate Center at the University of Nebraska, Lincoln, Nebraska, USA. [Thanks to Dev Niyogi of Purdue and Scott Robeson of Indiana University for alerting me to this paper!].

The abstract reads,

“The homogenized U.S. Historical Climatology Network (HCN) data set contains several statistical adjustments. One of the adjustments directly reflects the effect of instrument changes that occurred in the 1980s. About sixty percent of the U.S. HCN stations were adjusted to reflect this instrument change by use of separate constants applied universally to the monthly average maximum and minimum temperatures regardless of month or location. To test this adjustment, this paper reexamines the effect of instrument change in HCN using available observations. Our results indicate that the magnitudes of bias due to the instrument change at individual stations range from less than -1.0°C to over +1.0°C and some stations show no statistical discontinuities associated with instrument changes while others show a discontinuity for either maximum or minimum but not for both. Therefore, the universal constants to adjust for instrument change in the HCN are not appropriate. ”

An excerpt from the article read,

“It is clear that future attempts to remove bias should tackle this adjustment station by station.Our study demonstrates that some MMTS stations require an adjustment of more than one degree Celsius for either warming or cooling biases. These biases are not solely caused by the change in instrumentation but may reflect some important unknown or undocumented changes such as undocumented station relocations and siting microclimate changes (e.g., buildings, site obstacles, and traffic roads).”

This study provides further support that we need to move beyond using multi-decadal global trends in surface temperature as a primary metric to assess climate change, as has been recommended on the Climate Science weblog.

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The Next Generation of Climate Models – Too Narrow A Perspective Presented by Cray Inc.

A recent press release entitled “The Next Generation of Climate Models” by Per Nyberg Earth Sciences Segment Director, Cray Inc. , which was communicated by Timo Hameranta to his mailing group, is very informative with respect to the persistent and inappropriate focus of multi-decadal climate modeling on the atmospheric component of the climate system (Thanks Timo!). The news release has the statement,

“‘The heart of CCSM is weather modeling,’ says ORNL’s John Drake, chief computational scientist for the End Station effort. ‘But whereas the weather guys give up on their models after 15 days, we plow ahead for 100 years, and then take statistical averages. The statistical properties of that solution, linked with a variety of other processes, produces what we call climate.'”

Yet immediately before in the text it is stated that,

” CCSM is a ‘coupled’ climate model, meaning that it integrates various component models (ocean simulations, atmospheric simulations, ice sheet modeling, etc.) into a unified picture of the Earth’s climate system. To produce this unified view, the simulation must be designed so that each component model can influence and be influenced by other component models in the system. (For example, ocean models must be coupled with atmospheric models, because increases in sea surface temperature have an impact on tropical storms and El Niño events.) ”

The contradictory perspectives presented in these two paragraphs clearly illustrate a continued myopic view of the climate system. To state that “The heart of CCSM is weather modeling” shows an incomplete understanding of the climate, as has been articulated, for example, in the 2005 National Research Council report. The climate system figure and some of the other text in the news release is well done, and agrees with the NRC perspective, but the message that the different earth system components of the land, atmosphere, ocean and cryosphere are equal partners in climate is not accurately articulated in the quote by John Drake.

We need to move beyond the view that the atmosphere is the driver of the climate system (i.e. its “heart”) and recognize the intimate nonlinear coupling between components of the climate system in which no one component is its “heart”.

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