New Papers on the Importance of Land Use/Land Cover Change on Climate

Thanks to Timo Hämeranta, several new papers on the important role of land surface processes in the climate system are listed below. These are

1) Kleidon, Axel, 2006. The climate sensitivity to human appropriation of vegetation productivity and its thermodynamic characterization. Global and Planetary Change, in press, corrected proof available online 26 July 2006. The abstract reads,

” Humans appropriate terrestrial productivity to meet their food supply, their primary source of free energy. Removal of productivity from terrestrial vegetation has its direct impacts in that less energy is available for vegetation growth. Since vegetation strongly shapes the physical exchange of energy, water and momentum at the land surface, a lower ability for vegetation growth should affect this surface exchange, the overlying atmosphere, and therefore climate. Here I attempt to quantify the climate sensitivity to different intensities of human appropriation of vegetation productivity. I use sensitivity simulations with a coupled dynamic vegetation–climate system model of intermediate complexity in which I artificially remove different fractions of the simulated net primary productivity to implement human appropriation, thus reducing vegetation growth in the model. The simulations show noticeable differences in the surface energy- and water balance, with a consistent reduction in the amount of absorbed solar radiation and latent heat flux of up to 10 W m− 2 and 27 W m− 2 respectively and a reduction in continental precipitation by up to 30% in the global land mean when compared to the “Controlâ€? climate. However, the study also shows that mean land surface temperature is insensitive at the global scale despite pronounced regional patterns and is therefore not well suited to characterize the climatic sensitivity to land cover change at the global scale. I motivate the use of entropy production to characterize climate sensitivity. Entropy production is a thermodynamic measure of the strength of dissipative processes which perform physical work. With this measure, I show that the climate sensitivity is reflected as a clear trend towards less entropy production over land with increased intensity of human appropriation of NPP in general, and less entropy production by biotic activity in particular. I conclude that large-scale land cover changes are likely to lead to a noticeably different climate which is less favorable to biotic productivity and that this climate sensitivity is well captured by differences in entropy production as a meaningful, thermodynamic measure. ”

An important conclusion of this paper, which has also been one of the conclusions of Climate Science (e.g. see), is that

“However, the study also shows that mean land surface temperature is insensitive at the global scale despite pronounced regional patterns and is therefore not well suited to characterize the climatic sensitivity to land cover change at the global scale.”

2) Mahmood, Rezaul, Stuart A. Foster, Travis Keeling, Kenneth G. Hubbard,
Christy Carlson, and Ronnie Leeper, 2006. Impacts of irrigation on 20th century temperature in the northern Great Plains. Global and Planetary Change, in press, corrected proof available online 28 July 2006. The abstract of this paper reads,

“Land use change can modify root zone moisture distribution, energy partitioning and subsequently, near surface energy balance. Various modeling studies provided evidence of these changes. For example, land use change from natural grass land to irrigated land use would significantly increase and decrease latent and sensible energy flux, respectively. This type of long-term modification of energy balance would in turn change near surface temperatures. The Great Plains of North America experienced significant overturning of land from natural grass land to irrigated land use during the 20th century. This study provides assessment on the changes in the historical near surface temperature records in Nebraska, USA. Long-term mean monthly maximum, minimum, and monthly mean air temperature data from 5 irrigated and 5 non-irrigated sites were analyzed. Length and homogeneity of time series and stability of stations were primary determinants in selection of these stations. The time series include Cooperative Weather Observation Network (COOP) and Historical Climate Network (HCN) data sets. Pairwise comparisons of temperatures between irrigated and non-irrigated locations for pre- and post-1945, -1950, and -1955 periods were completed for both data sets. These breakdowns of time series helped to identify periods of widespread land use change. Results show notably cooler temperatures over irrigated areas. For example, mean maximum growing season temperature at irrigated Alliance was 0.64 °C and 1.65 °C cooler compared to non-irrigated Halsey during pre- and post-1945 period, respectively. Hence, there was a 1.01 °C cooling during post-1945 years. Moreover, there has been a greater cooling during the second half of 20th century. The bootstrap re-sampling method was applied and trend analyses were completed for further verification of results. These assessments largely show a decreasing trend in mean maximum growing season temperatures over irrigated areas. To further verify the results and to determine the impacts of extreme values (including extremely cool temperatures), the 20% trimmed mean approach was applied. The impacts of extreme values have been minimal and based on the results obtained we conclude that land use change in the northern Great Plains has modified near surface temperature records. ”

An important conclusion from this paper, that has also been concluded for a wide range of geographic regions on Climate Science (e.g.see), is that

“…….we conclude that land use change in the northern Great Plains has modified near surface temperature records.”

3) Ramankutty, Navin, Christine Delire, and Peter Snyder, 2006. Feedbacks between agriculture and climate: An illustration of the potential unintended consequences of human land use activities. Global and Planetary Change, in press, corrected proof available online 11 July 2006. The abstract reads,

“Agriculture has significantly transformed the face of the planet. In particular, croplands have replaced natural vegetation over large areas of the global land surface, covering around 18 million km2 of the land surface today. To grow crops, humans have taken advantage of the resource provided by climate — optimum temperature and precipitation. However, the clearing of land for establishing croplands might have resulted in an inadvertent change in the climate. This feedback might, in turn, have altered the suitability of land for growing crops. In this sensitivity study, we used a combination of land cover data sets, numerical models, and cropland suitability analysis, to estimate the degree to which the replacement of natural vegetation by croplands might have altered the land suitability for cultivation. We found that the global changes in cropland suitability are likely to have been fairly small, however large regional changes in cropland suitability might have occurred. Our theoretical study showed that major changes in suitability occurred in Canada, Eastern Europe, the Former Soviet Union, northern India, and China. Although the magnitude, sign, and spatial patterns of change indicated by this study may be an artifact of our particular model and experimental design, our study is illustrative of the potential inadvertent consequences of human activities on the land. Moreover, it offers a methodology for evaluating how climate changes due to human activities on the land may alter the multiple services offered by ecosystems to human beings. .”

Among the conclusions of this paper, is that their research methodology,

“….offers a methodology for evaluating how climate changes due to human activities on the land may alter the multiple services offered by ecosystems to human beings.”

This perspective fits with the framework that has been advocated on Climate Science of seeking to assess the vulnerability of important societal resources to the spectrum of social and environmental risk, including inadvertent land use change effects on climate (e.g. see).

These three papers add to an already overwhelming conclusion of the first-order climate effect of human alteration of the landscape. The drafts of the current IPCC Report that I have seen, as well as the first CCSP Report (see), have failed so far to adequately consider the importance of this climate forcing.

Mismatch Between Multi-decadal Global Climate Models Predictions And The Global Radiative Imbalance

There is a clear mismatch between the model predictions reported in the 2005 Science article by Hansen, J., L. Nazarenko, R. Ruedy, Mki. Sato, J. Willis, A. Del Genio, D. Koch, A. Lacis, K. Lo, S. Menon, T. Novakov, Ju. Perlwitz, G. Russell, G.A. Schmidt, and N. Tausnev 2005. “Earth’s energy imbalance: Confirmation and implications” , and the observational results in the Geophysical Research Letters paper by John M. Lyman, Josh K. Willis, and Gregory C. Johnson entitled “Recent Cooling of the Upper Oceanâ€?.

The abstract of the Hansen et al article reads,

“Our climate model, driven mainly by increasing human-made greenhouse gases and aerosols among other forcings, calculates that Earth is now absorbing 0.85±0.15 W/m2 more energy from the Sun than it is emitting to space. This imbalance is confirmed by precise measurements of increasing ocean heat content over the past 10 years. Implications include: (i) expectation of additional global warming of about 0.6°C without further change of atmospheric composition; (ii) confirmation of the climate system’s lag in responding to forcings, implying the need for anticipatory actions to avoid any specified level of climate change; and (iii) likelihood of acceleration of ice sheet disintegration and sea level rise.”

However, the new Lyman et al 2006 study which also is based on the same “precise measurements of increasing ocean heat content” report that the global radiative imbalance for 1993 through 2005, for the entire 13-year period, was an average warming rate of 0.33 ± 0.23 W/m2 , as a result of the 2003 to 2005 period which has a diagnosed radiative imbalance of -1.0 (+/- 0.3) W/meter squared.

The Comments on the Climate Science weblog with respect to earlier weblogs on the Lyman et al 2006 paper (see and see) include raising the issue on the relationship of this recent cooling to the reported continuing rise in the global average sea level. This is an appropriate scientific question.

However, if the upper ocean heat content data was considered precise in the Hansen et al 2005 study, and was used in that paper to bolster the confidence in their ability to model global climate process, then the same confidence should be placed on the recent diagnosis of observed cooling. The mismatch between the data and the model predictions, however, raises serious questions on the ability of the multi-decadal global climate models to accurately predict even the global average variability and long term trend of the radiative imbalance of the climate system.

Coupled Ocean-Atmosphere Response to Seasonal Modulation of Ocean Color: Impact on Interannual Climate Cimulations in the Tropical Pacific by Raghu Murtugudde

State of the art climate models continue to be plagued by some standard biases especially in the crucial regions such as the tropical Pacific (http://www.usclivar.org/Meeting_Files/SSC 11/Bias_worskhop_summary.pdf) which has a global reach through El Nino-Southern Oscillation (ENSO). One such bias is the so-called ‘cold bias’ where the model sea surface temperatures are typically colder than observed leading to a stronger east-west gradient, stronger winds, and the related cascades into annual cycle errors and thus ENSO and its teleconnections.

It has been known for decades that microscopic algae in the ocean convert light to heat during photosythesis and other suspended solids in the ocean also affect light attenuation in the water column. Satellites like SeaWiFS now provide global maps of pigments and other materials which affect light absorption in the ocean. With this global information, we can now specify the e-folding depth for light-attenuation in ocean general circulation models and represent the conversion of light to heat by the photosynthesizing critters (http://essic.umd.edu/~ragu/biofeedbacks/murtuguddeqpen.pdf).

The eastern equatorial Pacific is a really unique place with the mixed layer variability interacting closely with the location of the maximum chlorophyll concentrations. During the boreal spring months when the mixed layer is shallow due to weak winds and maximum surface heating, the chlorophyll maximum is just below the mixed layer since the thermo/nutricline are just below the mixed layer, i.e., in the euphotic zone. This provides a heat source of about 5-20 W/m2 depending on the strength of the chlorophyll maximum and this heat restratifies the water column leading to a deeper mixed layer and momentum penetration, thus weaker surface divergence and subsurface upwelling. Much of the ‘cold-bias’ is thus alleviated if this bio-climate feedback is represented appropriately (http://essic.umd.edu/~ragu/biofeedbacks/quimfeedback.pdf).

A hybrid coupled model and physical-biological models confirm that improving the annual cycle in this way is a natural solution to improving the annual phase-locking of the ENSO events in the model (http://essic.umd.edu/~ragu/biofeedbacks/coupledfeedbacks.pdf). The assumption that annual biases do not impact ENSO simulations and predictions thus appears not to be very robust since improving the annual cycle of the coupled system also improves the ENSO cycle by improved mixed layer-thermocline or Bjerknes feedbacks (http://essic.umd.edu/~ragu/biofeedbacks/marzeion.pdf).

Models tend to produce ENSO-like behavior that is surface-trapped and quasibienniel (http://www.grida.no/climate/ipcc_tar/wg1/024.htm) whereas improved Bjerknes feedback due to accurate biological feedbacks provides a more accurate representation of the recharge-discharge processes thus improving the ENSO amplitude and frequencies. As we continue to increase the complexity of our coupled climate models, these processes have to be represented appropriately since bio-climate feedbacks also occur in other regions of the World Ocean and also over land (http://www.gfdl.noaa.gov/~jpd/esmdt.html).

Raghu Murtugudde, Assoc. Professor, ESSIC/DAOS, Univ of MD, College Park, MD 20742
http://essic.umd.edu/~ragu

Information on the Argo Ocean Monitoring Network

In order to let readers of Climate Science have a clear idea of the spatial distribution of the upper ocean temperature distributions that were used so effectively in the Lyman et al 2006 study “Recent Cooling of the Upper Oceanâ€? [see and see ] information on the Argo monitoring network is provided below.

A summary of Argo is available (see). Excerpts from the Argo webiste are,

“Argo is a global array of 3,000 free-drifting profiling floats that measures the temperature and salinity of the upper 2000 m of the ocean. This allows, for the first time, continuous monitoring of the temperature, salinity, and velocity of the upper ocean, with all data being relayed and made publicly available within hours after collection.”

The objectives of Argo are:

“It will provide a quantitative description of the changing state of the upper ocean and the patterns of ocean climate variability from months to decades, including heat and freshwater storage and transport.

The data will enhance the value of the Jason altimeter through measurement of subsurface temperature, salinity, and velocity, with sufficient coverage and resolution to permit interpretation of altimetric sea surface height variability.

Argo data will be used for initializing ocean and coupled ocean-atmosphere forecast models, for data assimilation and for model testing.

A primary focus of Argo is to document seasonal to decadal climate variability and to aid our understanding of its predictability. A wide range of applications for high-quality global ocean analyses is anticipated. ”

The data density of the Argo network can be viewed here.

The Lyman et al 2006 GRL paper demonstrates the value of this data, including the demonstration that when multi-decadal climate predictions are compared with real-world data, the models can fail to explain observed climate variability and change.

Max Mayfield, Director of the National Hurricane Center Announces His Retirement

The weather and climate community will lose a valuable public servant whose work has saved lives. Max Mayfield, Director of the National Hurricane Center is retiring at the end of this season (see). We wish him continued success in whatever he chooses to do, as well as look forward to his continued involvement in weather and climate science.

Research Papers On The Accuracy Of Weather and Climate Modeling Simulation and Prediction Of The Stable Atmospheric Boundary Layer

A set of excellent papers has appeared in Boundary Layer Meteorology on the stable atmospheric boundary layer. This research is led by Professor Bert Holtslag of Wageningen University, and is part of the GEWEX Atmospheric Boundary-layer Study (GABLS) on Stable Boundary Layers program. The stable boundary layer occurs on most nights, and persists for months at high latitudes over land and sea ice in the winter.

An important research question is whether global and regional climate models can accurately simulate the stable boundary layer, including long term near surface temperature variability and trends that result from natural- and human-climate forcings and feedbacks. The research papers presented in the Boundary Layer Meteorology document that major issues remain on the ability of the climate (and weather) models to skillfully simulate stable boundary layer processes, including near surface temperatures.

Excerpts from two of the papers (subscriptions required) read,

[from “Modeling the arctic stable boundary layer and its coupling to the surface” by Steeneveld et al],

“The impact of coupling the atmosphere to the surface energy balance is examined for the stable boundary layer, as an extension of the first GABLS (GEWEX Atmospheric Boundary-Layer Study) one-dimensional model intercomparison. This coupling is of major importance for the stable boundary-layer structure and its development because coupling enables a realistic physical description of the interdependence of the surface temperature and the surface sensible heat flux. In the present case, the incorporation of a surface energy budget results in stronger cooling (surface decoupling), and a more stable and less deep boundary layer. The proper representation of this is a problematic feature in large scale numerical weather prediction and climate models…..”

and from [“Preface: GEWEX Atmospheric Boundary-layer Study (GABLS) on Stable Boundary Layers” by Bert Holtslag],

“Unfortunately regional and global climate models show great sensitivity to the model formulation of mixing in stratified conditions. As an example, Viterbo et al. (1999) studied the vertical mixing in the ECMWF model in stable conditions. From two model runs with the same forcing conditions, but with (slightly) different stability functions in the mixing scheme, they noticed that differences in the mean winter temperatures at a height of 2m between the two model runs can be as large as 10 K over continental areas.

In addition, King et al. (2001) found similar results between model runs for the winter climate over Antarctica. Also over Europe, it was found that significant differences are present between the 2-m temperatures of a 30- year regional climate simulation with observations for present day winter climate (e.g., Lenderink et al., 2003). It also appears that the magnitude of the diurnal temperature cycle is typically underestimated over land….”

“Within GABLS, a rather simple case was selected as a benchmark to review the state of the art and to compare the skills of single column (1D) models (Cuxart et al., 2006) and large-eddy simulation (LES) models (Beare et al., 2006). The papers in this special volume report on these findings. The case studied is based on the results originally presented by Kosovic and Curry (2000). As such the stable boundary layer is driven by an imposed, uniform geostrophic wind, with a specified surface-cooling rate over (homogeneous) ice. Overall it turns out that, with the same initial conditions and model forcings, the results of the LES models are surprisingly consistent (Beare et al., 2006). As such the LES outputs can serve as suitable reference for the 1D models. Moreover, the results of the LES models are consistent with field observations and local scaling ideas (Nieuwstadt, 1984), at least for the case studied here.

In contrast, the 1D models indicate a large range of results for the mean temperature and wind profiles as well as for the heat and momentum flux profiles (Cuxart et al., 2006). As expected the models in use at operational weather forecast and climate centres typically allow for enhanced mixing, while typical research models show less mixing, more in agreement with the LES results for this case. Because of the enhanced mixing in weather and climate models, these models tend to show too strong surface drag, a too deep boundary layer, and an underestimation of the wind turning in the lower atmosphere. However, by decreasing the mixing and surface drag, a direct impact on the atmospheric dynamics (‘Ekman pumping’) is noted (e.g., Beljaars and Viterbo, 1998). Consequently, cyclones may become too active, corresponding with too high an extremes for wind speed and for precipitation. When the models with enhanced mixing are coupled to a surface energy balance, they also produce a too high surface temperature (e.g., Steeneveld et al., 2006). Given the latter arguments and the current GABLS findings, there is still a clear need for a better understanding and a more general description of the atmospheric boundary layer under stably stratified conditions in atmospheric models for weather and climate.”

Since the 1D models are the type used in the parameterization of the stable boundary layer in multi-decadal climate model projections, this is yet another reason to question their use as accurate predictive models, including their use to forecast the long term trends in near surface air temperature over land and sea ice.

December 2006 American Geophysical Union meeting “Aerosol Cloud-Precipitation

What promises to be an informative climate science meeting is scheduled for December 2006 at the American Geophysical Union meeting in San Francisco. The Session is entitled “Aerosol Cloud-Precipitation Interaction: Facts and Fiction“. The announcement for the meeting reads,

“This session, which was initiated by the recently deceased Dr. Yoram Kaufman, recognizes his contributions to studies of aerosol, cloud and precipitation interactions. Aerosol interaction with clouds and precipitation is one of the most complex yet most important array of processes in the climate system. Aerosols affect clouds through determining their microstructure, and through affecting atmospheric and surface temperatures. Clouds process aerosols and their precursors, changing the aerosol size distributions and CCN concentrations that feed back on the clouds. Aerosols were shown to induce changes in cloudiness, in precipitation patterns, in regional circulations and in cloud-mediated forcing of climate. We are in the early stages of research and our knowledge may be faulty and lacking. Therefore we seek contributions that untangle these effects, distinguish facts from fiction using in situ and remote observations and model analysis, and discuss regional implications. The session is designed to foster stronger links between measurements, models and regional assessments.

We are looking forward to your participation and contributions to this exciting and controversial topic.”

The summary of this meeting accentuates how much more we need to learn about the climate system, including the role of humans. An obvious interpretation from this meeting announcement is that accurate multi-decadal climate predictions are not possible since we inadequately understand the diverse roles of aerosols in the climate system. This has been a message that has been repeatedly emphasized on Climate Science, and this AGU meeting summary reinforces this view.

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.

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.

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.