An unusual weather setup produced a very narrow and isolated band of snow across parts of Dodge City and Ford county on 1/19/11. The atmosphere was cold and moist with low clouds and fog preceding the formation of the snow. It appears that two slaughter plants and a power generating plant contributed to the snow as ice nuclei and copious amounts of water vapor were fed into the boundary layer. East/southeast winds carried the vapor and nuclei aloft into the lower clouds and then precipitated out as snow downwind of the source. Snowfall of as much as 0.7″ was reported in the snowfield with no snow observed at all outside of this area.
Monthly Archives: February 2011
Zeke starts the post with the text
“My personal pet peeve in the climate debate is how much time is wasted on arguments that are largely spurious, while more substantive and interesting subjects receive short shrift.”
I agree with this view, but conclude that Zeke is missing a fundamental issue.
“Climate sensitivity is somewhere between 1.5 C and 4.5 C for a doubling of carbon dioxide, due to feedbacks (primarily water vapor) in the climate system…”
The use of the terminology “climate sensitivity” indicates an importance of the climate system to this temperature range that does not exist. The range of temperatures of “1.5 C and 4.5 C for a doubling of carbon dioxide” refers to a global annual average surface temperature anomaly that is not even directly measurable, and its interpretation is even unclear, as we discussed in the paper
Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229.
This view of a surface temperature anomaly expressed by “climate sensitivity” is grossly misleading the public and policymakers as to what are the actual climate metrics that matter to society and the environment. A global annual average surface temperature anomaly is almost irrelevant for any climatic feature of importance.
Even with respect to the subset of climate effects that is referred to as global warming, the appropriate climate metric is heat changes as measured in Joules (e.g. see). The global annual average surface temperature anomaly is only useful to the extent it correlates with the global annual average climate system heat anomaly [most of which occurs within the upper oceans]. Such heating, if it occurs, is important as it is one component (the “steric component”) of sea level rise and fall.
For other societally and environmentally important climate effects, it is the regional atmospheric and ocean circulations patterns that matter. An accurate use of the terminology “climate sensitivity” would refer to the extent that these circulation patterns are altered due to human and natural climate forcings and feedbacks. As discussed in the excellent post on Judy Curry’s weblog
finding this sensitivity is a daunting challenge.
I have proposed definitions which could be used to advance the discussion of what we “agree on”, in my post
As I wrote there
Global Warming is an increase in the heat (in Joules) contained within the climate system. The majority of this accumulation of heat occurs in the upper 700m of the oceans.
Global Cooling is a decrease in the heat (in Joules) contained within the climate system. The majority of this accumulation of heat occurs in the upper 700m of the oceans.
Global warming and cooling occur within each year as shown, for example, in Figure 4 in
Ellis et al. 1978: The annual variation in the global heat balance of the Earth. J. Geophys. Res., 83, 1958-1962.
Multi-decadal global warming or cooling involves a long-term imbalance between the global warming and cooling that occurs each year.
Climate Change involves any alteration in the climate system , which is schematically illustrated in the figure below (from NRC, 2005)
which persists for an (arbitrarily defined) long enough time period.
Shorter term climate change is referred to as climate variability. An example of a climate change is if a growing season 20 year average of 100 days was reduced by 10 days in the following 20 years. Climate change includes changes in the statistics of weather (e.g. extreme events such as droughts, land falling hurricanes, etc), but also include changes in other climate system components (e.g. alterations in the pH of the oceans, changes in the spatial distribution of malaria carrying mosquitos, etc).
The recognition that climate involves much more than global warming and cooling is a very important issue. We can have climate change (as defined in this weblog post) without any long-term global warming or cooling. Such climate change can occur both due to natural and human causes.”
It is within this framework of definitions that Zeke and Judy should solicit feedback in response to their recent posts. I recommend a definition of “climate sensitivity” as
Climate Sensitivity is the response of the statistics of weather (e.g. extreme events such as droughts, land falling hurricanes, etc), and other climate system components (e.g. alterations in the pH of the oceans, changes in the spatial distribution of malaria carrying mosquitos, etc) to a climate forcing (e.g. added CO2, land use change, solar output changes, etc). This more accurate definition of climate sensitivity is what should be discussed rather than the dubious use of a global annual average surface temperature anomaly for this purpose.
Madhav Khandekar sent me the news article with his viewpoint that appeared in the Hindustan Times. It is titled
Madhav has presented several informative guest posts previously; e.g. see
and the article further summarizes his perspective.
New Paper “Deep Ocean Warming Assessed From Altimeters, Gravity Recovery And Climate” By Song and Colberg (2011).
There is a new paper that adds to the understanding the global warming. It is [h/t to Skeptical Science]
Song, Y. T., and F. Colberg (2011), Deep ocean warming assessed from altimeters, Gravity Recovery and Climate
Experiment, in situ measurements, and a non‐Boussinesq ocean general circulation model, J. Geophys. Res., 116, C02020,
with the abstract [boldface added]
“Observational surveys have shown significant oceanic bottom water warming, but they are too spatially and temporally sporadic to quantify the deep ocean contribution to the present‐day sea level rise (SLR). In this study, altimetry sea surface height (SSH), Gravity Recovery and Climate Experiment (GRACE) ocean mass, and in situ upper ocean (0–700 m) steric height have been assessed for their seasonal variability and trend maps. It is shown that neither the global mean nor the regional trends of altimetry SLR can be explained by the upper ocean steric height plus the GRACE ocean mass. A non‐Boussinesq ocean general circulation model (OGCM), allowing the sea level to rise as a direct response to the heat added into the ocean, is then used to diagnose the deep ocean steric height. Constrained by sea surface temperature data and the top of atmosphere (TOA) radiation measurements, the model reproduces the observed upper ocean heat content well. Combining the modeled deep ocean steric height with observational upper ocean data gives the full depth steric height. Adding a GRACE‐estimated mass trend, the data‐model combination explains not only the altimetry global mean SLR but also its regional trends fairly well. The deep ocean warming is mostly prevalent in the Atlantic and Indian oceans, and along the Antarctic Circumpolar Current, suggesting a strong relation to the oceanic circulation and dynamics. Its comparison with available bottom water measurements shows reasonably good agreement, indicating that deep ocean warming below 700 m might have contributed 1.1 mm/yr to the global mean SLR or one‐third of the altimeter‐observed rate of 3.11 ± 0.6 mm/yr over 1993–2008.”
There is a significant analysis quality issue with the authors using “the top of atmosphere (TOA) radiation measurements” as one of the constraints on their analysis. These radiation measurements are of fluxes and have a signficant uncertainty.
Nonetheless, if we assume the analysis of Song et al 2011 is robust in that there is significant ocean heating below 700m (~1/3 of that between the surface and 700m if the steric sea level rise scales linearly in Joules), then this is a significant sink for this heat with respect to the rest of the climate system.
The website Skeptical Science did not discuss this sink but, since heating at depth presumably is distributed spatially and becomes quite diffuse, its reentry into the higher ocean and atmosphere will be slow and muted, if it occurs at all in coming years and decades. The heating of the remainder of the climate system (included a “global annual average surface temperature trend) will be less than if this heat was confined to higher in the ocean (i.e. above the thermocline).
This characteristic of the deeper ocean as a heat sink conflicts with the Song and Colberg (2011) conclusion that
“…the enormous heat stored in the deep ocean would have a profound effect on the climate and deserves a serious attention in projecting future sea level changes.”
Any heat stored at depth in the oceans is a damping effect on the climate variability and longer-term change within the rest of the climate system.
There is a new paper which addresses an interesting question on the role of land use/land cover change in the vicinity of Mount Kilimanjaro on the climate in this region. The paper is
Fairman, J. G., Jr., U. S. Nair, S. A. Christopher, and T. Mölg (2011), Land use change impacts on regional climate over Kilimanjaro, J. Geophys. Res., 116, D03110, doi:10.1029/2010JD014712
and the abstract reads
“Glacier recession on Kilimanjaro has been linked to reduction in precipitation and cloudiness largely because of large‐scale changes in tropical climate. Prior studies show that local changes in land cover can also impact orographic cloudiness, precipitation, and terrain‐generated circulation patterns. This study uses the Regional Atmospheric Modeling System to simulate dry season orographic cloudiness, rainfall, and orographic flow patterns over Kilimanjaro for current deforested and reforested land cover scenarios. The simulations for current land cover show satisfactory performance compared to surface meteorology and satellite‐observed cloudiness. Clouds occur less frequently in response to deforestation, with the magnitude of decrease increasing with deforestation. On the windward side, cloud liquid water path (LWP) and precipitation both show decreases at lower elevations (∼1000–2000 m) and increases at higher elevations (2000–4000 m) in response to deforestation. This pattern is caused by decreased aerodynamic resistance, leading to enhanced wind speeds and convergence at higher elevations. On the lee regions, LWP deficits found in deforested simulations coincide with regions of reduced moisture while precipitation increased slightly at lower elevations (1000–1800 m) and decreased at higher elevations (1800–4000 m). Kilimanjaro offers less obstruction to background airflow, and reduced moisture transport to the lee side is found for deforested conditions, causing reduced LWP and rainfall. However, land use change has little effect on cloudiness and rainfall at elevations in excess of 4000 m and is not expected to impact glaciers in the summit zone of Kilimanjaro during the dry season. The effect in other seasons requires further investigation.”
The conclusions read
This study utilized numerical model simulations to investigate the impact of land cover changes at lower elevations of Kilimanjaro on the regional climate of the area. RAMS was used to simulate atmospheric conditions for July 2007, assuming current, deforested, and forested land cover scenarios. The findings from the comparison of these simulations can be summarized as follows.
1. Comparison of RAMS simulations for current land use conditions against surface meteorological observations and satellite observations of cloudiness show satisfactory performance of RAMS over the study region.
2. The RAMS simulations show that deforestation at lower elevations of Kilimanjaro lead to a decrease in the frequency of cloud occurrence at all elevations. The cloud liquid water path decreases in response to deforestation except at higher elevations on the windward side where it increases. Reforestation has the opposite effect, increasing frequency of occurrence of clouds at all elevations, increases in cloud liquid water path except at higher elevation on the windward side where it decreases.
3. Precipitation decreases at low elevations and increases at midelevations on the windward side in response to deforestation. On the leeward side, precipitation decreases at midelevations, while there is a very small increase at lower elevations. The magnitude of differences increases with the extent of deforestation.
4. Flow diversion values computed for the different scenarios also show that obstruction caused by Kilimanjaro is enhanced when the lower elevations areas are reforested.
5. Surface moisture patterns are also altered because of changes in terrain flow, with reforestation increasing moisture transport to the lee side of the mountain compared to current vegetation and deforestation.
6. While differences in surface moisture contributes to decrease in frequency of occurrence in cloudiness, changes in flow pattern caused by reduced aerodynamic roughness play an important role. When the lower‐elevation regions are deforested, Kilimanjaro offer less obstruction to background flow, and the resulting increase in flow around the mountain causes reduced moisture transport to the lee side, causing reduced cloud liquid water path and precipitation. On the windward side, the increase in wind speed directed parallel to the topographic gradient at higher elevations, caused by reduced aerodynamic roughness in upwind areas, leads to enhanced surface convergence, cloud liquid water path, and precipitation.
7. Lack of precipitation at the peak during the period of study prevents making conclusions about potential impacts on precipitation at that level. Further study is required to investigate the possibility of such effects occurring during other seasons.
This study addresses only the impact of deforestation on one dry season month. There are no compelling reasons for expecting the physical processes that cause the changes in clouds and precipitation to be substantially different if the analysis is extended to include the dry season month of July from other years. However, further study that extends the analysis to other seasons is required to establish the overall impact of land use change on the higher‐elevation climate of Kilimanjaro.
This is yet another important study which documents the significant role of human alterations of the landscape on the climate.
Daily Camera News Article By Laura Snider “Reservoirs Can Change Regional Rainfall Patterns- Research Highlights Ways Humans Inadvertently Affect Local Weather”
There is a well written news article in the Boulder Daily Camera based on our paper
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, doi:10.1029/2010GL046482, in press
It is reproduced below from the February 18 2011 Boulder’s Daily Camera.
CU-Boulder: Reservoirs can change regional rainfall patternsResearch highlights ways humans inadvertently affect local weather by Laura Snider Camera Staff Writer
Large dams — and the reservoirs they create — can increase rainfall in semiarid regions, according to a team of researchers that includes a scientist from the University of Colorado.
The study, published in the journal Geophysical Research Letters, took into account both the size of the reservoir and the changes the reservoir may have caused in the way surrounding lands were used.
“When you have a dam, the alteration of the landscape and the presence of the water body provide more water vapor to the atmosphere than would be there in the natural state,” said Roger Pielke Sr. of CU’s Cooperative Institute for Research in Environmental Sciences.
Studies such as this one are important, Pielke said, because they highlight the ability of humans to inadvertently alter weather patterns and, therefore, modify the local impacts of larger climatic changes. These types of studies also show that there are many ways humans can affect the climate beyond emitting greenhouse gases, one of which is by altering the landscape.
“There are a whole range of forcings that can affect weather as well as climate,” he said. “And they’re underreported in terms of how humans are altering the climate system.”
Faisal Hossain, a researcher at Tennessee Tech and a co-author of the study, agrees that the new research represents a change in mindset about how humans interact with the climate.
“We know a lot about how climate affects reservoirs, but what we didn’t know a lot about was what a reservoir could do to the local climate,” he said in a news release. “We just reversed our thinking by saying that a reservoir and the activities it supports are just as important a player for climate as the larger climate is for the reservoir.”
Mesoscale Associations Between Midwest Land Surface Properties And Convective Cloud Development in the Warm Season” By Allard and Carleton
There is an excellent article on the role of landscape within the climate system. It is
Allard, J. and Carleton, A.M. (2010) Mesoscale Associations between Midwest Land Surface Properties and Convective Cloud Development in the Warm Season. Physical Geography, Vol. 31, 107-136.
The abstract reads [highlight added]
“The study of land surface–atmosphere interactions is vital to understanding climatic variations in the Earth’s planetary boundary layer, particularly given continual land cover modifications by humans on local to regional scales. An agriculturally important region ideal for the study of land surface–atmosphere interactions is the Midwest United States “Corn Belt.” To evaluate the mesoscale relationships between Corn Belt land surface properties and a key indicator of the surface–atmosphere feedback in humid climates—warm-season convective cloud development—conventional meteorological data, digital maps of land surface properties, and satellite data were examined in a GIS framework for the May–September periods of 1991 through 1999. The results indicate associations between the surface and atmospheric moisture content and the formation of convective clouds: cumulus clouds initiate first and persist longer over a dry (moist) surface with an initially dry (moist) atmosphere. These relationships are evident when forcing from the free atmosphere is either extremely weak (i.e., when fronts and other disturbances are absent and 500 mb winds are 18 ms–1). However, the association between convective cloud development and other land surface properties (e.g., land use–land cover [LULC], soil order, elevation, and slope) is not consistent spatially. We find that a surface moisture–convective cloud relationship dominates Corn Belt land surface–atmosphere interactions across a range of barotropic synoptic conditions under different atmospheric moisture contents. The study results can help lead to improved prediction of convective cloud formation, and more realistic modeling of land surface–atmospheric interactions for weather and climate forecasting.”
The conclusion includes the text
“The results of this study ultimately could help lead to more realistic modeling of land surface–atmosphere interactions. Knowledge of the locations of climatically important land surface boundaries, including the relative proportions of cropland to forest at mesoscales, could help improve forecasts of deep convection for the Corn Belt during the warm season using mesoscale models. These improvements conceivably could come about through emphasizing the mesoscale land surface–atmosphere interaction important for deep convection, and would complement forecasting using model-predicted, synoptic-scale atmospheric dynamic and thermodynamic fields. Moreover, our findings are significant because they provide further observation-based knowledge of the feedbacks between the Earth’s surface and atmosphere in middle-latitude locations that have seen significant human modifications to the landscape (e.g., deforestation for intensive agriculture). This type of information is crucial as climate continues to change on regional and global scales: land-cover modifications may effect regional and local climate changes comparable to those driven by increased global emissions of greenhouse gases (Pielke et al., 2002). Future empirical and modeling research should continue to examine multiple synoptic flow types for other subregions within the Midwest at synoptic and mesoscales to afford further insights into the mechanisms of warm-season climate variations for the Midwest United States Corn Belt.”