Monthly Archives: December 2010

Additional Papers On The Role of Aerosols On Precipitation

Yesterday, we posted

Dev Niyogi has thoughtfully forwarded us other papers that show this important role of aerosols on precipitation. They are

 Khain, AP; Leung, LR; Lynn, B, et al., 2009:  Effects of aerosols on the dynamics and microphysics of squall lines simulated by spectral bin and bulk parameterization schemes   JGR-Atmospheres  Volume: 114 Article Number: D22203

Khain, A; Lynn, 2009: Simulation of a supercell storm in clean and dirty atmosphere using weather research and forecast model with spectral bin microphysics   JGR-Atmospheres Volume: 114 Article Number: D19209

Lynn, BH; Carlson, TN; Rosenzweig, C, et al. 2009: A Modification to the NOAH LSM to Simulate Heat Mitigation Strategies in the New York City Metropolitan Area     J. of Applied Meteorology and Climatology Volume: 48 Issue: 2 Pages: 199-216

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New Paper “Modeling Aerosol Impacts On Convective Storms In Different Environments” By Storer Et Al 2010

There is a new paper that examines the role of aerosols on thunderstorms (h/t to Dev Niyogi of Purdue). Aerosols, of course, have both natural and human sources. The latter include vehicular and industrial emissions, as well as from biomass burning and blowing dust from landscape degradation.

The paper is

Storer, Rachel L., Susan C. van den Heever, Graeme L. Stephens, 2010: Modeling Aerosol Impacts on Convective Storms in Different Environments. J. Atmos. Sci., 67, 3904–3915. doi: 10.1175/2010JAS3363.1

The abstract reads

“Aerosols are known to have both direct and indirect effects on clouds through their role as cloud condensation nuclei. This study examines the effects of differing aerosol concentrations on convective storms developing under different environments. The Regional Atmospheric Modeling System (RAMS), a cloud-resolving model with sophisticated microphysical and aerosol parameterization schemes, was used to achieve the goals of this study. A sounding that would produce deep convection was chosen and consistently modified to obtain a variety of [Convective Available Potential Energy] CAPE values. Additionally, the model was initiated with varying concentrations of aerosols that were available to act as cloud condensation nuclei. Each model run produced long-lived convective storms with similar storm development, but they differed slightly based on the initial conditions. Runs with higher initial CAPE values produced the strongest storms overall, with stronger updrafts and larger amounts of accumulated surface precipitation. Simulations initiated with larger concentrations of aerosols developed similar storm structures but showed some distinctive dynamical and microphysical changes because of aerosol indirect effects. Many of the changes seen because of varying aerosol concentrations were of either the same order or larger magnitude than those brought about by changing the convective environment.”

An excerpt from the conclusions reads

The results presented here have shown that differences both in the initial CAPE and in the available aerosol concentration will lead to important changes in both microphysical and dynamic properties of convective storms. The relative importance of these differences is something that has not previously been examined. For the range of CAPE (491–2828 J kg**-1) and aerosol concentrations (100–6400 [per] cm**3) examined here, the total precipitation produced by the storms is primarily driven by CAPE, but an increase in the concentration of available aerosols from 100 to 6400 [per] cm**3 leads to a decrease in total precipitation amount by 30%–40% (if considering only a 400% change in aerosol concentration to better compare with the range in CAPE, the decrease is still ~15%).

Since, for example, other climate forcings and feedbacks, such as landscape change and the effects variability and long-term change of atmospheric circulation features, alters both CAPE and aerosol composition, this is yet another complex interaction among the components of the climate system. To claim prediction skill for clouds and precipitation decades into the future fails to account for the actual real world difficulty in skillfully simulating these complex interactions as well as how they will change in the future.

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Further Comment On The Posts At Dot Earth On Judah Cohen’s Research

Andy Revkin has another thoughtful post titled

Putting a Siberian Snow Connection to the Test

on his informative weblog Dot Earth.

He requested my input on his post and I have reproduced it below [it is also in the Comments on Andy's post].

************************MY COMMENT*******************

 Judah Cohen wrote that “The best way to validate a scientific hypothesis is to make a successful prediction.” Actually, I would word this differently (although I agree with the sense of his statement).
 
A more precise statement, as I presented in my post
 
Hypothesis Testing – A Failure In The 2007 IPCC Reports
 
is
 
“The scientific method involves developing a hypothesis and then seeking to refute it. If all attempts to discredit the hypothesis fails, we start to accept the proposed theory as being an accurate description of how the real world works.”
 
I discussed the scientific process also in my posts
 
http://pielkeclimatesci.wordpress.com/2010/12/17/succinct-summary-of-the-scientific-process/
 
http://pielkeclimatesci.wordpress.com/2009/06/04/short-circuiting-the-scientific-process-a-serious-problem-in-the-climate-science-community/
 
I commend Judah for openly publishing his seasonal forecasts so they can be compared (tested) against real world data.
 
However, where he deviates from this scientifically rigorous approach is when he makes statements such as
 
“I have tried to stay focused on seasonal forecasting and not get distracted by global warming. The only comment I would say is, currently it is still cold enough in Siberia in the fall for precipitation to fall as snow. But if at some point temperatures warmed sufficiently that snow would fall as rain instead, then I think the lack of snow cover across Siberia in the fall could amplify winter warming. There are modeling studies looking at when this may happen, including the paper Dave Robinson cited, and it would be the latter half of this century. Also snow cover and the winter AO go through natural decadal cycles. If both went through a natural reversal in the upcoming years, I believe that we would experience much milder winters in the Eastern U.S. and Europe.”
 
His statements on the climate decades from now use conditional phrases; i.e. “if at some point temperatures warmed sufficiently that snow would fall as rain…” and  “If both went through a natural reversal in the upcoming years, I believe that we would experience much milder winters in the Eastern U.S. and Europe”.  His insistence to bring in these longer term (untestable for now) hypotheses dilutes his message on seasonal forecasts.
 
I do agree that enhanced snow cover can have a major effect on atmospheric circulation patterns. Heterogeneous diabatic heating is clearly a major under-recognized effect within the climate system, as we demonstrated in our paper (for aerosols)
 
Matsui, T., and R.A. Pielke Sr., 2006: Measurement-based estimation of the spatial gradient of aerosol radiative forcing. Geophys. Res. Letts., 33, L11813, doi:10.1029/2006GL025974. http://pielkeclimatesci.files.wordpress.com/2009/10/r-312.pdf.
 
The importance of these regional heating patterns was also reported in
 
National Research Council, 2005: Radiative forcing of climate change: Expanding the concept and addressing uncertainties. Committee on Radiative Forcing Effects on Climate Change, Climate Research Committee, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., 208 pp
 
where it is written
 
“regional variations in radiative forcing may have important regional and global climate implications that are not resolved by the concept of global mean radiative forcing.”

And furthermore:

“Regional diabatic heating can cause atmospheric teleconnections that influence regional climate thousands of kilometers away from the point of forcing.”

This regional diabatic heating produces temperature increases or decreases in the layer-averaged regional troposphere. This necessarily alters the regional pressure fields and thus the wind pattern. This pressure and wind pattern then affects the pressure and wind patterns at large distances from the region of the forcing which we refer to as teleconnections.

The seasonal work by Judah Cohen, based on Siberian snow cover, could be an an important new addition to our understanding of teleconnections in the climate system associated with regional variations in diabatic heating and cooling.

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Madhav Khandekar Guest Post On His Paper “Weather Extremes Of Summer 2010: Global Warming Or Natural Variability”

Madhav Khandekar has published the article

Weather Extremes Of Summer 2010: Global Warming Or Natural Variability”

Madhav provides a brief summary of this paper below.

Guest Post By Madhav Khandekar

In the paper from E&E 2010, I have surveyed the cold weather extremes as reported in various TV and news media and also from various weather & climate data archives. My preliminary assessment strongly suggests increasing frequency of cold weather extrems world-wide in the last five years or more. The increasing occurrences of cold weather extremes seem to contradict the AGW hypothesis and  IPCC 2007 Climate Change Documents which project ‘increasing Warm weather extremes in future’.  There is an urgent need to improve our database on weather extrems, warm & cold and to assess these events in the context of present climate change debate. The climate science community has over-emphasized calculation of regional and global temperature trend and their possible linkage to human-added carbon dioxide, while ignoring analysis of weather extremes and their mechanics and dynamics.
 
Are cold weather extremes impacting human societies worse than warm weather extremes? There is a need to do more research on this important socio-economic issue.
 
Madhav Khandekar

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Comment On The New York Times Op-Ed “Bundle Up, It’s Global Warming”

There is an opinion in the New York Times by Judah Cohen titled

Bundle Up, It’s Global Warming

The op-ed starts with the text

“The earth continues to get warmer, yet it’s feeling a lot colder outside. Over the past few weeks, subzero temperatures in Poland claimed 66 lives; snow arrived in Seattle well before the winter solstice, and fell heavily enough in Minneapolis to make the roof of the Metrodome collapse; and last week blizzards closed Europe’s busiest airports in London and Frankfurt for days, stranding holiday travelers. The snow and record cold have invaded the Eastern United States, with more bad weather predicted.

All of this cold was met with perfect comic timing by the release of a World Meteorological Organization report showing that 2010 will probably be among the three warmest years on record, and 2001 through 2010 the warmest decade on record.”

This article is yet another one that spins the recent cold extremes and winter storms (e.g. in Europe, the United States), to be consistent with annual global average warming.

I have several comments on this op-ed.

First, the author of this op-ed writes

“Annual cycles like El Niño/Southern Oscillation, solar variability and global ocean currents cannot account for recent winter cooling. And though it is well documented that the earth’s frozen areas are in retreat, evidence of thinning Arctic sea ice does not explain why the world’s major cities are having colder winters.”

This is incorrect. The El Niño/Southern Oscillation and other circulation features (such as the PDO, NAO etc) are NOT “annual cycles”. Moreover, as shown by Roy Spencer (e.g. see) these features can result in changes in the global average heat content ( by altering cloudiness).  Also, the recently relatively quiet Sun  (with its alteration of its shorter wavelengths of radiation -e. g. see at Watts Up With That) may be playing a role.

The author further writes

“But one phenomenon that may be significant is the way in which seasonal snow cover has continued to increase even as other frozen areas are shrinking. In the past two decades, snow cover has expanded across the high latitudes of the Northern Hemisphere, especially in Siberia, just north of a series of exceptionally high mountain ranges, including the Himalayas, the Tien Shan and the Altai.”

First, the statement that while the statement  “other frozen areas are shrinking”  is correct for Arctic sea ice, it is incorrect for Antarctic sea ice (e.g. see). Moreover, the statement that seasonal snow cover has increased conflicts with the 2007 IPCC WG1 Statement for Policymakers (SPM)   where they write

“Observed decreases in snow and ice extent are also consistent with warming……snow cover on average have declined in both hemispheres.”

Figure SPM.1c in the 2007 IPCC WG1 SPM illustrates this decline. Thus, the author of the op-ed reports on a major disagreement with the IPCC report, yet neglects to alert the reader to this difference.

The author continues

“As global temperatures have warmed and as Arctic sea ice has melted over the past two and a half decades, more moisture has become available to fall as snow over the continents. So the snow cover across Siberia in the fall has steadily increased.

The sun’s energy reflects off the bright white snow and escapes back out to space. As a result, the temperature cools.”

Even if we ignore whether atmospheric water vapor has really increased over this region during the last two and a half decades, the op-ed writer does not report that, if this effect of added snow cover in a warming world were real, it is a negative radiative feedback which the 2007 IPCC WG1 report (and multi-decadal global model projections) did not recognize.

Finally, he writes

“Most forecasts have failed to predict these colder winters, however, because the primary drivers in their models are the oceans, which have been warming even as winters have grown chillier. They have ignored the snow in Siberia. “

However, the oceans have not been warming in recent years (e.g. see the 2010 paper by Know and Douglas ). However, I agree that “[m]ost forecasts have failed”. Indeed, I would go further to state that ALL seasonal and longer time scale model predictions have failed to skillfully predict these extreme cold events. One of the reasons, of course, is that these models are unable to skillfully predict the development of large amplitude atmospheric circulations (such as blocking high and low pressure systems, called “Omega Blocks” and ‘Rex Blocks”).

Until, and unless, the multi-decadal global models can show skill in predicting these atmospheric features AND their change in frequency and patterning in the coming decades, they are misleading policymakers and others on their skill. This op-ed, despite seeking to support the 2007 WG1 IPCC perspective, actually raises further substantive issues with the robustness and accuracy of that report.

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Guest Post “The Continuing Recovery From The Little Ice Age” By Syun-Ichi Akasofu

The continuing recovery from the Little Ice Age

by Syun-Ichi Akasofu of the International Arctic Research Center, University of Alaska Fairbanks

[Dr. Syun-Ichi Akasofu, IARC Founding Director and Professor of Physics, Emeritus, was the the director of the International Arctic Research Center of the University of Alaska Fairbanks from its establishment in 1998 until January of 2007. He originally came to the University of Alaska Fairbanks in 1958 as a graduate student to study the aurora under Sydney Chapman, receiving his PhD in 1961. He has been professor of geophysics since 1964. Dr. Akasofu has published more than 550 professional journal articles, authored and co-authored 10 books and has been the invited author of many encyclopedia articles. He has collaborated with numerous colleagues nationally and internationally, and has guided nine students to their Ph.D. degrees.]

Guest Post

It is extremely difficult, if not impossible, to determine observationally the contribution of the natural component and the CO2 component in climate temperature changes during the last century. However, it may be possible to determine the contribution of those components by tracing back the past climate changes from 1000 to 2000. There is  abundant evidence to indicate that the Earth experienced a relatively cool period, of about -1°C, from ~1200 to ~1800, compared with the present.  This period is called the Little Ice Age (LIA).

The recovery from the LIA was not step function-like. A large number of data sets show that the recovery, namely the temperature rise, was approximately linear from 1800~1850 to 2000.

Thus the rate of temperature rise during the last 200 years is about +1°C/200 years,  + 0.5°C/100 years, which is about 5/6 of the rate of increase (+0.6°C/100 years) during the last century. Therefore, it may be inferred that the CO2 contribution was about +0.1°C during the last century. There was no obvious break in the rate of temperature rise during the last 150~200 years, so it is likely that the recovery from the LIA is continuing to the present. Sea level rise is approximately linear (1.7mm/year or less) from about 1850, to the present. The retreat of many glaciers in the world began from about 1800-1850, not after 1946, when CO2 began to increase rapidly. When discussing the present warming, we should not ignore the LIA and its recovery.

The only complication is that the multi-decadal oscillation, of the amplitude  ~0.2°C and a period of about 60 years, is superposed on the linear change. It peaked in about 1940 and 2000. After 1940, the temperature change was negative until 1975, so a similar negative trend may continue until about 2030. This may explain the present halting of the temperature rise. The inferred temperature increase by 2100 is about + 0.5°C±0.2°C, depending on the phase of the multi-decadal oscillation.

When does the recovery end ?  First of all, there is, unfortunately, no baseline to determine it. Both the Medieval Warm period and the LIA were relatively minor fluctuations during the last 10,000 years. Secondly, the cause of the LIA is not known. The cosmic-ray data (C14 and Be10) suggest that solar activity was relatively low from about 1200 to 1800 (including the Maunder Minimum) and started to increase after 1800. However, we are still uncertain about what changes of the Sun may cause cosmic-ray changes, and we do not know what intrinsic changes of the Sun cause climate change on the Earth. The attached figure summarizes the above discussion.

 

Figure Caption:  The figure shows that the linear trend between 1880 and 2000 is a continuation of the recovery from the LIA, together with the superposed multi-decadal oscillation. It is assumed that the recovery from the LIA would continue to 2100, together with the superposed multi-decadal oscillation.  This view could explain the halting of the warming after 2000.  The observed temperature in 2008 is shown by a red dot with a green arrow. It also shows the temperature rise after 2000 predicted by the IPCC. It has been suggested by the IPCC that the thick blue line portion was caused mostly by the greenhouse effect, so the IPCC’s future prediction is a sort of extension of the blue line.

For detail, please refer to my paper “ On the recovery from the Little Ice Age” in Natural Science, vol.2, No.11, 1211-1224 (2010): http://www.scirp.org/journal/NS/

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Comment On Judy Curry’s Post “Scenarios: 2010-2030. Part I”

Judy Curry at Climate Etc has another thoughtful and informative post titled

Scenarios: 2010-2030. Part I

In her post, she discusses (for this time scale)

“It is the combination of … natural variability and forced anthropogenic climate change that is of particular interest.   Natural variability dominates regional climate change in many locations.  Further, decision makers need to know the extent to which climate is varying because of natural variability, and hence expected to reverse at some point, or whether the climate is changing as the result of irreversible anthropogenic forcing.

So how should we approach this problem?  Is there predictability in the climate system on these timescales?  If so, how can this predictability be realized and converted into useful predictions?”

and writes

“The targets of interest on the timescale of the next two decades are

  • the evolution of the global average temperature anomaly, for understanding the relative roles of anthropogenic versus natural climate variability/change
  • the evolution of regional climate variability to support regional decision making: average temperature and precipitation; extreme events (heat/cold waves, floods, droughts, hurricanes, wildfires, etc).”

I would write these different in terms of what is needed by policymakers:

  • There are 5 broad areas that we can use to define the need for vulnerability assessments on all time scales : water, food, energy, health and ecosystem function. Each area has societally critical resources. There is a need to determine the vulnerability of these resources to the major threats to these resources from climate (e.g. for the next 20 years), but also from other social and environmental issues. After these threats are identified for each resource, then the relative risk from natural- and human-caused climate change (estimated from the global climate model multi-decadal projections, but also the historical, paleo-record and worst case sequences of events) can be compared with other risks in order to adopt the optimal mitigation/adaptation strategy (e.g. see)
  • Do the global climate multi-decadal projections results skillfully predict observationally documented CHANGES in the statistics (probabilities) of the different major circulation patterns, as well as their behaviour in coming years, and can these predictions be separated into a “natural” and a “human-caused’ component?
  • The global average surface temperature is an inadequate diagnostic for global warming and cooling. The skill of the global climate multi-decadal projections to predict the change in ocean heat content in Joules over the coming decade is a much more robust metric to assess global climate system heat changes (e.g see).

Judy further reports

Hoerling et al. (2010, submitted) has conducted an interesting set up experiments that makes predictions for the North American climate for 2011-2020:

North American mean surface air temperature and precipitation are predicted for the upcoming 2011-2020 decade. Multiple climate models forced by various plausible scenarios for the 2011-2020 change in ocean surface boundary conditions are first employed in order to estimate the forced response, and its uncertainty, to expected changes in anthropogenic forcing. A full probabilistic decadal forecast is then generated by commingling the statistics of the forced response with those arising from internal decadal sea surface temperature (SST) and sea ice variability. The latter are estimated from a multi-model suite of 20th Century atmospheric climate simulations driven by the observed time history of SST and sea ice variations.

The prediction is characterized by surface warming over the entire continent and precipitation decreases (increases) over the contiguous United States (Canada) relative to 1971-2000 conditions. The signs of these signals are robust across the scenarios and the models employed, though their amplitudes are not. An assessment of the sources of forecast uncertainty reveals comparable sensitivity to the various scenarios of forced SST change, model dependency, internal atmospheric noise, and internal decadal SST variability. Taking these sources of forecast uncertainty into account, predictions for the 2011-2020 decade indicate a 94% and 98% probability for warmer than normal conditions over the U.S. and Canada, respectively, a 99% probability of wet conditions over Canada, and a 75% probability of dry conditions over the U.S.”

These  forecasts are on a time period where we can actually compare with observations (2011 to 2020). This is worthwhile as these results are actually testable.

I agree with Judy where she writes

“The key challenge of multi-decadal climate forecasting is prediction of the change points (transitions) of the major ocean oscillations.   Again, based on my experience with probabilistic seasonal forecasting, the only way I see to do this potentially with any skill is to select the models that do a relatively good job at simulating the key features in hindcast mode, and then select the ensemble members from these models that compare best with observations for the first year or two of the simulation.  The rationale for such a selection is that ensemble members that get off to a good start are more likely to be on a good trajectory going forward.   I look forward to getting my hands on the CMIP5 simulations.”

I have discussed the value of these multi-decadal global model predictions in a comment on an article by Hurrell et al (2009)

in

Pielke Sr., R.A., 2010: Comment on “ A Unified Modeling Approach to Climate System Prediction“, Bull. Amer. Meteor. Soc., DOI:10.1175/2010BAMS2975., in press [it has taken BAMS well over a year to publish].

 My conclusion reads [and this applies to the Hoerling et al study that Judy refers to]

“Thus, although I commend the authors for starting to adopt a framework of climate modeling as an initial value problem, they are at serious risk of overselling what they will be able to provide to policy makers. A significant fraction of the funds they are seeking for prediction could more effectively be used if they were spent on assessing risk and ways to reduce the vulnerability of local/regional resources to climate variability and change and other environmental issues using the bottom-up, resources-based perspective discussed in Pielke and Bravo de Guenni (2004), Pielke (2004), and Pielke et al. (2009). This bottom-up focus is “of critical interest to society.”

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