Honorary Doctorate For Roger A. Pielke Jr. From Linköping University

I am proud to announce to the readers of this weblog that my son has been awarded an honorary doctorate from Linköping University in Sweden. This high level recognition was announced yesterday in the Daily Camera in an article by Brittany Annas;

Roger Pielke Jr., CU-Boulder climate scientist, receives honorary doctorate from Swedish university

The news article reads in part

Linkoping, in awarding the degree, wrote that Pielke’s “outstanding achievement in interdisciplinary climate research is a bold and refreshing voice in the climate debate.”

This is very well deserved!!!

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Question And Answer On The Value Of Dynamic Downscaling For Multi-Decadal Predictions

In response to our article

Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum,  93, No. 5, 52-53, doi:10.1029/2012EO050008.

and their paper

Xu, Zhongfeng and Zong-Liang Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. Journal of Climate 2012 doi: http://dx.doi.org/10.1175/JCLI-D-12-00005.1

and my weblog post

Comments On The New Paper “An Improved Dynamical Downscaling Method With GCM Bias Corrections And Its Validation With 30 years Of Climate Simulations” By Xu and Yang 2012

I have had the following informative and constructive interaction with Zong-Liang Yang of the University of Texas in Austin and Zhongfeng Xu of the Institute of Atmospheric Physics of the Chinese Academy of Sciences. My summary comment is at the send of the post.

Comment By Liang and Zhongfeng

“A more accurate regional projection of future climate is always useful for making decisions even the policymakers do not require them. In this paper we are trying to improve regional projections of future climate. This does not mean IDD can produce a 100% accurate projection. As a matter of fact, it is impossible to make a 100% accurate projection. However we still can make climate projections better. Our result shows IDD can better simulate the future climate mean states and extreme events than TDD.”

My Response:

I disagree. What you have shown are systematic errors in regional model simulations using a set of hindcast runs, which can be used to correct (adjust) simulation results for another subset. This is analogous to the Method of Model Output Statistics [MOS] and is certainly an appropriate approach. However, in a future run (after 2012), you are running the regional climate model with lateral boundary conditions and (interior nudging if used) which will have a different climatology of input. Those lateral boundary conditions will have systematic biases too, but likely will be different (but as you say there is no way to correct for from
observations).

Comment By Liang and Zhongfeng

Yes, there is no way to correct GCM biases in simulating climate change but we still can correct GCM systemic biases in simulating climatological means. A detailed discussion is as follows.

In the paper, we are trying to correct the systematic biases (which by no means represent all errors but only the biases in climatological mean and variance) in the GCM to prevent these systematic biases from being passed into the RCM through the LBC.

There are different biases between GCM simulations and the NCEP reanalysis, which can be reflected in the phase of interannual variations (e.g. the GCM simulates a positive anomaly but the NCEP shows a negative anomaly in a individual year), climate change (e.g. the GCM simulates a 0.1C/decade warming trend but the NCEP shows a 0.2C/decade warming trend), the climatological mean and the variance, and so on and so forth. Our bias correction method only corrects the climatological mean bias and variance bias because these are the GCM systemic biases and they usually do not change too much with time. Please refer to Fig. 1 in the paper. The differences in mean and variance between NNRP and the original GCM simulation do not change too much during the period of 1980-2010. We believed the conclusion remains the same if we plot the figure from 1950-2010. This means the GCM climatological mean bias and variance bias are generally time-independent. These biases could result from some parameterization schemes or something else. If a parameterization scheme tends to produce a negative bias in the current climate, then we have reason to believe it will produce a negative bias in the future climate. It is this type of biases we can correct. With regard to the biases such as the phase of interannual variation and climate change from the past to the future, we do not correct them because we do not know what would happen in the future. But we have the confidence to correct the climatological mean bias and the variance bias in the GCM. The GCM climatological mean bias over the future period is composed of the GCM systemic bias over the past period and the climate change bias (from the past to the future). While we only correct the former one in our current paper, our results show that the GCM climatological mean and variance bias corrections lead to a better downscaled mean climate and extreme event statistics relative to the traditional dynamical downscaling approach (TDD).

We would like to explain the GCM bias correction further through the following figure.

The black solid line represents the NNRP data – The blue solid line represents the original GCM simulation -The red solid line represents the bias corrected GCM simulation- The dotted lines represent the climatological means over the past (green shaded area) and the future periods, respectively.

The bias correction method proposed in the paper involves shifting (i.e., removing the climatological mean bias) and scaling (i.e., removing the variance bias) the original GCM simulation at each model grid. As we can see, the bias-corrected GCM has the same climatological mean as NNRP over the past period (red and black dotted line on the left). However, their climatological means may be different over the future period (red and black dotted line on the right). This difference results from the GCM biases in the climate change simulation (i.e. the difference between the past and the future mean climate), which can not be removed by our GCM bias correction method. Even so, for the future period, the difference between the red dotted line and the black dotted line is still smaller than the difference between the blue dotted line and the black dotted line because the GCM systemic bias in climatological mean has been removed. This means the bias corrected GCM is closer to NNRP over the “future” period than the original GCM simulation does. The improvement is mainly due to the GCM climatological mean bias correction. Note that the bias correction to the future GCM simulation does NOT need the NNRP data over the “future” period.

My Comment:

An even more serious issue is that simulating hindcast regional climate statistics is just one requirement. The regional climate model to have value beyond reanalyses [as well as for straightforward interpolation of the global model projection results onto a finer terrain and landscape map], must skillfully predict CHANGES in the regional climate statistics. This was not done in your paper.

Comment By Liang and Zhongfeng

We did not analyze the CHANGES simulated by the regional models because the GCM bias correction does not correct the CHANGES bias simulated by GCM. So it seems no reason we expect IDD can produce better CHANGES projection than TDD.

“In our study, we have 63-year NNRP data (1948-2010) and we also run CAM over the same period. Then we divided the 63-year into two periods: the ‘past’ (1948-1979) and the ‘future’ (1980-2010). We correct CAM biases of the future simulation based on the CAM past simulation and NNRP past data. In other words, we do NOT need future observations when correcting CAM future biases. The bias corrected CAM future simulation was used to drive WRF (i.e. the IDD experiment). The IDD experiment was compared with the WRF run driven by NNRP future data to assess the performance of IDD in simulating the future climate. In addition, the CAM bias corrections only correct the CAM climatological mean bias and the variance bias. The bias correction method retains the CAM simulated climate change from the past to the future plus the phase of interannual variation. We assume that the CAM systemic biases do not change over time when correcting CAM biases.

For climate projections, the future climate change may be more important than the future climatological means. Unfortunately, the CAM bias correction method can not correct CAM biases in simulating future climate change. Even so the IDD is still better than the TDD in regional projection of future climate.”

My Comment

If, as you say,

“Unfortunately, the CAM bias correction method can not correct CAM biases in simulating future climate change.”

what is the value of the approach in this context? That is the crux as to why they are needed by the impacts communities. How can it be presented as skillful?

Comment By Liang and Zhongfeng

 In terms of the value of the IDD we think there are at least two applications in which we can expect better downscaled climate: (1) regional projections of future climate; (2) sensitivity studies.

(1) Regional projections of future climate: As the paper showed, IDD is able to produce better climatological means and extreme event statistics relative to TDD although the GCM bias correction method can not remove all biases in GCM. Therefore IDD is able to provide more useful information than TDD in the impacts studies. Of course people care about the climate change more than the climatological means. However the climate change prediction is a very challenging and complicated issue. If someone can significantly improve the climate change prediction that would be a great contribution to the science community. We did not find a good way to improve the future climate change projection yet.

(2) Sensitivity studies: IDD can also be applied to downscale GCM sensitivity simulations. In this case the GCM bias correction should be considered in a different way. Namely the GCM control run is corresponding to the “past GCM simulation” in the paper and the GCM sensitivity run is corresponding to the “future GCM simulation” in the paper. The difference between the GCM control run and the sensitivity run is corresponding the “climate change (from the past to the future) simulated by GCM” in the paper. In this way the difference between various GCM simulations (control run and sensitivity run) can be retained and passed into RCM, and further impact the downscaled simulations.

My Comment

As I wrote in response to the first comment, the “future. (1980-2010)” must be able to predict that part of the results which involve CHANGES in the regional climate statistics. What fraction of your results “past” (1948-1979) and the “future” (1980-2010) involve changes in the regional statistics, and how well are they replicated based on the reanalyses?

Comment By Liang and Zhongfeng

We assess the IDD performance by comparing GCM-driven WRF simulation with reanalysis drive-simulation rather than comparing with NCEP reanalysis because they are in different resolutions. WRF simulation: 60km; NCEP2 reanalysis: 2.5 degree. We did not compare GCM-driven WRF simulation with NARR, either, because we want to isolate LBC influences. The difference between GCM-driven WRF simulation and NARR results from both the GCM and RCM biases. Two biases could appear in same sign or opposite sign. The opposite sign biases would offset each other then produces a correct simulation for the wrong reasons.

Except the increasing CO2 concentration in RCM, the climate change in RCM mainly comes from GCM in our simulation. So the climate changes in RCM strongly depends GCM simulation. If GCM is able to produce a good climate change simulation, the RCM is supposed to do a better job, too, vice versa.

We did not analyze the climate CHANGES in the regional statistics. We guess that the performance of IDD in simulating climate CHANGES is similar with TDD.

My Comment

On your comment

“In our paper, we corrected CAM biases of atmospheric variables such as air temperature and geopotential height. The bias correction method can also be applied to Type 4 dynamical downscaling only with SST bias correction being included as well.”

It would be valuable for you to expand on this approach, as I really feel your methodology has its real power for type 3 downscaling (i.e. seasonal prediction).

My Comment:

Since the global model is not adjusted outside the domain of the regional climate model, the systematic biases are continually being fed into the regional model in the future scenarios. How do you feel you handle this continual insertion of what are actually errors?”

Comment By Liang and Zhongfeng

We corrected GCM biases at each global model grid (including the areas both inside and outside the domain of regional climate model as well as the boundary regions of RCM). However, only the GCM data over the RCM boundary was used in the dynamical downscaling run since the GCM data was used as the lateral boundary condition(LBC) of RCM. The GCM data outside and inside the RCM domain do not impact RCM simulation. In future study, we will further employ spectral nudging in WRF. By doing this the bias corrected GCM data  inside the RCM domain will be fed into RCM as well. Hopefully the spectral nudging will reduce the RCM system biases in future climate dynamical downscaling. The combination of GCM bias correction and spectral nudging are expected to reduce both the GCM bias and RCM bias and in turn produce a downscaled simulation closer to observation. The numerical simulations with GCM bias correction and nudging had been finished and we are going to work on them shortly. Hope receive your comments as well in future.

Liang: please correct me if any response are wrong or you have different opinions.

My Final Comment

This is a very informative discussion by two outstanding climate scientists. Their method of adjusting for systematic biases in the global models is an important scientific contribution.

• It first shows the level of error in the global  models even for the current climate.
• It also provides a method to improve on long-term model predictions, particularly on the seasonal time scale.

However, in terms of multi-decadal climate projections (predictions), their results show that they are not adding value. They wrote that they do “not analyze the climate CHANGES in the regional statistics. We guess that the performance of IDD in simulating climate CHANGES is similar with TDD.”  While they list above under #1 that they are providing “regional projections of future climate”, if the model’s cannot be shown to accurately predict CHANGES in climate statistics,  they are not providing skillful projections to the impacts community.

Indeed, the impacts community should just go directly to the reanalyses for their climate statistics. They could insert arbitrary perturbations in that weather data (e.g. add 1C to summer temperatures, reduce summer rainfall by 10%, ect) in order to assess risk to the resource of interest to them.  Using model predictions for decades from now, which have no demonstrated skill at predicting changes in regional climate statistics, is misleading policymakers.

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Comment On Andy Revkin’s Post On May 11 2012 Titled “Varied Views On Extreme Weather In A Warming Climate”

On Andy Revkin’s weblog Dot Earth, he presented the viewpoints of two well-respected scientists in his post

Varied Views on Extreme Weather in a Warming Climate

I have reproduced them below followed by my comment

From Martin Hoerling:

In his recent New York Times Op-Ed piece, Jim Hansen asserts:

“Over the next several decades, the Western United States and the semi-arid region from North Dakota to Texas will develop semi-permanent drought, with rain, when it does come, occurring in extreme events with heavy flooding. Economic losses would be incalculable. More and more of the Midwest would be a dust bowl. California’s Central Valley could no longer be irrigated. Food prices would rise to unprecedented levels.”

He doesn’t define “several decades,” but a reasonable assumption is that he refers to a period from today through mid-century. I am unaware of any projection for “semi-permanent” drought in this time frame over the expansive region of the Central Great Plains. He implies the drought will be due to a lack of rain (except for the brief, and ineffective downpours). I am unaware of indications, from model projections, for a material decline in mean rainfall. Indeed, that region has seen a general increase in rainfall over the long term during most seasons (certainly no material decline). Also, for the warm season when evaporative loss is especially effective, the climate of the central Great Plains has not become materially warmer (perhaps even cooled) since 1900. In other words, climate conditions in the growing season of the Central Great Plains are today not materially different from those existing 100 years ago. This observational fact belies the expectations from climate simulations and, in truth, our science lacks a good explanation for this discrepancy.

The Hansen piece is policy more than it is science, to be sure, and one can read it for the former. But facts should, and do, matter to some. The vision of a Midwest Dustbowl is a scary one, and the author appears intent to instill fear rather than reason.

The article makes these additional assertions:

“The global warming signal is now louder than the noise of random weather…”

This is patently false. Take temperature over the U.S. as an example. The variability of daily temperature over the U.S. is much larger than the anthropogenic warming signal at the time scales of local weather. Depending on season and location, the disparity is at least a factor of 5 to 10.

I think that a more scientifically justifiable statement, at least for the U.S. and extratropical land areas is that daily weather noise continues to drum out the siren call of climate change on local, weather scales.

Hansen goes on to assert that:

“Extremely hot summers have increased noticeably. We can say with high confidence that the recent heat waves in Texas and Russia, and the one in Europe in 2003, which killed tens of thousands, were not natural events — they were caused by human-induced climate change.”

Published scientific studies on the Russian heat wave indicate this claim to be false. Our own study on the Texas heat wave and drought, submitted this week to the Journal of Climate, likewise shows that that event was not caused by human-induced climate change. These are not de novo events, but upon scientific scrutiny, one finds both the Russian and Texas extreme events to be part of the physics of what has driven variability in those regions over the past century. This is not to say that climate change didn’t contribute to those cases, but their intensity owes to natural, not human, causes.

The closing comment by Hansen is then all the more ironic, though not surprising knowing he often writes from passion and not reason:

“The science of the situation is clear — it’s time for the politics to follow. ”

Let me borrow from a recent excellent piece in New Scientist by tornado expert Dr. Harold Brooks regarding the global warming and tornado debate, and state:

“Those who continue to talk in certain terms of how local weather extremes are the result of human climate change are failing to heed all the available evidence.”

From Kerry Emanuel:

I see overstatements on all sides. Extreme weather begets extreme views. On the Russian heat wave, Marty is citing a single paper that claims it had nothing to do with climate change, but there are other papers that purport to demonstrate that events of that magnitude are now three times more likely than before the industrial era.

This is a collision between the fledgling application of the science of extremes and the inexperience we all have in conveying what we do know about this to the public. A complicating factor is the human psychological need to ascribe every unusual event to a cause. Our Puritan forebears ascribed them to sin, while in the 80’s is was fashionable to blame unusual weather on El Niño. Global warming is the latest whipping boy. But even conveying our level of ignorance is hard: Marty’s quotation of Harold Brooks makes it sound as though he is saying that the recent uptick in severe weather had nothing to do with climate change. The truth is that we do not know whether it did or did not; absence of evidence is not evidence of absence.

Andy wrote

Regular readers of my work will not be surprised that I align with Emanuel.

My Comment:  Andy Revkin (and Kerry Emmanuel) have made the error of seeming to assume that one can proof a negative.  Kerry wrote

The truth is that we do not know whether it did or did not; absence of evidence is not evidence of absence.

I have a lot of respect for Kerry (and for Andy) but they are in error in terms of the scientific method. To illustrate (and this example applies to the other extreme events), Martin Hoerling’s text on heat waves could be summarized as a hypothesis:

 Human caused changes in heat waves resulting from the addition of CO2 into the atmosphere have not been shown using real world observational data.

The scientific method requires presenting analyses that refute this hypothesis. Kerry wrote [regarding heat waves]

“….there are other papers that purport to demonstrate that events of that magnitude are now three times more likely than before the industrial era.”

with the implication, presumably, that by mentioning the “industrial era” he means the effect on climate of added CO2. However. Kerry provided no citations, and Andy accepted this view without questioning this. At the very least, Kerry should have cited papers that claim to refute the hypothesis that I presented above.

In terms of heat waves and lower tropospheric temperature anomalies, we have published on this issue in our papers

Chase, T.N., K. Wolter, R.A. Pielke Sr., and Ichtiaque Rasool, 2006: Was  the 2003 European summer heat wave unusual in a global context? Geophys.  Res. Lett.,  33, L23709, doi:10.1029/2006GL027470. http://pielkeclimatesci.files.wordpress.com/2009/10/r-310.pdf

Chase, T.N., K. Wolter, R.A. Pielke Sr., and Ichtiaque Rasool, 2008: Reply to comment by W.M. Connolley on ‘‘Was the 2003 European summer heat wave unusual in a global context?’’Geophys.  Res. Lett., 35, L02704, doi:10.1029/2007GL031574.

whose findings were confirmed in

Connolley  W.M. 2008: Comment on  “Was  the 2003 European summer heat wave unusual in a global context?” by Thomas N. Chase et al. Geophys.  Res. Lett., 35, L02703, doi:10.1029/2007GL031171.

In our Chase et al 2006 study we did report on an upward trend in the number of heat waves as measured from tropospheric temperature anomalies, but we concluded that

“….the increased probability of such extremes with time suggested by Stott et al. [2004] is not yet apparent.”

We concluded that the extreme heat in Europe in 2003 was amplified by precedent and concurrent drought conditions which resulted in even higher temperatures than would have occurred with the same tropospheric temperature anomalies (as a result of less evaporation and transpiration from the surface which would have reduced the warmth both from this loss of sensible heating and from more cloudiness).  If added CO2 were the main reason for the heat wave, it would have been at least as unusual in the lower tropsphere, but it was not.

A clear signal, of course, may emerge in the coming years, but, for now at least. both Kerry and Andy have not refuted the hypothesis. 

Human caused changes in heat waves resulting from the addition of CO2 into the atmosphere have not been shown using real world observational data

Kerry’s statement that the

absence of evidence is not evidence of absence.

is embodying a fallacy where from the Merriam-Webster Dictionary

it’s fallacious to say that something must exist because science hasn’t proven its nonexistence

Kerry and Andy are misleading readers when they make the statement that “absence of evidence is not evidence of absence.”

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Visual Evidence Of Abrupt Shifts Of Climate On Decadal Time Scales

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In 2004, the following paper was published

Seidel, D. J., and J. R. Lanzante (2004), An assessment of three alternatives to linear trends for characterizing global atmospheric temperature changes, J. Geophys. Res., 109, D14108, doi:10.1029/2003JD004414.

The abstract reads [highlight added]

Historical changes in global atmospheric temperature are typically estimated using simple linear trends. This paper considers three alternative simple statistical models, each involving breakpoints (abrupt changes): a flat steps model, in which all changes occur abruptly; a piecewise linear model; and a sloped steps model, incorporating both abrupt changes and slopes during the periods between breakpoints. First- and second-order autoregressive models are used in combination with each of the above. Goodness of fit of the models is evaluated using the Schwarz Bayesian Information Criterion. These models are applied to the instrumental record of global monthly temperature anomalies at the surface and to the radiosonde and satellite records for the troposphere and stratosphere. The alternative models often provide a better fit to the observations than the simple linear model. Typically the two top-performing models have very close values of the Schwarz Bayesian Information Criterion. Usually the two models have the same basic form and the same net temperature change but with a different choice of autoregressive model. However, in some cases the best fits are from two different basic models, yielding different net temperature changes and suggesting different interpretations of the nature of those changes. For the surface data during 1900–2002 the sloped steps and piecewise linear models offer the best fits. Results for tropospheric data suggest that it is reasonable to consider most of the warming during 1958–2001 to have occurred at the time of the abrupt climate regime shift in 1977. Two fundamentally different, but equally valid, descriptions of stratospheric cooling were found: gradual linear change versus more abrupt ratcheting down of temperature concentrated in postvolcanic periods (∼2 years after eruption). Because models incorporating abrupt changes can be as explanatory as simple linear trends, we suggest consideration of these alternatives in climate change detection and attribution studies.

The significance of this paper seems to have been missed in the discussion of long term trends in climate metric trends, including the posts on Tamino by Grant Foster and Skeptical Science by dana1981.  The assessment of shorter term abrupt changes was the intent of my post on trends in Arctic sea ice area.

As we wrote in

Rial, J., R.A. Pielke Sr., M. Beniston, M. Claussen, J. Canadell, P. Cox,  H. Held, N. de Noblet-Ducoudre, R. Prinn, J. Reynolds, and J.D. Salas,  2004: Nonlinearities, feedbacks and critical thresholds within the Earth’s  climate system. Climatic Change, 65, 11-38.

The Earth’s climate system is highly nonlinear: inputs and outputs are not proportional, change is often episodic and abrupt, rather than slow and gradual, and multiple equilibria are the norm.

I present below several other climate metrics which appear (using the eyecrometer) to have an abrupt [step] change in the trends. They also seem to be a long enough change such that they would be statistically significant, but will let others examine that. The Arctic sea area ice trend may be just a short deviation from a longer term decline, for example, or a significant abrupt step, but I now agree the time since a possible shift is too short to know which is correct.

For the fields with a longer record since an apparent shift:

1. The first field to show is the lower tropospheric temperature anomalies over time from the University of Alabama at Huntsville and from Remote Sensing Systems.

While a linear trend is plotted on the bottom figure, the eyecrometer indicates a change to a flatter trend (if there is any trend at all] after 1998. The lower tropospheric anomalies are clearly (even by eye) warmer than earlier in the record. But after about 12 years ago, there is no obvious slope.

2. The northern hemisphere anomaly plot from the Rutgers Snow Lab shows a similar abrupt change, but this time about 1988.

It does not require a quantitative statistical analysis to see that the snow cover anomalies are less than the values before 1988 but have been ~flat since then.

By month there are also abrupt appearing changes. For example, in Aprils from the Rutgers Snow Lab there is shift to mostly below average anomalies in 1988.

However, for Decembers from the Rutgers Snow Lab, there no such abrupt change, and indeed, there is less variation in the last two decades and as well as somewhat higher values.

3. The Antarctic sea ice from the Cryosphere Today  seems to indicate a jump to higher values in about 1995 [although not as clearly as in the other figures above].

Each of this “steps” are seen visually but need quantitative statistical testing to see if they are real. [Note: the weblog Jonova also has a post on such "steps" in the data]

“Global Warming Or Not? The Relative Roles Of Externally Forced Versus Internally Generated Decadal Climate ” By Gerald A. Meehl, NCAR

Seminar Announcement

I am pleased that the role of internal climate dynamics in climate system heat changes (and other aspects of the climate system) is finally becoming better appreciated. This movement towards a broader view is ot be presented in the seminar below [h/t to Jason English]. Note that the talk will be live webcast at http://www.fin.ucar.edu/it/mms/ml-live.htm. This abstract is an important new contribution to our understanding of how the climate system works [highlight added]

Global warming or not? The relative roles of externally forced versus internally generated decadal climate “

Tuesday, 15 May, 2012
3:30 p.m. Mesa Lab, Main Seminar Room
NCAR, 1850 Table Mesa Drive

The last decade has been marked by very little globally averaged warming trend. This has led some to conclude that global warming has stopped for good. However, there have been previous decades when there was little or no global temperature increase set against the background of a longer term warming trend. This raises the issue of the relative roles of internally generated decadal climate variability and externally forced climate system response. The mid-1970s climate shift is given as an example of globally averaged temperatures dramatically rising after a period of little warming. Results show that this shift was part internally generated, related to a transition from a negative to positive phase of the Interdecadal Pacific Oscillation (IPO), and part externally forced by increasing greenhouse gases (GHGs) with a possible contribution from solar forcing. To better understand the recent period of little warming, 21st century simulations with CCSM4 are analyzed to show that during decades of slightly negative global temperature trend, the IPO is in a negative phase, along with reductions of Antarctic Bottom Water (AABW) formation and a weaker Atlantic Meridional Overturning Circulation (AMOC). Thus, during hiatus decades, the excess heat being trapped in the system due to increasing GHGs goes into the deep ocean. Conversely, decades in the model when there is an unusually large positive global temperature trend show the opposite response. This highlights the importance of understanding relative contributions of external forcing and internally generated variability for the decadal climate prediction problem.

My Comments:

• First, if  ”excess heat [is] being trapped in the system due to increasing GHGs…. into the deep ocean” this heat may be sequestered there indefinitely. It is not clear how such heat could be quickly transferred back into the atmosphere.
• Moreover, what is the magnitude of the flux of heat through the upper 700m of the ocean in the model, and can this be seen in the real world observations?
• Finally, this is yet another example of why the use of the global average surface temperature trend (which does not sample this heat when it is used to diagnose global warming) is a very inadequate metric for this purpose.

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Research Paper “Cleaner Air Brings Better Views, More Sunshine And Warmer Summer Days In The Netherlands” By van Beelen And Van Delden 2012

We have been alerted by Jos de Laat to a new article

van Beelen, Aldert J. and Aarnout J. van Delden. Institute for Marine and Atmospheric Research Utrecht, Utrecht University, The Netherlands; 2012: Cleaner air brings better views, more sunshine and warmer summer days in the Netherlands. Weather. January 2012. Vol 67 No 1

Excerpts from the paper read [highlight added]

Here we analyse the trends in the frequency of days with high visibility at Schiphol (the main airport in the Netherlands, at 52°18!N and 4°46!E) and at De Bilt (the site of the Royal Netherlands Meteorological Institute, the KNMI, at 52°6!N and 5°11!E) (Figure 2). These stations are roughly 45km apart: Schiphol is about 20km, and De Bilt about 60km, from the sea. Reliable measurements of daily maximum visibility at both stations are available since 1955.

Figure 8 demonstrates that the clearing of the atmosphere is occurring in summer only during daytime. Visibility has changed relatively most strongly in the morning. Visibility has hardly changed during the night, probably because of the competing effect of increasing relative humidity (which, again, is much more important at night than during daytime).

All these changes during daytime are leading to an increase in surface solar radiation. This is confirmed by the measurements of global short-wave radiation at De Bilt, which show that this has steadily increased in summer but changed little in winter. It is likely that this effect is responsible for a significant part of the daytime upward temperature trend in summer, which is reflected also in an accelerated increase of the yearly average temperature after 1985. Nevertheless, we should not jump to conclusions too easily. Apparent agreement between trends does not imply causality. Possible causal links can only be identified by a model study in combination with an analysis of observations.

The conclusion reads

A major clearing of the air has occurred in the Netherlands in the past few decades. These changes are so large that they have become very obvious when looking at the data of individual stations. Strong indications can be found linking human emissions of aerosols to the visibility changes. Coincident with the visibility changes, large trends in cloud cover, sunshine duration and temperature are found, in particular during daytime in summer, showing that these tiny particles might have a significant influence on regional climate.

In addition to documenting the benefit of cleaning up regionally emitted particulate and gas emissions, this analysis suggests that a significant fraction of daytime warming that has been attributed to “global warming” may actually be due to the reduction of aerosols overhead.

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Global Temperature Report: April 2012 From The University Of Alabama At Huntsville

Philip Gentry has provided us with the April 2012 University of Alabama at Huntsville Global Temperature Report:

 

Global climate trend since Nov. 16, 1978: +0.13 C per decade

April temperatures (preliminary)

Global composite temp.: +0.30 C (about 0.54 degrees Fahrenheit) above 30-year average for April.

Northern Hemisphere: +0.41 C (about 0.74 degrees Fahrenheit) above 30-year average for April.

Southern Hemisphere: +0.18 C (about 0.32 degrees Fahrenheit) above 30-year average for April.

Tropics: -0.12 C (about 0.22 degrees Fahrenheit) below 30-year average for April.

March temperatures (revised):

Global Composite: +0.11 C above 30-year average

Northern Hemisphere: +0.13 C below 30-year average

Southern Hemisphere: +0.09 C below 30-year average

Tropics: -0.11 C below 30-year average

(All temperature anomalies are based on a 30-year average (1981-2010) for the month reported.)

Notes on data released May 10, 2012:

Spring brought somewhat more seasonal temperatures to the continental U.S., although it was still warmer than seasonal norms in April, according to Dr. John Christy, a professor of atmospheric science and director of the Earth System Science Center at The University of Alabama in Huntsville. Temperatures over the contiguous 48 states averaged 1.49 C (about 2.7 degrees Fahrenheit) warmer than seasonal norms in April, making it the fifth warmest April in the 33-year satellite climate record. That was cooler than the record-setting 2.82 C (almost 5.1 degrees Fahrenheit) anomaly in March.

April 2012 was the fourth warmest April in the temperature record both globally and in the Northern Hemisphere. It was the warmest April in 33 years for the Northern Extra Tropics — everything from 20 degrees North all the way to the North Pole. Average temperatures there for the month were 0.73 C (1.3 degrees F) warmer than seasonal norms.

The warmest and coolest spots on the globe show up as adjacent spots on the global map: Air over the Norwegian Sea was as much as 3.1 C (5.6 F) cooler than seasonal norms, while a large region of warmer than normal air over Europe peaked over Kazakhstan with temperatures as much as 5.92 C (10.66 F) warmer than seasonal norms.

Archived color maps of local temperature anomalies are available on-line at:

http://nsstc.uah.edu/climate/

The processed temperature data is available on-line at:

vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt

As part of an ongoing joint project between UAHuntsville, NOAA and NASA, John Christy, a professor of atmospheric science and director of the Earth System Science Center (ESSC) at The University of Alabama in Huntsville, and Dr. Roy Spencer, an ESSC principal scientist, use data gathered by advanced microwave sounding units on NOAA and NASA satellites to get accurate temperature readings for almost all regions of the Earth. This includes remote desert, ocean and rain forest areas where reliable climate data are not otherwise available.

The satellite-based instruments measure the temperature of the atmosphere from the surface up to an altitude of about eight kilometers above sea level. Once the monthly temperature data is collected and processed, it is placed in a “public” computer file for immediate access by atmospheric scientists in the U.S. and abroad.

Neither Christy nor Spencer receives any research support or funding from oil, coal or industrial companies or organizations, or from any private or special interest groups. All of their climate research funding comes from federal and state grants or contracts.

Jim Hansen’s 1981 Model Prediction Needs Scrutiny

source of image from Levitus et al 2012

Today in the New York Times [h/t to Watts Up With That, there is an op-ed by Jim Hansen of NASA GISS titled

Game Over for the Climate

where Jim writes

The global warming signal is now louder than the noise of random weather, as I predicted would happen by now in the journal Science in 1981.

I have posted on his prediction in

Comment On Ocean Heat Content “World Ocean Heat Content And Thermosteric Sea Level Change (0-2000), 1955-2010″ By Levitus Et Al 2012

Comments On The Poor Post “Lessons from Past Predictions: Hansen 1981″ By Dana1981 At The Skeptical Science

As reported in the Levitus et al 2012 paper [highlight added]

We provide updated estimates of the change of heat content  for 1955-2010]….. of the 0-700 m layer…..The heat content of the world ocean for the 0-700 m layer increased by 16.7×1022 J corresponding to a rate of 0.27 Wm-2 (per unit area of the world ocean).

Jim Hansen wrote in 2005 in a comment he sent me that

Our simulated 1993-2003 heat storage rate was 0.6 W/m2 in the upper 750 m of the ocean.

This is a discrepancy of ~2 between his prediction and the analysis of Levitus et al 2012 if the latter observational analysis is correct.

He is correct that the climate system has warmed. However, he  is significantly overstating its magnitude. While one possibility is that the rate increased after 1993 compared to earlier in the 1955-2010 period, but visually (using the eyecrometer) this does not seem to be the case. Moreover, the Levitus et al 2012 may be overstating the magnitude of recent upper ocean heating as clearly seen in the figure below from NOAA’s Pacific Marine Environmental Laboratory

Lets see if Jim, Gavin Schmidt, or other weblogs that communicate his viewpoint, such as Grant Foster at Tamino and Skeptical Science, respond to this observational study that illustrates a substantive disagreement with the climate model prediction of global warming.  So far they have ignore this disparity between the real world and the models.

Paper “Vertical Structure Of Recent Arctic Warming From Observed Data And Reanalysis Products” By Alezeev Et al 2011

Erik has alerted us to another paper. It is

Vladimir A. Alexeev, Igor Esau, Igor V. Polyakov,  Sarah J. Byam and Svetlana Sorokina, 2011: Vertical structure of recent arctic warming from observed data and reanalysis products. Climatic Change DOI 10.1007/s10584-011-0192-8

The abstract reads [highlight added]

Spatiotemporal patterns of recent (1979–2008) air temperature trends are evaluated using three reanalysis datasets and radiosonde data. Our analysis demonstrates large discrepancies between the reanalysis datasets, possibly due to differences in the data assimilation procedures as well as sparseness and inhomogeneity of high-latitude observations. We test the robustness of arctic tropospheric warming based on the ERA-40 dataset. ERA-40 Arctic atmosphere temperatures tend to be closer to the observed ones in terms of root mean square error compared to other reanalysis products used in the article. However, changes in the ERA-40 data assimilation procedure produce unphysical jumps in atmospheric temperatures, which may be the likely reason for the elevated tropospheric warming trend in 1979–2002.NCEP/NCAR Reanalysis data show that the near-surface upward temperature trend over the same period is greater than the tropospheric trend, which is consistent with direct radiosonde observations and inconsistent with ERA-40 results. A change of sign in the winter temperature trend from negative to positive in the late 1980s is documented in the upper troposphere/lower stratosphere with a maximum over the Canadian Arctic, based on radiosonde data. This change from cooling to warming tendency is associated with weakening of the stratospheric polar vortex and shift of its center toward the Siberian coast and possibly can be explained by the changes in the dynamics of the Arctic Oscillation. This temporal pattern is consistent with multi-decadal variations of key arctic climate parameters like, for example, surface air temperature and oceanic freshwater content. Elucidating the mechanisms behind these changes will be critical to understanding the complex nature of high-latitude variability and its impact on global climate change.

The summary of main findings in the study are quite interesting. They include

The uncertainty in temperature trends is too great to make any conclusive statements about the faster elevated warming in the lower troposphere in the Arctic during the last two decades. The only station showing elevated warming similar to the warming described by Graversen et al. (2008) is Tiksi. All other stations used in our analysis do not show any indication of faster elevated warming in the troposphere in any season.

Disagreement in temperature trends between the datasets used for the analysis is substantial even at the surface. All the “hotspots” of disagreement are in regions with sparse data coverage. There is a major disagreement between reanalysis products and IABP with regard to the trend at the surface for 1990–2004. The recent winter warming signal over the Beaufort Sea is statistically significant according to IABP/POLES and NCEP-1. However, other reanalysis products disagree substantially over trend magnitude and even sign: for example, NCEP-2 results show a significant negative SAT trend over the Beaufort Sea.

source of image 

Research Paper “Climate Model Simulated Changes In Temperature Extremes Due To Land Cover Change” By Avila Et Al 2012

I discussed this excellent paper

A. J. Pitman, F. B. Avila, G. Abramowitz, Y. P.Wang, S. J. Phipps and N. de Noblet-Ducoudré, 2011: Importance of background climate in determining impact of land-cover change on regional climate. Nature Climate Change.: 20 November 2011 | DOI: 10.1038/NCLIMATE1294

in my post

Announcement Of New Paper “Importance Of Background Climate In Determining Impact Of Land-Cover Change On Regional Climate” By Pitman Et Al 2011

Erik has alerted me to a new related paper

Avila, F. B., A. J. Pitman, M. G. Donat, L. V. Alexander, and G. Abramowitz (2012), Climate model simulated changes in temperature extremes due to land cover change, J. Geophys. Res., 117, D04108, doi:10.1029/2011JD016382

with the abstract [highlight added]

A climate model, coupled to a sophisticated land surface scheme, is used to explore the impact of land use induced land cover change (LULCC) on climate extremes indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The impact from LULCC is contrasted with the impact of doubling atmospheric carbon dioxide (CO2). Many of the extremes indices related to temperature are affected by LULCC and the resulting changes are locally and field significant. Some indices are systematically affected by LULCC in the same direction as increasing CO2 while for others LULCC opposes the impact of increasing CO2. We suggest that assumptions that anthropogenically induced changes in temperature extremes can be approximated just by increasing greenhouse gases are flawed, as LULCC may regionally mask or amplify the impact of increasing CO2 on climate extremes. In some regions, the scale of the LULCC forcing is of a magnitude similar to the impact of CO2 alone. We conclude that our results complicate detection and attribution studies, but also offer a way forward to a clearer and an even more robust attribution of the impact of increasing CO2 at regional scales.

Excerpts from the paper are

“….we have been examining the impact of LULCC on the ETCCDI temperature indices at large spatial scales. LULCC has a strong and statistically significant impact at the climate model grid-scale on many of the ETCCDI temperature indices at these scales. And even where the impact of LULCC seems small compared to increasing CO2, in some regions it adds to the impact of elevated CO2 and in some regions it counters this impact. There are also regions that appear to show quite large impacts due to LULCC despite the perturbation in LULCC being locally small.”

“We also note that we have focused on the impacts of one major type of LULCC and omitted urbanization, irrigation and other types of land use change that could strongly affect regional climate [Pielke et al., 2011].”

“Our results demonstrate that the impact on the ETCCDI indices of doubling CO2 is almost always much more geographically extensive and mostly of a larger magnitude than the impact of LULCC. However, many of the temperature indices show locally strong and statistically significant responses to LULCC, such that commonly 30– 50% of the continental surfaces of the tropics and northern and southern hemispheres are changed by LULCC. The scale of the impact is large enough to be field significant on seasonal timescales.”

The role of land use-land cover change on climate is finally getting the attention it deserves. I look forward to the next research study from this group.

source of image