Category Archives: Climate Change Metrics

Arctic Lower Tropospheric Temperature Trends Since 1979

As part of a set of papers we are working on, Emily Gill of the University of Colorado has analyzed the NCEP/NCAR lower tropospheric temperature trends from latitude 60N and 70N to the North Pole for June,July and August. This is shown below for two time periods; the top figure from the time period when satellite coverage becames global and the bottom figure since the large ENSO event in 1998.

These plots are provided as part of the examination of the reasons for the greater sea ice melt in recent years, which I discussed in the post

Summary Of Arctic Ice Decline – Recommendations For Investigation Of The Cause(s)

These two figures address the issue raised in that post to perform

 “…analyses of lower tropospheric and surface temperature anomalies by season for the Arctic sea ice regions.”

It is clear there has been warming over the period of record. However, it is relatively small.  Using a linear regression, the June, July and August warming since 1979 was +1.0 C, and since 1998 +0.5 to +0.6 C in the region from 60N and from 70N North Pole. There is quite bit of interannual variability such that a linear trend does not explain a majority of the variations over this time period.

Emily Gill has also provided the global June, July and August analyses. The global linear regression change for 1979 to 2012 is +0.73C.  For the period 1998 to 2012 and for 1999 to 2012 the linear regression change is +0.43 C and +0.57 C, respectively (the different start years were to include the 1998 large positive value associated with the large ENSO event).  Interestingly, there is not much of an Arctic amplification of warming.

It is not clear how this modest lower tropospheric warming would have resulted in such large Arctic sea ice melting unless

i) the warmth was accompanied by less cloudiness than average,


ii) the sea ice was always very marginally close to melting.

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Were 2009 and 2010 The Warmest Years In India Since 1901? By S. Raghavan

UPDATE OCT 13 2012: The article discussed below is available from here – INDIAN METEOROLOGICAL SOCIETY CHENNAI CHAPTER.

S. Raghavan of  the Indian Meteorological Society, Chennai in India has sent us the article below which appeared in the publication Breeze Volume 14 in June 2012. S. Raghavan is a retired Deputy Director-General of Meteorology of the India Meteorological Department. His earlier post on my weblog is

A Perspective on Weather and Climate Science by S. Raghavan

Were 2009 and 2010 the warmest years in India since 1901? by S. Raghavan

1. Warmest years

The India Meteorological Department (IMD) announced in 2010 that 2009 was the warmest year in India since 1901 (Attri and Tyagi, 2010). Again in 2011 it was stated that 2010 was the warmest since 1901 (IMD, 2011). The annual mean temperature for the country as a whole is estimated to have risen by 0.56ºC over the period. This agrees with the widespread perception that the world is warming.

What was the basis for this assessment? The IMD has utilised the records of about 210 surface observatories (including those at major cities) all over India and computed the average of the daily maximum and minimum temperatures at each station. Data have been gridded and weighted average of all grid values has been calculated for the country as a whole. While this is a straightforward process there are certain limitations of the data which need to be considered, as the likely errors in the data could be larger than the “expected” warming due to any climate change.

2. Changes in Land Use

Over the period of more than a century many land use changes have evidently taken place all over the country. The changes in urban areas may be in the form of new structures which can contribute radiation or alter wind circulation. In other areas there can be changes such as development of irrigated lands, change in farming practices, drying up or filling up of water bodies and removal of vegetation. These changes affect the radiation
balance, evaporation, soil moisture and wind flow. The observed increase in temperature can have a component due to land use change and a component due to changes in atmospheric composition and it will be difficult to separate the two.

It is interesting to note that the Inter-Governmental Panel on Climate Change (IPCC) (IPCC, 2012) has recently redefined climate change as

“A change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings, or to persistent anthropogenic changes in the composition of the atmosphere or in land use”.

This is different from the previous definition. IPCC states

“This definition differs from that in the United Nations Framework Convention on Climate Change (UNFCCC), where climate change is defined as: “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability.”

The IMD (Attri and Tyagi, 2010) is therefore correct in listing the trend under “Climate Change Scenario”. However the land use changes in each area and their impact will depend on many factors (meteorological as well as socio-economic) and will be widely different in different areas. Hence the temperature changes may not be compatible among all the stations or the particular station’s own data of earlier decades. An average of the temperatures for the whole country is therefore unlikely to be a good measure of climate change.

3. Deterioration of Exposure Conditions

In case an observatory site is changed, IMD has procedures to compare observations at the new and old sites for ensuring compatibility. But the change in exposure conditions at the same site is difficult to quantify or correct for. In major cities such as Mumbai and Kolkata where the observatories are surrounded by newly developed
roads and buildings, the changes are large and the observatory exposure is drastically affected. In addition to the changes in radiation fluxes and wind flow, even the instruments could have been shadowed in some cases.
It is not often possible to shift the observatory to a more open and representative site to overcome this problem. As different stations are differently affected, the computed country average will be affected.

4. Heat island effect

The heat island effect in cities is well-known. A study organised by the present author at Chennai (Jayanthi , 1991) in the 1980s showed heat island effects of up to 4ºC in some pockets in the minimum temperature epoch in winter. The effect on maximum temperature may be expected to be smaller. The result indicates that the heat island effect
is much larger than any increase which may be expected due to climate change. There may also be effects of changes in local wind circulations due to urban development or due to increasing air pollution. Such an effect will bias the country average.

5. Maximum and minimum temperatures

Maximum and minimum temperatures may be affected differently by land use changes or the heat island effect. Hence an average of the maximum and minimum temperatures may not bring out the correct change over time if any.

6. Network Selection

The basis of selection of the 210 stations for the computation of trend is not clear. Presumably the departmentally manned observatories with long period records which can be expected to have been set up originally with good exposure and yield more reliable data have been selected. Presumably there has been no change in type of instrumentation or observing practices at these stations. These need to be verified.

The USA has a Historical Climate Network consisting of a subset of stations of the National Weather Service for Climate change analysis. But even this is said to have several stations with unsatisfactory exposures (Davey and Pielke, 2005). More recently a U.S. Climate Reference Network (CRN) has been established (Vose et al., 2005). The
IMD also maintains a network of 10 Global Atmosphere Watch stations (GAW, formerly Background Air Pollution Monitoring Network or BAPMoN) as per WMO protocols and standards (Attri and Tyagi 2010). These may perhaps have a record which has not been significantly affected by the above effects but these stations are available only from 1974. They are few in number and widely different in geographical distribution and in topographic characteristics. Hence they may also not be representative of the country. The optimum station density network for assessing trends may need to be determined (See e.g. Voss and Menne, 2004).

7. Correction of data

Evidently while assessing long-term trends the impact of these effects has to be minimised. How is this to be done?

The stations to be included in the analysis can be reviewed to exclude those which are affected by significant heat island effect or exposure deterioration. A station by station check is necessary to exclude those which have poor or non-standard exposures or are unrepresentative in other respects. Techniques for “homogeneity adjustments” have
been suggested (e.g. Easterling et al., 1996). Another method suggested is to use temperature anomalies instead of the temperatures themselves because temperature anomalies are expected to be much more geographically coherent than actual temperatures (Peterson, 2006).The anomaly time series is derived by subtracting the mean
temperature from a base period. Such corrections need to be effected before announcing to the public the rise in the temperatures.

8. Significance and interpretation of temperature trends

How to interpret the trends corrected as suggested and use the information?

There is a widespread view among scientists that near-surface temperature is not the most reliable metric to assess climate change. Other parameters such as ocean heat content have been suggested as most of the energy received by the earth is stored in the oceans (e.g. Ellis et al. 1978). Publishing a temperature trend without interpreting it may cause the public to derive wrong conclusions. For example the public and the media often state and feel during every summer that the current summer is hotter than any they experienced earlier. They interpret this as climate change. This perception is in most cases not correct.

As discussed earlier, whether the observed trend is due to land use change or change in atmospheric composition, it is to be considered as climate change. But the actions to be taken to minimise the trend will be different in the two cases. The meteorological community should be able to advise decision-makers about measures to be taken in the two cases. Any information which goes to users should put these issues in proper perspective.


Attri S. D. and A Tyagi, 2010, “Climate Profile of India” by Met Monograph No. Environment Meteorology-01/2010

Davey, C. A., and R. A. Pielke Sr., 2005: Microclimate exposures of surface-based weather stations. Bull. Amer. Meteor. Soc., 86, 497–504.

Easterling, D. R., T. C. Peterson, and T. R. Karl, 1996, On the development and use of homogenized climatological datasets. J. Climate, 9, 1429–1434.

Ellis J. S., T. H. Vonder Haar, S. Levltus and A. H. Oort, 1978, The Annual Variation in the Global Heat Balance of the Earth, J. Geophys. Res., 83, 1958-1962IMD. 2011, Press Release dated 13 January 2011

IPCC, 2012: Summary for Policymakers. In: “Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation” [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 1-19. (available on IPCC website).

Jayanthi N., 1991, Heat Island study over Madras city and neighbourhood, Mausam, 42, 1, 83-88.

Vose R.S,, D. R. Easterling, T. R. Karl and M. Helfert, 2005, “Comments on “Microclimate Exposures of Surface-Based Weather Stations”, Bull. Amer. Meteor. Soc., 86, 504-506.

Vose, R. S., and M. J. Menne, 2004: A method to determine station density requirements for climate observing networks. J. Climate, 17, 2961–2971.

source of image

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Global Temperature Report: August 2012 From the University of Alabama At Huntsville

Phil Gentry has provided us with the August 2012 lower tropospheric temperature anomaly analysis from the University of Alabama at Huntsville. It is presented below.

Global Temperature Report: August 2012 [click images below for a clearer image]

Global climate trend since Nov. 16, 1978: +0.14 C per decade

August temperatures (preliminary)

Global composite temp.: +0.34 C (about 0.61 degrees Fahrenheit) above 30-year average for August.

Northern Hemisphere: +0.38 C (about 0.68 degrees Fahrenheit) above 30-year average for August.

Southern Hemisphere: +0.31 C (about 0.56 degrees Fahrenheit) above 30-year average for August.

Tropics: +0.26 C (about 0.47 degrees Fahrenheit) above 30-year average for August.

July temperatures (revised):

Global Composite: +0.28 C above 30-year average

Northern Hemisphere: +0.45 C above 30-year average

Southern Hemisphere: +0.11 C above 30-year average

Tropics: +0.33 C above 30-year average

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

Notes on data released Sept. 5, 2012:

Compared to global seasonal norms, August 2012 was the third hottest August in the 34-year satellite temperature record, 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. The last three Augusts have been three of the four warmest in the past 34 years, trailing only August 1998 — which was during a major El Nino Pacific Ocean warming event.

An El Nino warming event is still evident in the global temperature maps, stretching out across the tropical and southern Pacific Ocean from the west coast of South America, with temperatures in the tropics warming slightly from July through August.

The coldest and hottest spots on the globe (compared to seasonal norms) weren’t all that far apart in August: The “warmest” area was in the southwestern Atlantic Ocean off the coast of Argentina, where temperatures were as much as 3.43 C (6.17 degrees Fahrenheit) warmer than season norms. The Antarctic winter continues to run colder than normal. Compared to seasonal norms, the “coldest” spot on the globe in August was near the South Pole, with average temperatures as much as 3.38 C (6.08 F) colder than normal for the month.

Global August Temperature Anomalies     1.  1998   0.46   2.  2010   0.44   3.  2012  0.34   4.  2011   0.33   5.  2001   0.25   6.  1995   0.21   7.  2006   0.19   8.  2002   0.17   8.  2007   0.17   8.  2009   0.17 11.  1991   0.14 12.  2005   0.13 13.  2003   0.11 14.  1988   0.09 15.  1980   0.05 15.  1996   0.05 17.  1997   0.02 18.  1983  -0.01 19.  1981  -0.02 20.  1987  -0.04 21.  1990  -0.05 22.  2004  -0.06 22.  2008  -0.06 24.  1999  -0.12 24.  2000  -0.12 26.  1989  -0.13 26.  1994  -0.13 28.  1979  -0.24 29.  1993  -0.25 30.  1982  -0.26 31.  1985  -0.27 32.  1984  -0.28 33.  1986  -0.30 34.  1992  -0.47

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

The processed temperature data is available on-line at:

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.

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Comments On “The Shifting Probability Distribution Of Global Daytime And Night-Time Temperatures” By Donat and Alexander 2012 – A Not Ready For Prime Time Study

above figure from Caesar et al 2006

A new paper has appeared;

Donat, M. G. and L. V. Alexander (2012), The shifting probability distribution of global daytime and night-time temperatures, Geophys. Res. Lett., 39, L14707, doi:10.1029/2012GL052459.

The abstract reads [highlight added]

Using a global observational dataset of daily gridded maximum and minimum temperatures we investigate changes in the respective probability density functions of both variables using two 30-year periods; 1951–1980 and 1981–2010. The results indicate that the distributions of both daily maximum and minimum temperatures have significantly shifted towards higher values in the latter period compared to the earlier period in almost all regions, whereas changes in variance are spatially heterogeneous and mostly less significant. However asymmetry appears to have decreased but is altered in such a way that it has become skewed towards the hotter part of the distribution. Changes are greater for daily minimum (night-time) temperatures than for daily maximum (daytime) temperatures. As expected, these changes have had the greatest impact on the extremes of the distribution and we conclude that the distribution of global daily temperatures has indeed become “more extreme” since the middle of the 20th century.

This study, unfortunately, perpetuates the use of Global Historical Climate Reference Network surface temperature data as being a robust measure of surface temperature trends. The authors report that

 We use HadGHCND [Caesar et al., 2006], a global gridded data set of observed near-surface daily minimum (Tmin) and maximum (Tmax) temperatures from weather stations, available from 1951 and updated to 2010. For this study, we consider daily Tmax and Tmin anomalies calculated with respect to the 1961 to 1990 daily climatological average.

As described in the paper

Caesar, J., L. Alexander, and R. Vose (2006), Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set, J. Geophys. Res., 111, D05101, doi:10.1029/2005JD006280.

A gridded land-only data set representing near-surface observations of daily maximum and minimum temperatures (HadGHCND) has been created to allow analysis of recent changes in climate extremes and for the evaluation of climate model simulations. Using a global data set of quality-controlled station observations compiled by the U.S. National Climatic Data Center (NCDC), daily anomalies were created relative to the 1961–1990 reference period for each contributing station. An angular distance weighting technique was used to interpolate these observed anomalies onto a 2.5° latitude by 3.75° longitude grid over the period from January 1946 to December 2000. We have used the data set to examine regional trends in time-varying percentiles. Data over consecutive 5 year periods were used to calculate percentiles which allow us to see how the distributions of daily maximum and minimum temperature have changed over time. Changes during the winter and spring periods are larger than in the other seasons, particularly with respect to increasing temperatures at the lower end of the maximum and minimum temperature distributions. Regional differences suggest that it is not possible to infer distributional changes from changes in the mean alone.

The Donat and Alexander 2012 article concludes with the text

Using the data from this study we conclude that daily temperatures (both daytime and night-time) have indeed become “more extreme” and that these changes are related to shifts in multiple aspects of the daily temperature distribution other than just changes in the mean. However evidence is less conclusive as to whether it has become “more variable”.

The Donat and Alexander (2012) paper and the Caesar et al (2006) paper, however, both suffer in their ignoring issues that have been raised regarding the robustness of the data they are using for their analyses. They either ignored or are unaware of papers that show that the conclusions they give cannot be considered accurate unless they can show that the unresolved uncertainties  have either been corrected for, or shown not to affect their analyses. An overview of these issues is given in

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.

which the authors ignored in their study. The questions the authors did not examine before accepting the robustness of their analyses include:

1. The quality of station siting in the HadGHCND and whether this affects the extreme surface temperatures [Pielke et al 2002; Mahmood et al 2006Fall et al 2011; Martinez et al 2012].

2. The effect of a concurrent change over time in the dew point temperatures at each HadGHCND location, which, if they are lower, could result in higher dry bulb temperatures [Davey et al 2006; Fall et al 2010; Peterson et al 2011 ]

3.  A bias in the siting of the HadGHCND observing sites for particular landscape types [Montandon et al 2011]

4. Small scale vegetation effects on maximum and minimum temperatures observed at HadGHCND sites [Hanamean et al 2003]

5. The uncertainty associated with each separate step in the HadGHCND homogenization method to develop grid area averages [Pielke 2005].

6. The warm bias that is expected to be in the HadGHCND with respect  to minimum temperatures [which would be expected to be even more pronounced with respect to extreme cold temperatures] [Klotzbach et al 2010,2011; McNider et al 2012].

As just one example from the above list, Mahmood et al 2006 finds that

…the difference in average extreme monthly minimum temperatures can be as high as 3.6 °C between nearby stations, largely owing to the differences in instrument exposures.’

Note also in the figure at the top of this post, the poor spatial sampling for large portions of land.

The conclusion is that the HadGHCND data set is NOT sufficiently quality controlled, despite the assumption of the authors to the contrary. Ignoring peer reviewed papers that raise issues with their methodology does not follow the scientific  method.

The complete cite for these peer-reviewed papers that were ignored are listed below:

Davey, C.A., R.A. Pielke Sr., and K.P. Gallo, 2006: Differences between  near-surface equivalent temperature and temperature trends for the eastern  United States – Equivalent temperature as an alternative measure of heat  content. Global and Planetary Change, 54, 19–32.

Fall, S., N. Diffenbaugh, D. Niyogi, R.A. Pielke Sr., and G. Rochon, 2010: Temperature and equivalent temperature over the United States (1979 – 2005). Int. J. Climatol., DOI: 10.1002/joc.2094.

Fall, S., A. Watts, J. Nielsen-Gammon, E. Jones, D. Niyogi, J. Christy, and R.A. Pielke Sr., 2011: Analysis of the impacts of station exposure on the U.S. Historical Climatology Network temperatures and temperature trends. J. Geophys. Res.,  116, D14120, doi:10.1029/2010JD015146.Copyright (2011) American Geophysical Union.

Hanamean,  J.R. Jr., R.A. Pielke Sr., C.L. Castro, D.S. Ojima, B.C. Reed, and Z.  Gao, 2003: Vegetation impacts on maximum and minimum temperatures in northeast  Colorado. Meteorological Applications, 10, 203-215.

Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr.,  J.R. Christy, and R.T. McNider, 2009: An alternative explanation for differential temperature trends at the  surface and in the lower troposphere. J. Geophys. Res., 114, D21102, doi:10.1029/2009JD011841.

Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr.,  J.R. Christy, and R.T. McNider, 2010: Correction to: “An alternative explanation for differential temperature trends at the  surface and in the lower troposphere. J. Geophys. Res., 114, D21102, doi:10.1029/2009JD011841″, J. Geophys. Res.,  115, D1, doi:10.1029/2009JD013655

Mahmood, R., S. A. Foster, and D. Logan (2006a), The geoprofile metadata, exposure of instruments, and measurement bias in climatic record revisited, Int. J. Climatol., 26, 1091–1124.

Martinez, C.J., Maleski, J.J., Miller, M.F, 2012: Trends in precipitation and temperature in Florida, USA. Journal of Hydrology. volume 452-453, issue , year 2012, pp. 259 – 281

McNider, R.T., G.J. Steeneveld, B. Holtslag, R. Pielke Sr, S.   Mackaro, A. Pour Biazar, J.T. Walters, U.S. Nair, and J.R. Christy, 2012: Response and sensitivity of the nocturnal boundary layer over  land to added longwave radiative forcing. J. Geophys. Res., doi:10.1029/2012JD017578, in press.

Montandon, L.M., S. Fall, R.A. Pielke Sr., and D. Niyogi, 2011: Distribution of landscape types in the Global Historical Climatology Network. Earth Interactions, 15:6, doi: 10.1175/2010EI371

Peterson, T. C., K. M. Willett, and P. W. Thorne (2011), Observed changes in surface atmospheric energy over land, Geophys. Res. Lett., 38, L16707, doi:10.1029/2011GL048442

Pielke Sr., R.A., T. Stohlgren, L. Schell, W. Parton, N. Doesken, K. Redmond,  J. Moeny, T. McKee, and T.G.F. Kittel, 2002: Problems in evaluating regional  and local trends in temperature: An example from eastern Colorado, USA.  Int. J. Climatol., 22, 421-434.

Pielke Sr., Roger A., 2005: Public Comment on CCSP Report “Temperature Trends  in the Lower Atmosphere: Steps for Understanding and Reconciling Differences“. 88 pp including appendices.

The Donat and Alexander (2012) is particularly at fault in this neglect as most of the papers questioning the robustness of the GHCN type data sets were published well before their article was completed.  The conclusions of the Donat and Alexander study should not be considered as robust until they address the issues we raised in our papers.  

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Summary Of Arctic Ice Decline – Recommendations For Investigation Of The Cause(s)

source of top image from the Cryosphere Today

I was alerted to an interesting short movie of the Arctic ice decline;

See also

Arctic Sea Ice Graphs


the WUWT Sea Ice Page

There will be quite a bit of discussion of the upcoming minimum areal extent (which is likely to be a record minimum) in the coming weeks.  My suggestion is we need (for the period 1979 to the present)

i) a presentation of what the CMIP5 models have predicted when run in a hindcast mode,

ii) analyses of lower tropospheric and surface temperature anomalies by season for the Arctic sea ice regions,

iii) analyses of the export volume of sea ice out of the Arctic ocean basin,

iv) analyses of black carbon (soot) deposition on the sea ice,


v) analyses of turbulent shearing stress at the surface (which will affect waves and vertical overturning rates of sea ice, such as during storms).

Readers who are aware of such studies are invited to send to me, and I will post on.

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New Article “Monitoring and Understanding Trends in Extreme Storms: State of Knowledge” By Kunkel Et Al 2012

Jos de Laat of KNMI has alerted us to the informative new paper

Kunkel, K, et al 2012: Monitoring and Understanding Trends in Extreme Storms: State of Knowledge. Bulletin of the American Meteorological Society 2012 ; doi:

The abstract reads [highlight added]

The state of knowledge regarding trends and an understanding of their causes is presented for a specific subset of extreme weather and climate types. For severe convective storms (tornadoes, hail storms, and severe thunderstorms), differences in time and space of practices of collecting reports of events make the use of the reporting database to detect trends extremely difficult. Overall, changes in the frequency of environments favorable for severe thunderstorms have not been statistically significant. For extreme precipitation, there is strong evidence for a nationally-averaged upward trend in the frequency and intensity of events. The causes of the observed trends have not been determined with certainty, although there is evidence that increasing atmospheric water vapor may be one factor. For hurricanes and typhoons, robust detection of trends in Atlantic and western North Pacific tropical cyclone (TC) activity is significantly constrained by data heterogeneity and deficient quantification of internal variability. Attribution of past TC changes is further challenged by a lack of consensus on the physical linkages between climate forcing and TC activity. As a result, attribution of trends to anthropogenic forcing remains controversial. For severe snowstorms and ice storms, the number of severe regional snowstorms that occurred since 1960 was more than twice that of the preceding 60 years. There are no significant multi-decadal trends in the areal percentage of the contiguous U.S. impacted by extreme seasonal snowfall amounts since 1900. There is no distinguishable trend in the frequency of ice storms for the U.S. as a whole since 1950.

The article is an important new contribution in the assessment of changes in climate metrics over time. I have, however, one comments about the analyses and their conclusions in regards to their suggestion of attributing an increase in extreme precipitation to an increase in atmospheric water vapor.  Kunkel et al 2012 write

Karl and Trenberth (2003) have empirically demonstrated that for the same annual or seasonal precipitation totals, warmer climates generate more extreme precipitation events compared to cooler climates. This is consistent with water vapor being a critical limiting factor for the most extreme precipitation events. A number of analyses have documented significant positive trends in water vapor concentration and have linked these trends to human fingerprints in both changes of surface (Willet et al.2007) and atmospheric moisture (Santer et al. 2007).

The authors present analyses in their Table 2 to document an increase in atmospheric water vapor. They describe their analysis in the Table caption as

Table 2. Differences between two periods (1990-2009 minus 1971-845 1989) for daily, 1-in-5yr extreme events and maximum precipitable water values measured in the spatial vicinity of the extreme event location and within 24 hours of the event time.

However, in their analysis they use just two blocks of time (1990-2009) and (1971-1989) when different sliding analysis windows should have been used, in order to assess our robust there finding is with respect to sampling window.

They also should consider a peer-reviewed study which yields a different finding when assessing the overall North American trend in precipitable water;

Wang, J.-W., K. Wang, R.A. Pielke, J.C. Lin, and T. Matsui, 2008: Towards a robust test on North America warming trend and precipitable water content increase. Geophys. Res. Letts., 35, L18804, doi:10.1029/2008GL034564.

where we report

An increase in the atmospheric moist content has been generally assumed when the lower-tropospheric temperature (Tcol) increases, with relative humidity holding steady. Rather than using simple linear regression, we propose a more rigorous trend detection method that considers time series memory. The autoregressive moving-average (ARMA) parameters for the time series of Tcol, precipitable water vapor (PWAV), and total precipitable water content (PWAT) from the North American Regional Reanalysis data were first computed. We then applied the Monte Carlo method to replicate the ARMA time series samples to estimate the variances of their Ordinary Least Square trends. Student’s t tests showed that Tcol from 1979 to 2006 increased significantly; however, PWAVand PWAT did not. This suggests that atmospheric temperature and water vapor trends do not follow the conjecture of constant relative humidity over North America. We thus urge further evaluations of Tcol, PWAV, and PWAT trends for the globe.

They also did not consider peer-reviewed papers on the role of land use change in altering extreme precipitation events, where irrigation of surrounding landscapes when dams are constructed, appears to enhance extreme precipitation at least in arid and semi-arid landscapes through the enhancement of convective available potential energy (CAPE); e.g. see

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, L04405, doi:10.1029/2010GL046482.

In this paper we wrote

Understanding the forcings exerted by large dams on local climate is key to establishing if artificial reservoirs inadvertently modify precipitation patterns in impounded river basins. Using a 30 year record of reanalysis data, the spatial gradients of atmospheric variables related to precipitation formation are identified around the reservoir shoreline for 92 large dams of North America. Our study reports that large dams influence local climate most in Mediterranean, and semi‐arid climates, while for humid climates the influence is least apparent. Clear spatial gradients of convective available potential energy, specific humidity and surface evaporation are also observed around the fringes between the reservoir shoreline and farther from these dams. Because of the increasing correlation observed between CAPE and extreme precipitation percentiles, our findings point to the possibility of storm intensification in impounded basins of the Mediterranean and arid climates of the United States.

Another example of a study that documents how landscape change in the United States can alter precipitation patterns, including intensity, is

Georgescu, M., D. B. Lobell, and C. B. Field (2009), The Potential Impact of US biofuels on Regional Climate, Geophys. Res. Lett., In Press, doi: 10.1029/2009GL040477

who reported that

Using the latest version of the WRF modeling system we conducted twenty-four, midsummer, continental-wide, sensitivity experiments by imposing realistic biophysical parameter limits appropriate for bio-energy crops in the Corn Belt of the United States….. Maximum, local changes in 2m temperature of the order of 1°C occur for the full breadth of albedo (ALB), minimum canopy resistance (RCMIN), and rooting depth (ROOT) specifications, while the regionally (105°W – 75°W and 35°N – 50°N) and monthly averaged response of 2m temperature was most pronounced for the ALB and RCMIN experiments, exceeding 0.2°C….The full range of albedo variability associated with biofuel crops may be sufficient to drive regional changes in summertime rainfall.

An increase in surface temperature would increase CAPE (and the resultant intensity of thunderstorms) if the water vapor content remained the same (or increased).

Urban landscapes also can contribute to enhancing the magnitude of extreme precipitation; e.g. see

Lei, M., D. Niyogi, C. Kishtawal, R. Pielke Sr., A. Beltrán-Przekurat, T. Nobis, and S. Vaidya, 2008: Effect of explicit urban land surface representation on the simulation of the 26 July 2005 heavy rain event over Mumbai, India. Atmos. Chem. Phys. Discussions, 8, 8773–8816

where among the conclusions is written

The results indicate that even for this synoptically active rainfall event, the vertical wind and precipitation are significantly influenced by urbanization, and the effect is more significant during the storm initiation…….The results suggest that urbanization can significantly contribute to extremes in monsoonal rain events that have been reported to be on the rise;

and see also, as another example,

Georgescu, M., G. Miguez-Macho, L. T. Steyaert, and C. P. Weaver (2009), Climatic effects of 30 years of landscape change over the Greater Phoenix, Arizona, region: 2. Dynamical and thermodynamical response, J. Geophys. Res., doi:10.1029/2008JD010762.

where in his guest post on February 9 2009 wrote

Our modeling results show a systematic difference in total accumulated precipitation between the most recent (2001) and least recent (1973) landscape reconstructions: a rainfall enhancement for 2001 relative to the 1973 landscape.

We recommend that in the next assessment led by Ken Kunkel and colleagues they include consideration of the role of landscape processes in affecting extreme weather over the United States (and elsewhere).

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“Numerical Simulation Of The Surface Air Temperature Change Caused By Increases Of Urban Area” By Aoyagi et al 2012

There is a new paper that documents the continuning effect of urbanization on surface air temperature trends [h/t Koji Dairaku]

Aoyagi, T., N. Kayaba and Seino, N., 2012: Numerical Simulation of the Surface Air Temperature Change Caused by Increases of Urban Area, Anthropogenic Heat, and Building Aspect Ratio in the Kanto-Koshin Area. Journal of the Meteorological Society of Japan, Vol. 90B, pp. 11–31, 2012 11 doi:10.2151/jmsj.2012-B02

The abstract reads

We investigated a warming trend in the Kanto-Koshin area during a 30-year period (1976-2006). The warming trends at AMeDAS stations were estimated to average a little less than 1.3°C/30 years in both summer and winter. These warming trends were considered to include the trends of large-scale and local-scale warming effects. Because a regional climate model with 20-km resolution without any urban parameterization could not well express the observed warming trends and their daily variations, we investigated whether a mesoscale atmospheric model with an urban canopy scheme could express them. To make the simulations realistic, we used 3 sets of real data: National Land Numerical Information datasets for the estimation of the land use area fractions, anthropogenic heat datasets varying in space and time, and GIS datasets of building shapes in the Tokyo Metropolis for the setting of building aspect ratios. The time integrations over 2 months were executed for both summer and winter. A certain level of correlation was found between the simulated temperature rises and the observed warming trends at the AMeDAS stations. The daily variation of the temperature rises in urban grids was higher at night than in the daytime, and its range was larger in winter than in summer. Such tendencies were consistent with the observational results. From factor analyses, we figured out the classic and some unexpected features of urban warming, as follows: (1) Land use distribution change (mainly caused by the decrease of vegetation cover) had the largest daytime warming effect, and the effect was larger in summer than in winter; (2) anthropogenic heat had a warming effect with 2 small peaks owing to the daily variation of the released heat and the timing of stable atmospheric layer formation; and (3) increased building height was the largest factor contributing to the temperature rises, with a single peak in early morning.

The conclusions state that

By numerical simulations using the JMA-NHM, we studied how much 3 bottom boundary condition changes, namely, in land use area fraction, anthropogenic heat release, and increased building aspect ratio, could explain the warming trends observed at the AMeDAS stations during a 30-year period (1976–2006).

A sensitivity study of land use modification, i.e., the spread of urban area, showed a warming effect on average, and that the effect was larger in grids where the land use modification rate was larger. The e¤ects were very small in central Tokyo because the urban area fraction was already saturated there by 1976. This effect was larger in summer when the Bowen ratio is originally small.

The warming effect of anthropogenic heat was concentrated to the central urban area where the heat was mainly loaded. The effect was larger in winter owing to relatively stable atmospheric conditions. Maximum warming was observed in the morning and a secondary peak was seen in the evening if we set the heat to vary realistically with time.

The increase of the aspect ratio of the buildings also had a warming effect on the surface air temperature. It was mainly caused by the inhibition of radiative cooling during nighttime, and the effect was larger in winter. The daily variation of this effect had a single peak in the morning.

This is a very important study, as it documents that climate observating stations that are in locations which are undergoing urbanization will have a warming (positive temperature trend) which is separate from any larger scale warming. As shown in the post

2012 IGBP Article “Cities Expand By Area Equal To France, Germany And Spain Combined In Less Than 20 years”

urbanization continues unabated. NCDC, GISS, CRU and BEST, in their analyses have not adequately considered the bias that urbanization produces in their analyses.

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