by Judith Curry
What do these three papers share in common? All were written by scientists well outside the fields of atmospheric and climate science.
Minding our Methods: How Choice of Time Series, Reference Dates, and Statistical Approach Can Influence the Representation of Temperature Change
Kalb Stephenson, Lillian Alessa, Mark Altaweel, Andrew Kliskey, Kacy Krieger
From the closing section, Future Recommendations:
Choice of time scale, reference dates, and statistical approach can severely impact the representation of climate change. In particular, special consideration must be given to major climatic events, such as the 1976 PDO shift, which has the potential to interact with nearby reference start dates and introduce erroneous information into reports. Having a time range that covered well before and after the 1976 PDO would help to minimize the effects of this event; a method that de-emphasized extremes, such as a running mean, could be used to estimate long-term temperature trends. A cautious approach should be taken when comparing temperature trends from multiple studies that do not use identical methods or when considering the selection of reference start date from a year exhibiting a temperature extreme. Furthermore, different types of climatic patterns and anomalies are captured when using various local and global methods, suggesting that caution is needed when comparing estimates from these different classes of methods. The chosen method should be able to capture trends in a data set without being overly sensitive to variation. A subfield of robust statistics has been developed to handle this.
The ability to adequately describe temperature change may, at some point, be compromised given the increase in temperature extremes in contemporary climate change. Therefore, an essential part of future estimations of multidecadal temperature trends in Alaska and elsewhere should be the implementation of a comprehensive and comparative analysis of time series and sensitivities to start dates and statistical methods. Applying multiple methods with different start and end dates, along with varying window size and filters used, should be considered and outputs tested using sensitivity analysis in order to determine how sensitive results are to extreme variations. Methods that over- emphasize extreme variations are likely to be less useful to climate scientists and policy makers interested in long-term temperature trends. Furthermore, comparisons of studies carried out by different groups would be best served by using standardized time periods along WMO guidelines (see above). This is especially important when different methods are used to analyze data.
There are a number of complex drivers underpinning arctic and subarctic climate, and therefore caution should be used against making large, broad-scale, or sweeping statements about climate and climate change. In the recent past, some regions of Alaska have been warming while others appear to have been cooling. However, the summarized or reported direction and degree of change depends heavily upon the choice of time scale, reference date, and statistical approach. For Alaska, greater variation in microclimates could lead to temperature trend estimates being more sensitive to reference start dates, and thus greater discrepancy between temperature changes reported by different statistical methods. This has implications for management practices that rely either on historical trend estimates or on anticipated temperature trajectories. It also has strong implications for Northern cultures that either directly or indirectly depend on a certain level of predictability in seasons and seasonal events (e.g., freeze−thaw events, cold snaps, first snowfall, spring melt, density of the snowpack, storm frequency, sea ice availability or thickness, river ice thickness) for their acquisition of food and fuel, their socio-cultural identity, and their safety while traveling for subsistence purposes.
If scientists are able to utilize the methods and approaches described above appropriately, there is likely to be better representation of changes in climate, shifts in seasonality, and any resulting influence on subsistence fish and game species or existing infrastructure. As policy makers contend with deve- loping responses to climate change and its impacts in Alaska and beyond, it is imperative that the use and interpretation of scientific studies to support policy development minimizes any potential for bias by giving due consideration to the methodsused to estimate temperature change.
Published online in the ACS journal Environ. Sci. Technol., full paper is available online [here].
Biosketches: Kalb Stevenson is a postdoctoral scientist affiliated with the Department of Biological Sciences and Resilience and Adaptive Management Group at the University of Alaska Anchorage. Lilian Alessa is a Professor of Biological Sciences at the University of Alaska Anchorage and co- leader of the Resilience and Adaptive Management Group at UAA. Mark Altaweel is a Lecturer at University College London and a visiting scientist at the University of Chicago and Argonne National Laboratory. Dr. Altaweel is interested in researching past and modern social ecological systems as they pertain to water, agriculture, and trans- portation issues. Andrew Kliskey is a Professor of Biological Sciences and Environmental Studies at the University of Alaska Anchorage, where he co-leads the Resilience and Adaptive Management (RAM) Group. Kacy Krieger is a Geospatial Scientist with the Resilience and Adaptive Management Group at the University of Alaska Anchorage. Kacy has a background in geomorphology.
Evaluating explanatory models of the spatial pattern of surface climate trends using model selection and bayesian averaging methods
Ross McKitrick • Lisa Tole
Abstract. We evaluate three categories of variables for explaining the spatial pattern of warming and cooling trends over land: predictions of general circulation models (GCMs) in response to observed forcings; geographical factors like latitude and pressure; and socioeconomic influences on the land surface and data quality. Spatial autocorrelation (SAC) in the observed trend pattern is removed from the residuals by a well-specified explanatory model. Encompassing tests show that none of the three classes of variables account for the contributions of the other two, though 20 of 22 GCMs individually contribute either no significant explanatory power or yield a trend pattern negatively correlated with observations. Non-nes- ted testing rejects the null hypothesis that socioeconomic variables have no explanatory power. We apply a Bayesian Model Averaging (BMA) method to search over all pos- sible linear combinations of explanatory variables and generate posterior coefficient distributions robust to model selection. These results, confirmed by classical encom- passing tests, indicate that the geographical variables plus three of the 22 GCMs and three socioeconomic variables provide all the explanatory power in the data set. We conclude that the most valid model of the spatial pattern of trends in land surface temperature records over 1979–2002 requires a combination of the processes represented in some GCMs and certain socioeconomic measures that capture data quality variations and changes to the land surface.
Online version published by Climate Dynamics. Full paper is available online [here].
Ross McKitrick is no stranger to those who follow the climate debate. Ross is a Professor of Economics at Guelph University, specializing in environmental economics and policy analysis.
Did the global temperature trend change at the end of the 1990s?
Abstract. The apparent leveling of the global temperature time series at the end of the 1990s may represent a break in the upward trend. A study of the time series measurements for temperature, carbon dioxide, humidity and methane shows changes coincident with phase changes of the Atlantic and Pacific Decadal Oscillations. There are changes in carbon dioxide, humidity and methane measurement series in 2000. If these changes mark a phase change of the Pacific Decadal Oscillation then it might explain the global temperature behaviour.
Accepted for publication in the Asia-Pacific Journal of Atmospheric Sciences. Link to full paper [here].
Tom Quirk’s biosketch: Tom Quirk trained as a physicist at the Universities of Melbourne (M.Sci.) and Oxford (D. Phil). He has been a Fellow of three Oxford Colleges and has worked as a high energy physicist in the United States at Fermilab, the universities of Chicago and Harvard and at CERN inEurope. In addition he has been through the Harvard Business Schooland subsequently worked for Rio Tinto. He was an early director of Biota, the developer of Relenza, a new influenza drug. In addition he has been involved in the management of gas and electricity transmission systems as a director of the Victorian Power Exchange (electricity) and Deputy Chairman of VENCorp, the company that managed the transmission and the market for wholesale natural gas in South East Australia.
JC comments: Each of these papers provides a fresh perspective on interpreting surface temperature data trends. The authors of all these papers have academic training and expertise that lies well outside of the field of atmospheric science and climatology. I have long said that fresh perspectives from outside the field of climate science are needed. Congrats to Ross for getting his paper published in Climate Dynamics, which is a high impact mainstream climate journal. My main question is whether the lead authors of the relevant IPCC AR5 chapter will pay attention to these papers; they should (and the papers meet the publication deadline for inclusion in the AR5).