Archive for the ‘Statistics’ Category
After promising to have our flagship paper on reconciling the signal and noise of global warming on decadal timescales subject to open review, it is finally on. The paper has been submitted and accepted for open review at Earth System Dynamics.
Reconciling the signal and noise of atmospheric warming on decadal timescales
Roger N. Jones and James H. Ricketts
Victoria Institute of Strategic Economic Studies, Victoria University, Melbourne, Victoria 8001, Australia
Received: 13 Aug 2016 – Accepted: 22 Aug 2016 – Published: 23 Aug 2016
Interactions between externally-forced and internally-generated climate variations on decadal timescales is a major determinant of changing climate risk. Severe testing is applied to observed global and regional surface and satellite temperatures and modelled surface temperatures to determine whether these interactions are independent, as in the traditional signal-to-noise model, or whether they interact, resulting in steplike warming. The multi-step bivariate test is used to detect step changes in temperature data. The resulting data are then subject to six tests designed to show strong differences between the two statistical hypotheses, hstep and htrend: (1) Since the mid-20th century, most of the observed warming has taken place in four events: in 1979/80 and 1997/98 at the global scale, 1988/89 in the northern hemisphere and 1968/70 in the southern hemisphere. Temperature is more steplike than trend-like on a regional basis. Satellite temperature is more steplike than surface temperature. Warming from internal trends is less than 40 % of the total for four of five global records tested (1880–2013/14). (2) Correlations between step-change frequency in models and observations (1880–2005), are 0.32 (CMIP3) and 0.34 (CMIP5). For the period 1950–2005, grouping selected events (1963/64, 1968–70, 1976/77, 1979/80, 1987/88 and 1996–98), correlation increases to 0.78. (3) Steps and shifts (steps minus internal trends) from a 107-member climate model ensemble 2006–2095 explain total warming and equilibrium climate sensitivity better than internal trends. (4) In three regions tested, the change between stationary and non-stationary temperatures is steplike and attributable to external forcing. (5) Steplike changes are also present in tide gauge observations, rainfall, ocean heat content, forest fire danger index and related variables. (6) Across a selection of tests, a simple stepladder model better represents the internal structures of warming than a simple trend – strong evidence that the climate system is exhibiting complex system behaviour on decadal timescales. This model indicates that in situ warming of the atmosphere does not occur; instead, a store-and-release mechanism from the ocean to the atmosphere is proposed. It is physically plausible and theoretically sound. The presence of steplike – rather than gradual – warming is important information for characterising and managing future climate risk.
Comments welcome: here or there. Deadline October 4.
Imagine you didn’t know anything about climate change and the greenhouse effect but were interested and you know a bit about general science. Would you accept the following story?
“Earth’s climate is a large, complex system, affected by forces that produce both linear and nonlinear responses. Shortwave radiation – basically UV – from the sun comes in and heats up the planet, producing infrared radiation. Some UV gets reflected straight back out by clouds, snow and ice and stuff. The land can heat up quite a lot, but it cools back down again and doesn’t store much. If a forest is cleared and replaced by buildings, it will warm up a bit but the effect is only local.”
“But the ocean – that’s another story. It absorbs a lot of radiation, so is taking up heat all the time. Huge streams of energy are entering and leaving the ocean store each year. Some is ‘dry’ or sensible heat, which is ordinary warmth. Some is ‘wet heat’ or evaporated moisture. Energy gets taken up when the moisture is evaporated and it will be released again when the moisture cools, condenses and then gets rained out. In this way, the oceans provide a lot of heat to the land every year, largely as rainfall and a bit of snow.”
I gave a seminar yesterday at the ARC Centre of Excellence for Climate System Science at the University of New South Wales. Thanks Alvin Stone and Andrea Taschetto for organising it. It’s the first time I’ve had the opportunity to go through the entire ‘step change’ hypothesis of how the climate changes, the theoretical background, structural models developed from that and how the testing was set up, prior to showing a whole raft of test results.
One of the questions I got at the end, which also comes up quite often in the literature, was about the potential cause of the step changes in temperature data. It came from a question as to whether we had tested the step change model with artificial data that had been ‘reddened’ – that is, made dependent on the previous data. Such time series can have long-term persistence and contain a number of different quasi-periodic timescales, so do not conform to a single statistical model. This line of questioning alludes to whether a step or nonlinear response in a time series needs to be have an underlying cause that can be linked to an external source or whether it’s the result of random variations (see paper by Rodionov for a more more technical description). I gave a somewhat flip answer – because there is real energy in the system we are assessing (the climate system), whether a rapid shift is due to red noise or not matters less than understanding what that means for risk.