RealClimate
LightNews LightNews
realclimate.bsky.social
RealClimate
@realclimate.bsky.social
130 followers 1 following 1.1K posts
https://www.realclimate.org is a commentary site on climate science by working climate scientists for the public and journalists. We aim to provide a quick response to developing stories and provide the context sometimes missing in mainstream commentary.
Posts Replies Media Videos
realclimate.bsky.social
The Silurian Hypothesis - 17 APR 2018 www.realclimate.org/index.php/ar... #climatechange #science
The Silurian Hypothesis (preprint) is the idea if industrial civilization had arisen on Earth prior to the existence of hominids, what traces would be left that could be detectable now? As a starting point, we explore what the traces of the Anthropocene will be in millions of years – carbon isotope changes, global warming, increased sedimentation, spikes in heavy metal concentrations, plastics and more – and then look at previous examples of similar events in the geological record. What is unique about our presence on Earth and what might be common to any industrial civilization? Can we rule out similar causes?
April 2, 2025 at 7:40 PM
realclimate.bsky.social
Stronger evidence for a weaker Atlantic overturning circulation
11 APR 2018 - www.realclimate.org/index.php/ar... #climatechange #science #climatechange #science
Fig. 1 Trends in sea surface temperatures. Left: in the climate model CM2.6 in a scenario with a doubling of the amount of CO2 in the air. Right: in the observation data from 1870 to the present day. In order to make the trends comparable despite the different periods and CO2 increases, they were divided by the globally averaged warming trend, i.e. all values above 1 show an above-average warming (orange-red), values below 1 a below-average warming, negative values a cooling. Due to the limited availability of ship measurements, the measurement data are much more “blurred” than the high-resolution model data. Graph: Levke Caesar Fig. 2 Time evolution of the Atlantic overturning circulation reconstructed from different data types since 1700. The scales on the left and right indicate the units of the different data types. The blue curve was shifted to the right by 12 years since Thornalley found the best correlation with temperature with this lag. Makes sense: it takes a while until a change in currents alters the temperatures. Graph: Levke Caesar.
April 2, 2025 at 7:37 PM
realclimate.bsky.social
Alsup asks for answers - 11 MAR 2018 www.realclimate.org/index.php/ar... #climatechange #science
Main sources of human CO2 emissions are fossil fuel burning and (net) deforestation. This figure is from the Global Carbon Project in 2017. Prior to ~1750, atmospheric CO2 had been stable (within a few ppm) for millenia sustained by a balance between natural sources and sinks. This figure shows the changes seen in ice cores and the instrumental record. The Earth’s surface emits infrared radiation. This is absorbed by greenhouse gases, which through collisions with other molecules cause the atmosphere to heat up. Emission from greenhouse gases (in all directions, including downwards) adds to the warming at the surface. The figure shows the easiest mathematical description of the greenhouse effect. The downward radiation from greenhouse gases can be easily measured at the surface in nights under clear skies and no other heat sources in the atmosphere (e.g. Philipona and Dürr, 2004). The US National Climate Assessment attribution statement is a bit more specific than the one in IPCC: The likely range of the human contribution to the global mean temperature increase over the period 1951–2010 is 1.1° to 1.4°F (0.6° to 0.8°C), and the central estimate of the observed warming of 1.2°F (0.65°C) lies within this range (high confidence). This translates to a likely human contribution of 93%–123% of the observed 1951–2010 change. It is extremely likely that more than half of the global mean temperature increase since 1951 was caused by human influence on climate (high confidence). The likely contributions of natural forcing and internal variability to global temperature change over that period are minor (high confidence). This summary graphic is useful: Basically, all of the warming trend in the last ~60yrs is anthropogenic (a combination of greenhouse gases, aerosols, land use change, ozone etc.). To get a sense of the breakdown of that per contribution for the global mean temperature, and over a longer time-period, the Bloomberg data visualization, using data from GISS simulations is very useful. This is the biggie. What is the attribution for the temperature trends in recent decades? The question doesn’t specify a time-scale, so let’s assume either the last 60 years or so (which corresponds to the period specifically addressed by the IPCC, or the whole difference between now and the ‘pre-industrial’ (say the decades around 1850) (differences as a function of baseline are minimal). For the period since 1950, all credible studies are in accord with the IPCC AR5 statement: It is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together. The best estimate of the human-induced contribution to warming is similar to the observed warming over this period.
March 26, 2025 at 6:04 PM
realclimate.bsky.social
More ice-out and skating day data sets - 26 FEB 2018 www.realclimate.org/index.php/ar... #climatechange #science
Figure 1: The seven longest data sets of Minnesotan lake “ice out” dates (with data back to at least 1900). Ice out date is shown relative to the vernal equinox (see below for details). Red lines are loess ~30yr smooths. There are good data sets for dozens of other lakes in North America, notably ice duration from Lakes Mendota and Monona in Wisconsin. Note that ice duration is slightly less noisy dataset than ice out. These data are nominally collated by the Global Lake and River Ice Phenology Database, but it isn’t that up-to-date, though it does include a lot of long records from Europe. One other notable lake is Lake Winnipesaukee in NH (h/t David Appell), which has records from 1887 and a betting pool.
March 5, 2025 at 6:35 PM
realclimate.bsky.social
Rideau Canal Skateway - 22 FEB 2018 www.realclimate.org/index.php/ar... #climatechange #science
I’ve been interested in indirect climate-related datasets for a while (for instance, the Nenana Ice Classic). One that I was reminded of yesterday is the 48-year series of openings and closings of the Rideau Canal Skateway in Ottawa. Rideau Canal Skateway. Lauren Bath Since 1971, the National Capital Commission (NCC) in Ottawa has (once the ice is thick enough for safe skating) methodically tried to keep the frozen canal available for ice skaters (by clearing snow, smoothing surfaces, filling cracks etc.). This is possible only if the weather permits – first by being cold enough to sufficiently freeze the ice, and second by not being warm enough to melt the ice surface as the season progresses. Apart from the first season, which was not planned ahead of time, each year since has been anticipated to start in the second half of December (or early January) and ideally extends to March. Updating the Brammer et al graph to 2018 (including the record shortest season in 2016) is straightforward: Rideau Canal Skateway. Brammer et al (updated) As expected, there are clear trends in season length (a reduction of ~23±11 days (95% CI) since 1972), and while there are decreases in skating days, they aren’t significant due to the too short period (similarly with the available opening/closing dates). There is of course the possibility on non-climatic artifacts. Increasing skill/experience of the Skateway managers might prolong the season, while decreasing tolerances for risk(?) might shorten it. These are issues that are hard to quantify without much greater amounts of the meta-data associated with the opening and closing. Nevertheless, we have another independent dataset which conforms to our expectations that outdoor ice in North America is suffering.
March 5, 2025 at 3:46 PM
realclimate.bsky.social
The global CO2 rise: the facts, Exxon and the favorite denial tricks - 25 JAN 2018 www.realclimate.org/index.php/ar... #climatechange #science
Fig. 1 Perhaps the most important scientific measurement series of the 20th century: the CO2 concentration of the atmosphere, measured on Mauna Loa in Hawaii. Other stations of the global CO2 measurement network show almost exactly the same; the most important regional variation is the greatly subdued seasonal cycle at stations in the southern hemisphere. This seasonal variation is mainly due to the “inhaling and exhaling” of the forests over the year on the land masses of the northern hemisphere. Source (updated daily): Scripps Institution of Oceanography. Fig. 4 CO2 budget for 2007-2016, showing the various net sources and sinks. The figures here are expressed in gigatons of CO2 and not in gigatons of carbon as in Fig. 3. The conversion factor is 44/12 (molecular weight of CO2 divided by atomic weight of carbon). Source: Global Carbon Project. Fig. 3 Scheme of the global carbon cycle. Values ​​for the carbon stocks are given in Gt C (ie, billions of tonnes of carbon) (bold numbers). Values ​​for average carbon fluxes are given in Gt C per year (normal numbers). Source: WBGU 2006 . (A similar graph can also be found at Wikipedia.) Since this graph was prepared, anthropogenic emissions and the atmospheric CO2 content have increased further, see Figs 4 and 5, but I like the simplicity of this graph. Fig. 6 Excerpt from the New York Times of 6 November 1997 The text to go with it read: While most of the CO2 emitted by far is the result of natural phenomena – namely respiration and decomposition, most attention has centered on the three to four percent related to human activities – burning of fossil fuels, deforestation. That is pretty clever and could hardly be an accident. The impression is given that human emissions are not a big deal and only responsible for a small percentage of the CO2 increase in the atmosphere – but without explicitly saying that. In my view the authors of this piece knew that this idea is plain wrong, so they did not say it but preferred to insinuate it. A recent publication by Geoffrey Supran and Naomi Oreskes in Environmental Research Letters has systematically assessed ExxonMobil’s climate change communications during 1977–2014 and found: We conclude that ExxonMobil contributed to advancing climate science—by way of its scientists’ academic publications—but promoted doubt about it in advertorials. Given this discrepancy, we conclude that ExxonMobil misled the public.
March 3, 2025 at 4:08 PM
realclimate.bsky.social
The claim of reduced uncertainty for equilibrium climate sensitivity is premature - 21 JAN 2018 www.realclimate.org/index.php/ar... #climatechange #science
Figure 1. Test of the Hasselmann model through a regression analysis, where the coloured curves are the best-fit modelled values for Q based on the Hasselmann model and global mean temperatures (PDF). Figure 2. The regression coefficients. Negative values for C are unphysical and suggest that the Hasselmann model is far from perfect. The estimated error margins for C are substantial, however, and also include positive values. Blue point shows the estimates for NCEP/NCAR reanalysis. The shaded areas cover the best estimates plus/minus two standard errors (PDF). Figure 3. The area of Earth’s surface with valid temperature data (PDF).
March 3, 2025 at 4:03 PM
realclimate.bsky.social
2017 temperature summary - 19 JAN 2018 www.realclimate.org/index.php/ar... #climatechange #science
2017 temperature summary 19 JAN 2018 BY GAVIN This is a thread to discuss the surface temperature records that were all released yesterday (Jan 18). There is far too much data-vizualization on this to link to, but feel free to do so in the comments. Bottom line? It’s still getting warmer.
March 3, 2025 at 4:00 PM
realclimate.bsky.social
What did NASA know? and when did they know it? - 24 DEC 2017 www.realclimate.org/index.php/ar... #climatechange #science
If you think you know why NASA did not report the discovery of the Antarctic polar ozone hole in 1984 before the publication of Farman et al in May 1985, you might well be wrong. One of the most fun things in research is what happens when you try and find a reference to a commonly-known fact and slowly discover that your “fact” is not actually that factual, and that the real story is more interesting than you imagined… Joe Farman and colleagues (BAS)Here is the standard story (one I’ve told repeatedly myself): The publication in 1985 by scientists from the British Antarctic Survey working at Halley Station (right) of observations of extremely low ozone values in Oct 1983 (SH springtime) came as a huge shock to the scientific community. Given that NASA had been monitoring ozone by satellite using the NIMBUS instruments since the late 1970s, people were surprised that this had not been reported already. NASA scientists went back to the satellite data and found that anomalously low values had been rejected as bad data and were not included in the analyses. After reprocessing the data with this flag removed, the trends became clear and the confirmation of ground-based data was reported in the NY Times in Nov 1985 and published formally the next year (Stolarski et al., 1986). This is mostly true, but not quite… It is true that the Quality Control (QC) flag on the retrieval was set whenever the inferred ozone level dropped below 180 Dobson Units [1 DU is equivalent to a 0.01mm thick pure ozone layer at standard temperature and pressure]. Prior to 1983, there had never been an observation below 200 DU and so values lower than 180 DU were out of calibration range for the sensor. However, it wasn’t true that no-one at NASA had noticed.
March 3, 2025 at 1:31 PM
realclimate.bsky.social
A brief review of rainfall statistics - 21 NOV 2017 www.realclimate.org/index.php/ar... #climatechange #science
Historgam of 24-hr precipitation measured at Bjørnholt in a forest near Oslo. There will always be some clutter at the upper end of plots like these because there are so few data points representing these extreme values. The nice thing with the exponential distribution (which is a particular case of the gamma function) is that it only requires one parameter to specify the mathematical curve: it’s the inverse of the mean value \mu. I then used Bayes’ theorem to account for dry and wet days, where the probability for rainfall was taken to be the wet-day frequency f_w. The advantage of this approach is that I now had two parameters which were easy to estimate: the wet-day mean precipitation (or mean rainfall intensity) \mu and the wet-day frequency f_w. Figure 1. A comparison between probabilities estimated with the rain equation and the observed fraction of events with more than 30 mm rain in Groningen in the Netherlands. Here H(X - x) refers to the Heaviside function, which is a mathematical way of expressing that I only counted the number of events with more than 30 mm/day each year in the observations (the plot was made with the R-package esd and the command test. rainequation(loc='GRONINGEN-1',threshold=20)). Figure 2. A scatter plot of probabilities and corresponding fractions of events from long rain gauge records in Europe, based on the wet-day mean precipitation and frequency from the observations (the plot was made with the R-package esd and the command scatterplot.rainequation()).
February 19, 2025 at 5:16 PM
realclimate.bsky.social
O Say can you See Ice… - 6 NOV 2017 www.realclimate.org/index.php/ar... #climatechange #science
While many instruments can be used to detect sea ice, the continuity required for long-term climate monitoring makes it vital that the different products are cross-calibrated and have similar characteristics to be useful. The closest instruments to the those on the DMSP satellites are the radiometers (MWRI) on the Chinese Feng Yun-3 series of satellites. Unfortunately, again because of Congress, NASA collaborations with China are restricted and since the sea ice work at NSIDC is funded by NASA, that might prevent this source of data being used in the US (though presumably non-US colleagues would not have this problem). Another possibility is the Japanese satellite GCOM-W1 which has a more advanced AMSR2 instrument (in space since 2012, and has also passed it’s design lifetime), but the merge of this data with the DMSP satellites is still a work in progress. This is being used for the Bremen University sea ice maps though. Differences in views from passive microwave instruments (SSMI vs. AMSR2) via Arctic Roos. Unfortunately, the next scheduled passive microwave sensor to be launched is not until 2022 on the European Space Agency’s 2nd Generation MetOp satellite, and will need a year’s overlap with an existing satellite to be optimally calibrated. Thus the likelihood of a gap in the record developing before then is very high.
February 19, 2025 at 5:12 PM
realclimate.bsky.social
El Niño and the record years 1998 and 2016 - 4 NOV 2017 www.realclimate.org/index.php/ar... #climatechange #science
Fig. 1 GISTEMP global temperature data, in 12-months running average (anomalies relative to the first 30 years). The data are available monthly and averaging over 12 months removes a considerable amount of month-to-month ‘noise’. Showing only calendar-year averages would lose some information – e.g. it would only fully show peaks in temperature if by chance the maxima aligned with the calendar year. Fig. 2 The two El Niño peaks in global temperature from Fig. 1, zoomed in and overlayed by shifting the 2016 peak back in time by 14 years and down by 0.4 °C. The darker red curve is the 2016 peak, as in Fig. 1.
February 19, 2025 at 5:08 PM
realclimate.bsky.social
O Say Can You CO2… - 12 OCT 2017 www.realclimate.org/index.php/ar... #climatechange #science
Fig. Extreme heat and drought impacted the carbon cycle in tropical forests differently in different regions, leading to the fastest growth rate of CO2 in at least 10,000 years. (NASA/JPL-Caltech). OCO-2 has given us two revolutionary new ways to understand the effects of drought and heat on tropical forests. The instrument directly measures CO2 over these regions thousands of times every day (Crisp et al, 2004). These column-averaged concentration retrievals respond to the net amount of CO2 passing in and out of the atmosphere under the instrument. OCO-2 also senses the rate of photosynthesis by detecting fluorescent chlorophyll in the trees themselves (Frankenberg et al, 2011). Liu et al used observations of CO from the MOPITT instrument aboard NASA’s Terra satellite to identify CO2 released from upwind wildfires. They used solar-induced chlorophyll fluorescence (SIF) to quantify changes in plant photosynthesis (also called gross primary production, GPP). Their results include time-resolved maps of the sources and sinks of atmospheric CO2 that are optimally consistent with both mechanistic forward models and the CO2, CO, and SIF observed by the satellite instruments.
February 17, 2025 at 8:33 AM
realclimate.bsky.social
1.5ºC: Geophysically impossible or not? - 4 OCT 2017 www.realclimate.org/index.php/ar... #climatechange #science
Figure 1: (a) shows temperature change in the CMIP5 simulations relative to observed temperature products. Grey regions show model range under historical and RCP8.5 forcing relative to a 1900-1940 baseline. Right-hand axis shows temperatures relative to 1861-1880 (offset using HadCRUT4 temperature difference). (b) shows temperature change as a function of cumulative emissions. Black solid line shows the CMIP5 historical mean, and black dashed is the RCP8.5 projection. Colored lines represent regression reconstructions as in Otto (2015) using observational temperatures from HadCRUT4 and GISTEMP, with cumulative emissions from the Global Carbon Project. Colored points show individual years from observations. The choice of temperature data We can illustrate how these effects might influence the Millar analysis by repeating the calculation with alternative temperature data. Their approach requires an estimate of the forced global mean temperature in a given year (excluding any natural variability), which are derived from Otto et al (2015), who employ a regression approach to reconstruct a prediction of global mean temperatures as a function of anthropogenic and natural forcing agents. In Fig. 1(a), we apply the Otto approach to data from GISTEMP as well as the HadCRUT4 product used in the original paper – again using data up to 2014. Although the HadCRUT4 forced Otto-style reconstruction suggests 2014 temperatures were less than the 25th percentile of the CMIP5 distribution, following the same procedure with GISTEMP yields 2014 temperatures of 1.08K – corresponding to the 58th percentile of the CMIP5 distribution. Figure 2: (a) Temperatures of reconstructed global mean temperature in 2014 for the CESM large ensemble following the Otto (2015) regression methodology, plotted as a function of average global mean temperature in the years 2005-2014. (b) correlation between mean grid-point temperatures in 2005-2014 and reconstructed global mean temperature in 2014, ellipses show regions proposed for Pacific Climate Index. (c) CESM large ensemble reconstructed global mean temperature in 2014 as a function of Pacific Climate Index. Vertical lines show index values for observations in period 2005-2014 (solid) and historical (dashed). In order to assess how this potential bias might have been manifested in the historical record, we construct an index of this pattern using regions of strong positive and negative correlation to the inferred forced warming. Fig 2(b) shows the correlation between inferred forced 2014 temperature and 2005-2014 temperatures, showing a pattern reminiscent of the Interdecadal Pacific Oscillation, a leading mode of unforced variability. The warming estimate is positively correlated with central Pacific temperatures, and negatively correlated with South Pacific temperatures. An index of the difference between these regions is shown in Fig. 1(c) for observations and models. Both HadCRUT and GISTEMP suggest strongly negative index values for the period 2005-2014, suggesting a potential cold bias in the warming estimate due to natural variability of 0.1˚C (with 5-95% values of 0.05-0.15˚C).
February 17, 2025 at 8:30 AM
realclimate.bsky.social
Is there really still a chance for staying below 1.5 °C global warming? - 22 SEP 2017 www.realclimate.org/index.php/ar... #climatechange #science
Figure: Difference between modeled and observed warming in 2015, with respect to the 1861-1880 average. Observational data has had short-term variability removed per the Otto et al 2015 approach used in the Millar et al 2017. Both RCP4.5 CMIP5 multimodel mean surface air temperatures (via KNMI) and blended surface air/ocean temperatures (via Cowtan et al 2015) are shown – the latter provide the proper “apples-to-apples” comparison. Chart by Carbon Brief.
February 15, 2025 at 2:56 PM
realclimate.bsky.social
Impressions from the European Meteorological Society’s annual meeting in Dublin - 14 SEP 2017 www.realclimate.org/index.php/ar... #climatechange #science
The Helix at DCU was the main venue of #EMS2017
February 15, 2025 at 2:54 PM
realclimate.bsky.social
Why extremes are expected to change with a global warming - 5 SEP 2017 www.realclimate.org/index.php/ar... #climatechange #science
Typical probability density functions (pdfs) of temperature (left) and precipitation on rainy days (right). Fig. 2 shows predictions with a simple model that predicts the number of tropical cyclones (NTC and n) in the North Atlantic based on the area of warm sea surface (A) and the NINO3.4 index. It was created in R using the script tropical cyclones.R which also retrieves the data. The model was calibrated over the period 1900-1960, and the predictions provide reasonable similar evolution of the North-Atlantic tropical cyclones outside this period.
February 15, 2025 at 2:52 PM
realclimate.bsky.social
Data rescue projects - 17 AUG 2017 www.realclimate.org/index.php/ar... #climatechange #science
Data rescue projects It’s often been said that while we can only gather new data about the planet at the rate of one year per year, rescuing old data can add far more data more quickly. Data rescue is however extremely labor intensive. Nonetheless there are multiple data rescue projects and citizen science efforts ongoing, some of which we have highlighted here before. For those looking for an intro into the subject, this 2014 article is an great introduction.
February 14, 2025 at 9:29 AM
realclimate.bsky.social
Sensible Questions on Climate Sensitivity - 15 AUG 2017 www.realclimate.org/index.php/ar... #climatechange #science
Figure shows model Equilibrium Climate Sensitivity (ECS, blue, from PH17), compared with observationally- and model-derived Inferred/Instantaneous Climate Sensitivity (ICS, black and red). Circles denote medians, while the line denotes the 5-95% confidence interval. Solid lines indicate published estimates, dashed lines indicate PH17 and A17 values with Nic Lewis’ comments taken into account, and dot-dashed lines indicate PH17 and A17 values with both the Lewis correction and the Richardson et al (2015) correction.
February 14, 2025 at 9:26 AM
realclimate.bsky.social
Observations, Reanalyses and the Elusive Absolute Global Mean Temperature - 10 AUG 2017 www.realclimate.org/index.php/ar... #climatechange #science
Reanalysis Analysis Illustrating results from various reanalyses. “Reanalyses” are effectively weather forecasts you would have got over the years if we had modern computers & models available. Weather forecasts (the “analyses”) have improved because computers are faster & models more skillful. To track real changes in weather, you don’t want to have to worry about models changing. Reanalyses were designed to get around that by redoing the forecasts over again. One major caveat with these products; while the model isn’t changing over time, the input data is and there are large variations in the amount and quality of observations – particularly around 1979 when a lot of satellite observations came on line, but also later as the mix and quality of data changed. The advantage of reanalyses is they incorporate a huge amount of observations, from ground stations, ocean surface, remotely-sensed data from satellites etc.. In theory, you might expect them to give the best estimates of what the climate is. Here are the absolute global mean surface temperatures in five reanalysis products (ERAi, NCEP CFSR, NCEP1, JRA55 and MERRA2) since 1980 (data via WRIT at NOAA ESRL). (Using Kelvin here, but ºC and ºF later). There is a substantial spread in absolute temperatures in any one year (about 0.6K on average): fluctuations are relatively synchronous. The biggest outlier is NCEP1 which is the oldest product, but even without that one, the spread is about 0.3K. The means over the most recent climatology period (1981-2010) range from 287.2 to 287.7K. This range can be compared to an estimate from Jones et al (1999) (derived solely from surface observations) of 287.1±0.5 K for the 1961-1990 period. A correction for the different baselines suggests that for 1981-2010, Jones would also get 287.4±0.5K (14.3±0.5ºC, 57.7±0.9ºF)- reasonable agreement with reanalyses. NOAA NCEI uses 13.9ºC for the period 1901-2000 which is equivalent to about 287.5K/14.3ºC/57.8ºF for 1981-2010 period. Plotting these temperatures as anomalies (by removing the mean over a common baseline period) (red lines) reduces the spread, but it is still significant, and much larger than the spread between the observational products (GISTEMP, HadCRUT4/Cowtan&Way, and Berkeley Earth (blue lines)): Note that there is a product from ECMWF (green) that uses the ERAi reanalysis with adjustments for non-climatic effects that is in much better agreement with the station-based products. Compared to the original ERAi plot, the adjustments are important (about 0.1ºK over the period shown), and thus we can conclude that uncritically using the unadjusted metric from any of the other reanalyses is not wise. In contrast, the uncertainty in the station-based anomaly products are around 0.05ºC for recent years, going up to about 0.1ºC for years earlier in the 20th century. Those uncertainties are based on issues of interpolation, homogenization (for non-climatic changes in location/measurements) etc. and have been evaluated multiple ways – including totally independent homogenization schemes, non-overlapping data subsets etc. The coherence across different products is therefore very high. Combine harvesting So what can we legitimately combine, and what can’t we? Perhaps surprisingly, the spread in the seasonal cycle in the reanalyses is small once the annual mean has been removed. This is the basis for the combined seasonal anomaly plots that are now published on the GISTEMP website. The uncertainties when comparing one month to another are slightly larger than for the anomalies for a single month, but the shifts over time are still robust. But think about what happens when we try and estimate the absolute global mean temperature for, say, 2016. The climatology for 1981-2010 is 287.4±0.5K, and the anomaly for 2016 is (from GISTEMP w.r.t. that baseline) 0.56±0.05ºC. So our estimate for the absolute value is (using the first rule shown above) is 287.96±0.502K, and then using the second, that reduces to 288.0±0.5K. The same approach for 2015 gives 287.8±0.5K, and for 2014 it is 287.7±0.5K. All of which appear to be the same within the uncertainty. Thus we lose the ability to judge which year was the warmest if we only look at the absolute numbers. Now, you might think this is just nit-picking – why not just use a fixed value for the climatology, ignore the uncertainty in that, and give the absolute temperature for a year with the precision of the anomaly? Indeed, that has been done a lot. But remember that for a number that is uncertain, new analyses or better datasets might give a new ‘best estimate’ (hopefully within the uncertainties of the previous number) and this has happened a lot for the global mean temperature. Metaphor alert Imagine you want to measure how your child is growing (actually anybody’s child will do as long as you ask permission first). A widespread and accurate methodology is to make marks on a doorpost and measure the increments on a yearly basis. I’m not however aware of anyone taking into account the approximate height above sea level of the floor when making that calculation.
February 14, 2025 at 9:24 AM
realclimate.bsky.social
Joy plots for climate change - 22 JUL 2017 www.realclimate.org/index.php/ar... #climatechange #science
Joy plots for climate change 22 JUL 2017 BY GAVIN This is joy as in ‘Joy Division’, not as in actual fun. Many of you will be familiar with the iconic cover of Joy Division’s Unknown Pleasures album, but maybe fewer will know that it’s a plot of signals from a pulsar (check out this Scientific American article on the history). The length of the line is matched to the frequency of the pulsing so that successive pulses are plotted almost on top of each other. For many years this kind of plot did not have a well-known designation until, in fact, April this year
February 13, 2025 at 1:36 PM
realclimate.bsky.social
The climate has always changed. What do you conclude? - 20 JUL 2017 www.realclimate.org/index.php/ar... #climatechange #science
Fig. 1 Radiative forcing is the cause of global temperature changes. Red bars show warming, blue bars cooling effects. I am showing the diagram from the fourth IPCC report of 2007, because it is easier to understand than the more recent from the 5th IPCC Report of 2013, which Gavin discussed here. The overall human-caused radiative forcing, which is given here as 1.6 watts per square meter, had already risen to 2.3 watts per square meter by the year 2011 according to the 5th IPCC report. Source: IPCC report 4 Fig. SPM.2.
February 13, 2025 at 1:30 PM
realclimate.bsky.social
Red team/Blue team Day 1 - 15 JUL 2017 www.realclimate.org/index.php/ar... #climatechange #science
Cartoon of Dilbert vs. Michael Mann on climate modeling and really just how absurd the skepticism is.
February 10, 2025 at 3:44 PM
realclimate.bsky.social
Climate Sensitivity Estimates and Corrections - 12 JUL 2017 www.realclimate.org/index.php/ar... #climatechange #science
Relationship between the CO2/T regression and the actual ESS for two specific cases of our simple model when you run it with an 80,000 year orbital cycle. The ratio in the right hand figure and is almost everywhere greater than one, implying an overestimate when using the regression. As simple as possible (but no simpler) The essence of the comment is a model that we put together that (I think) is the simplest that you can derive that includes a carbon cycle, ice sheets, and allows for the standard ‘Charney’ sensitivity (ECS) and the ESS to vary independently. The documentation and R code for the model is part of the supplementary material, as is a Jupyter notebook for a python version (so knock yourself out if you want to explore it further). What this model shows is that if orbital variations in insolation impact ice sheets directly in any significant way (which evidence suggests they do Roe (2006)), then the regression between CO2 and temperature over the glacial-interglacial cycles (which was used in Snyder (2016)) is a very biased (over)estimate of ESS. The results from this model demonstrate clearly (and in line with our initial criticisms) that the Snyder (2016) suggestion of a very high ESS and committed warming is unfounded. The second example follows a few other papers in challenging the assumptions behind constraints on the Charney sensitivity derived from historical changes in temperature and forcings. These transient constraints have tended to come in lower than the other estimates based on paleo-climate or emergent constraints, and thus have been embraced by (let’s say) more ‘optimistic’ commentators (though until the mismatches are resolved it would be premature to only favor only one class of results).
February 10, 2025 at 3:40 PM
realclimate.bsky.social
Unforced variations: July 2017 - 1 JUL 2017 www.realclimate.org/index.php/ar... #climatechange #science
Unforced variations: July 2017 So, big news this week: The latest update to the RSS lower troposphere temperatures (Zeke at Carbon Brief, J. Climate paper) and, of course, more chatter about the red team/blue team concept. Comments?
February 10, 2025 at 3:37 PM