I finished processing an updated #temperature dataset (DCENT_MLE_v1.1) using data from @lizk40.bsky.social and the DCENT team. According to this dataset, 2024 was 1.71 °C warmer than the 1850-1900 average with a 95% confidence interval of [1.57,1.85] °C. #climatechange #globalwarming

Look at all of this unnecessary traffic and waste of tax money due to the #King Charles visit in #Ottawa this morning. According to opinion polls, most Canadian's are against the #monarchy. This idea that #Canada should invite a foreign monarch to show #Trump how independent we are is crazy to me.

I have finished processing HadCRU_MLE_v1.3. 2024 was 1.62°C warmer than the late 19th century, with an 95% CI of [1.53,1.71]°C. The update also includes improved bias adjustments for 194 stations (doi.org/10.1002/joc....) and a sensitivity test with COBE-SST3 sea ice. #climate #change #globalwarming

I am glad to see that the local empirical Kriging approach has been applied, which should remove the small (0.003°C) underestimate of warming during the 1961-1990 reference period in CRUTEM5 (as I mentioned in my recent paper), as well as allow more stations to be used.

A new infilled global temperature dataset is now available: DCENT_MLE_v1. This dataset estimates more #globalwarming than other datasets: an increase of 1.59 °C from the late 19th century to 2023, with an uncertainty range of [1.48,1.72] °C.
www.wdc-climate.de/ui/entry?acr...

Some temperature datasets (e.g., CRUTEM5) calculate temperature anomalies relative to a reference period. Warming can be underestimated if the timing of observations during the reference period is not accounting for. Accounting for this increased my #globalwarming estimates by about 0.003°C.

My estimate of 0.079°C for the impact of sea ice change on warming since the late 19th century may be overestimated by a factor of two since I modeled the surface temperature as discontinuous between open sea and sea ice regions, which can cause some double counting of warming.

Applying freezing temperatures to observations of sea surface temperatures or ice surface air temperatures can cause an overestimation of the impact of sea ice loss on #globalwarming. My research paper estimates that such an overestimation is likely small (< 0.01°C).

If a sea surface temperature anomaly of -1.8°C is observed in a region of sea ice loss, some datasets use methodologies that can estimate zero warming for this region since sea water generally cannot be colder than -1.8°C. This can result in an underestimation of #globalwarming!

Regions of melting sea ice are among the fastest warming regions of the planet. Sea ice loss increases absorption of sunlight and allows for more heat transfer between the water and air. Accounting for sea ice changes increased my #globalwarming estimates by about 0.079°C.

Figure 1 of my paper illustrates how using temperature anomalies of observed regions (black dots) and a spatially non-uniform trend to estimate temperature anomalies of unobserved regions (blue dot) can improve estimates compared to when using a uniform trend (red dot). #climate

Temperature datasets typically model non-uniform warming across the planet as residuals relative to trends of spatially uniform warming. When ignoring sea ice changes, I found that accounting for non-uniform warming trends increased #globalwarming estimates by about 0.006°C.