The Madden-Julian oscillation (MJO) is characterized by eastward propagating convection anomalies in the Indian and Pacific tropical oceans with a period of 30-60 days (Madden & Julian, 1971, 1972. In addition to being a dominant intraseasonal variability in the tropics, the state of the MJO has also been connected to weather at higher latitudes (e.g., Arcodia et al., 2020;Henderson et al., 2016), among many other aspects of global atmospheric circulation. Reviews on the significance of the MJO appear in Jiang et al. (2020) andZhang (2005).The influence of the MJO on the global climate system is dependent on the spatial pattern and amplitude of the MJO signal, which are often quantified using an index generated by empirical orthogonal functions (EOFs). The phase of the MJO, based on these indices, is used to predict or otherwise characterize the influence of the MJO on various atmospheric phenomena (J. Wang et al., 2020). One frequently used index is the real-time multivariate (RMM) index (M. C. Wheeler & Hendon, 2004). The RMM is based on the zonal structure of OLR and zonal wind at 200 and 850 hPa, and can be used for real-time monitoring of the MJO. However, Straub (2013) showed that the RMM underrepresents convection compared to zonal wind, causing the RMM to miss some MJO-like convective signals. In addition, the lack of meridional structure confounds the MJO signal with equatorial Kelvin waves (Roundy et al., 2009).An OLR-based MJO index (OMI) was developed to counteract some of these issues, since it incorporates the zonal and meridional structure of OLR into a pair of propagating EOFs over the course of the year (Kiladis et al., 2014) (hereafter K14). Incorporation of meridional structure helps separate the MJO signal from Kelvin waves, and using solely OLR more directly tracks the convective signal. Since OLR can be measured directly by satellites, the OMI provides a reliable long-term record of tropical convective patterns (S. Wang, 2019).Due to these benefits, the OMI is a widespread index used for MJO analyses. However, there remain a few small but important issues with the original OMI. Since the EOFs of the OMI represent propagating waves of the MJO, the leading EOF pair are of a similar magnitude (North et al., 1982), resulting in a somewhat arbitrary choice when assigning the EOFs as EOF1 or EOF2. In the original OMI calculation, the leading pair of eigenvalues

Introduction BackgroundIn June and July, 2019, Alaska experienced an extended period of record high temperatures. Temperatures at several weather stations broke all-time records; in Anchorage, temperatures reached 32°C, breaking records by 3°C (Di Liberto, 2019). Temperatures remained abnormally high from approximately June 23-July 10, with Anchorage breaking 27°C for six consecutive days (another record).In Alaska, increasing temperatures have significant societal and economic effects: degrading permafrost can result in damages to roads and infrastructure as soils sink (Melvin et al., 2017), shifting marine ecosystems can cause serious damage to Alaska's vitally important fish and crab industry (Thorsteinson & Love, 2016), and increasing wildfire risk can pose risk to many communities (Young et al., 2017, Yu et al., 2021. The risk of highly active and damaging fire seasons such as in the summer of 2015 has been shown to have increased by 35%-60% due to anthropogenic forcing (Partain et al., 2016). Damages in Alaska due to human-induced climate change are expected to range from $110 to $270 million per year and will disproportionately affect Alaska's rural and Indigenous communities (Cochran et al., 2013;Gray et al., 2018). The extent of future warming and expected frequency of extreme heat events in the region are still uncertain, and thus it is vital to increase our understanding of observed events and improve our ability to quantify present and future climate risk. Extreme heat events have frequently been observed in Arctic regions in the last decade (Thoman & Brettschneider, 2016), likely the result of anthropogenic climate change. Observations can be used to quantify changes in temperature over the past several decades, while climate models can be used to project future warming-which may accelerate-and changes in frequency of extreme heat events. Studies analyzing observed climate identify clear warning signals over the Arctic at a faster rate than much of the planet; this polar Abstract Extreme heat occurred over Alaska in June-July 2019, posing risks to infrastructure, ecosystem, and human health. It is vital to improve our understanding of the causes of such events and the extent to which anthropogenic forcing may alter their likelihood and magnitude. Here, we use multiple large ensembles of climate models, comprising thousands of simulated years, to investigate these issues. Our results suggest that the presence of anthropogenic radiative forcing increased the likelihood of the 2019 extreme heat event by as much as 6%. Further we show the rate of occurrence of such an extreme heat event is likely to substantially increase in the future with increasing levels of atmospheric greenhouse gases. While uncertainty in projected climate risk from model choice leads to a broad range of future extreme heat event probabilities, some models project that with rapidly increasing levels of greenhouse gases the likelihood of such events would exceed 75% by 2090.Plain Language Summary An extended period of high temperatu...

Various indices have been defined to characterize the phase and amplitude of the Madden-Julian oscillation (MJO). One widely used index is the Outgoing Longwave Radiation (OLR) based MJO index (OMI), which is calculated using the spatial pattern of 30-96-day eastward-filtered OLR. The EOFs used to calculate the OMI in observations are prone to degeneracy and exhibit oscillations on the order of 10-20 days, despite initial filtering of the OLR. We propose a simple modification to the OMI that involves aligning the EOFs between neighboring days while retaining the spatial pattern described by the EOFs. This rotation method is implemented as a postprocessing step of the current OMI calculation and cleanly removes the spurious oscillations and degeneracy issues seen in the standard method. A similar rotation procedure can be implemented in calculations of other MJO indices.