dataesr/scientific-paragraphs-categorization huggingface dataset:
(This is equivalent to the method used by the Hadley Centre to report global values from HadCRUT5). The values thus calculated may differ from those reported by the data providers themselves, due to different averaging methodologies. Although the difference is less pronounced in the AR6 datasets than in earlier generations of datasets, there are more gridpoints with missing data in the Southern Hemisphere than the Northern (particularly before an observation network was established on Antarctica in the 1950s), and using hemispheric means ensures that the two hemispheres are equally weighted. The uncertainty assessment in AR6 combines the spread of the individual datasets with uncertainties derived from ensembles for HadCRUT5 and an earlier version of NOAAGlobalTemp, with the other two datasets assumed to have the same uncertainty as HadCRUT5.
HadCRUT5 is the only one of the datasets for which regularly updated ensembles are currently produced, limiting the extent to which uncertainty assessments can be regularly updated from those used in AR6. In this update it was assumed that the width of the confidence interval for each individual dataset was the same as that used in AR6. Based on the updates available as of February 2023 (which were reported in the AR6 SYR), the change in GMST from 1850-1900 to 2013-2022, using the same underlying data sets and methodology as AR6, is 1. 15 [1. 00-1. 25] °C, an increase of 0. 06 °C within two years from the 2011-2020 value reported in the AR6 Working Group I report. The change from 1850-1900 to 2003-2022 was 1. 03 [0. 87-1. 13] °C, 0. 04 °C higher than the earlier value reported in the AR6 Working Group I report.
Time series of heating associated with loss of ice and warming of the atmosphere and continental land surface are obtained from the recent Global Climate Observing System (GCOS) initiative  (von Schuckmann et al. , 2023b; Adusumilli et al. , 2022; Cuesta-Valero et al. , 2023; Vanderkelen and Thiery, 2022; Nitzbon et al. , 2022b; Kirchengast et al. , 2022). At the time of writing, only four of the five OHC datasets used in the AR6 assessment are available with updated annual values to 2022. Therefore, our approach is to use the original AR6 time series ensemble OHC time series for the period 1971-2018 and then switch to a smaller four-member ensemble for the period 2019-2022. We "splice" the two sets of time series by adding an offset as needed to ensure that the 2018 values are identical. The AR6 heating rates and uncertainties for the ocean below 2000 m are assumed to be time-constant.
(2021). The update made here uses the same subset of 22 CMIP6 models. However, HadCRUT5 observations are used, instead of previous datasets, over an extended 1850-2022 period. Consistent with the AR6 assessment about GMST to GSAT warming ratio, no scaling correction is applied, i. e. , the global mean value from HadCRUT5 is assumed to be representative for GSAT changes (see Sect. 7. 1. 2). As it relies on available CMIP6 simulations, this update assumes that the world has followed a SSP2-4. 5 pathway since 2015. Emissions in the SSP scenarios are similar in the period up until 2022, and close to those which have occurred (e. g. Chen et al. , 2021) , therefore this is a reasonable approximation. Future updates with this method will incorporate new observations. In parallel, we will try to replace the CMIP6 models by emulators, thus allowing the latest available estimates of radiative forcings to be considered, instead of the SSP2-4.
AR6 WGI reassessed all five terms  (Canadell et al. , 2021)  and updated the incorporation of factor (v)  (Lamboll and Rogelj, 2022). Of these factors, only factor (i) (human-induced warming) lends itself to a regular and systematic annual update. If humaninduced warming estimates are only available for decade-long periods (such as the 2010-2019 period in WGI AR6), up-todate historical CO2 emissions from the middle of this period until the start of the RCB are required to have an as up-to-date estimate as possible. However, if human-induced warming estimates are available up to the preceding year, RCB estimates can be derived without the need for estimates of historical CO2 emissions. Other factors can be updated, but depend on new evidence and insights being published rather than an additional year of observational data becoming available.
Factor (iv) (temperature contribution of non-CO2 emissions) depends both on the available scenario evidence and the assessment of non-CO2 warming. Additional scenario evidence has become available through the publication of the AR6 WGIII report  (Byers et al. , 2022)  which is taken into account in this update. The RCB for 1. 5°C, 1. 2). Based on the variation in non-CO2 emissions across the scenarios in AR6 WGIII scenario database, the estimated RCB values can be higher or lower by around 200 GtCO2 depending on how deeply non-CO2 emissions are reduced. The impact of non-CO2 emissions on warming includes both the warming effects of other greenhouse gases such as methane and the cooling 1055 effects of aerosols such as sulphates. The impacts of these are assessed using a climate emulator (MAGICC,  Meinshausen et al.
2  (Seneviratne et al. , 2021). Berkeley Earth provides daily maximum temperatures and we require more than 99% data availability for each individual year and grid, such that years with more than four missing days are removed. Based on this criterion, Berkeley Earth covers at least 95% of the global land area from 1955 onwards. ERA5, on the other hand, has full spatiotemporal coverage by design, and hence the entire currently available time period of 1950 to 2022 is used. The annual maximum temperature is then computed for each grid cell, and a global area-weighted average is calculated for all grid cells with at least 90% temporal completeness in the respective available period  (1955-2021 and 1961-2018  for Berkeley Earth and HadEX3, while ERA5 is again not affected by this criterion). We thus enforce high data availability to adequately calculate global land-averaged TXx across all three datasets, but their coverage is not identical which introduces minor deviations in the estimated global land averages.
The resulting TXx timeseries are then computed as anomalies with respect to a baseline period of 1961-1990. To express the TXx as anomalies with respect to 1850-1900 we add an offset to all three datasets. The offset is based on the Berkeley Earth data and is derived from the linear regression of land-mean TXx to the annual mean global mean air temperature Our climate has warmed rapidly in the last few decades, which also manifests in changes in the occurrence and intensity of climate and weather extremes. We visualise this with land-averaged annual maximum temperatures (TXx) from three different datasets (ERA5, Berkeley Earth and HadEX3), expressed as anomalies with respect to the pre-industrial baseline period of 1850-1900 (Figure  9 ). From about 1980 onwards, all employed datasets point to a strong TXx increase, which coincides with the transition from global dimming, associated with aerosol increases, to brightening, associated with decreases  (Wild et al.
, 2005). Together with strongly increasing greenhouse gas emissions (Sect. 2), this explains why human-induced climate change has emerged at an even greater pace in the last four decades than previously. For example, land-averaged annual maximum temperatures have warmed by more than 0. 5 °C in the past 10 years (1. 72 °C with respect to pre-industrial conditions) compared to the first decade of the millennium (1. 22 °C; Table  9 ). Since the offset relative to our pre-industrial baseline period is calculated relative to 1961-1990, within the latter period, temperature anomalies align by construction but can diverge This suggests that both our choice of datasets and approach to calculate anomalies does not affect our conclusion -the.
Discussion and conclusions 1175 The first year of the Global Climate Change (IGCC) initiative has built from the AR6 report cycle to provide a comprehensive update of the climate change indicators required to estimate the human induced warming and the remaining carbon budget. Table  10  presents a summary of the headline figures from each section compared to that given in the AR6 assessment. The only substantive data change since AR6 is that land-use CO2 emissions have been revised down by around 1 GtCO2e. However, as CO2 ERF and human induced warming estimates depend on concentrations, not emissions, this does not affect most of the 1180 other findings. Note it does slightly increase the remaining carbon budget, but this is only by 5 GtCO2, less than the 50 GtCO2 rounding precision. The 1.
5°C budget is becoming very small with about a 1-in-3 chance that it is already exhausted (RCB estimate for 67% is zero). The RCB will exhaust before the 1. 5°C threshold is reached due to non-CO2 warming that is still to be expected when CO2 transitions to net zero. It is hoped that this update can support the science community in its collection and provision of reliable and timely global climate data. In future years we are particularly interested in improving SLCF updating methods to get a more accurate estimate of short-term ERF changes. The work also highlights the importance of high-quality metadata to document changes in methodological approaches over time. In future years we hope to improve the robustness of the indicators presented here but also extend the breadth of indicators reported through coordinated research activities.
Methods Generally, scientists and scientific organisations such as WMO and IPCC have an important role as "watchdogs" to critically inform evidence-based decision making. This annual update traced to IPCC methods can provide a reliable, timely source of trustworthy information. As well as helping inform decisions, we can use the update to track changes in dataset homogeneity between their use in one IPCC report and the next. We can also provide information and testing to motivate updates in methods that future IPCC reports might choose to employ. Figure  8  shows decadal trends for the attributed warming and ERF. These trends were unprecedented at the time of AR6 and have increased further since then (red markers), showing that human activities are consistently causing global warming of more than 0.
2 °C per decade. As nations and businesses forge climate policies and take meaningful action, our assessment shows that global actions are not yet at the scale to manifest a substantive shift in the direction of travel for global warming. Indeed, our results point to the opposite: continued high levels of greenhouse gas emissions, combined with improvements in air quality, are reducing the level of aerosol cooling -leading to an unprecedented rate of human-induced warming. Both AR6 WGI and WGIII reports highlighted the benefits of short-term reductions in methane emissions to counter the loss of aerosol cooling and further improve air quality -however, at the global scale, methane emissions are at their highest level and rising (see Table  1 ). Policy makers, civil society and the scientific community require monitoring data and analyses from rigorous, robust assessments available on a regular basis.
Acknowledgements  Piers Forster ,  Debbie Rosen ,  Joeri Rogelj  and  Robin Lamboll  were supported by the  EU   Horizon 2020 Research and Innovation Programme  grant no. 820829  (CONSTRAIN). Chris Smith  was supported by a  NERC/ IIASA collaborative research fellowship  ( NE/T009381/1 ). Matthew D. Palmer ,  Colin Morice  and  Rachel Killick  were supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS. William F. Lamb  and  Jan C. Minx  were supported by the  ERC-2020-SyG "GENIE"  (grant  ID 951542 ). Pierre Friedlingstein ,  Glen P. Peters  and  Robbie M. Andrew  were supported by  EU   Horizon 2020 Research and Innovation Programme  grant no. 821003  (4C). HadEX3  [3. 0. 4] data were obtained from https://www. metoffice. gov. uk/hadobs/hadex3/ on 05. 04. 2023 and are © British Crown Copyright, Met Office, 2022, provided under an Open Government Licence http://www.
Results Results are summarised in Table  7  and Figure 5. WGI AR6 results for the period average 2010-2019 are quoted in Table  7 (a), compared with a repeat calculation using updated methods and datasets, and finally updated for the 2013-2022 period. Results from SR1. 5 are quoted in Table  7 (b) for the 2017 single-year level of human-induced warming, compared with a repeat calculation using the updated selection of methods and datasets (see Sect. 7. 2) and the WGI AR6 multi-method assessment approach (see Sect. 7. 3. 2), and finally updated for 2022. More details of the attributable warming timeseries and comparison across the three methods are also given in Appendix B. The repeat calculations for attributable warming in 2010-2019 exhibit good correspondence with the results in WGI AR6 for the same period, (see also Appendix B, Figure  B1 ), with an exact correspondence in the best estimate and likely range of human-induced warming (Ant).
The attribution assessment in WGI AR6 implied that, for the 2010-2019 decade-average, almost all observed warming was human-induced, with natural forcings only a minor contributor; this remains true for the 2013-2022 period. The repeat calculation for the level of attributable anthropogenic warming in 2017 is about 0. 1°C larger than the estimate provided in SR1. 5; this upward correction is due to changes in observational understanding since SR1. 5 (see WGI AR6 Chapter 2 Cross-Chapter Box 2. 3, Table  1 ,  Gulev et al. , 2021) ; the attribution method (GWI) used in SR1. 5 used HadCRUT4, whereas this update uses HadCRUT5. The updated results for present-day single-year warming contributions in 2022 are therefore higher than in 2017 due to both observational dataset updates and five additional years of anthropogenic forcing.
06 [0. 00 to 0. 14] W m -2 for 1750-2022. We determine from provisional data that aviation activity in 2022 had not yet returned to pre-COVID levels. Therefore, ERF from contrails and contrail-induced cirrus is lower than AR6, at 0. 05 [0. 02 to 0. 09] W m -2 in 2022 compared to 0. 06 [0. 02 to 0. 10] W m -2 in 2019. The headline assessment of solar ERF is unchanged, at 0. 01 [-0. 06 to +0. 08] W m -2 from pre-industrial to the 2009-2019 solar cycle mean. Separate to the assessment of solar forcing over complete solar cycles, we provide a single year 2022 solar ERF of 0. 06 [-0. 02 to +0. 14] W m -2. This is higher than the single year estimate of solar ERF for 2019 (a solar minimum) of -0. 02[-0. 08 to 0. 06] W m -2. For volcanic ERF, updating of the pre-industrial dataset for sAOD from eVolv2k v3 to HolVol v1 increased the sAOD over 500 BCE to 1749 CE, resulting in a larger difference to post-1750 sAOD and resulting in a volcanic ERF difference of +0.
015 W m -2 compared to AR6. In addition, the earlier Holocene was more volcanically active than the period after 500 BCE, further increasing the mean sAOD in the pre-industrial. Taking the longer baseline period into account in HolVol, post-1750 ERF is https://doi. org/10. 5194/essd-2023-166 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License. further increased by 0. 031 W m -2. The net effect is that volcanic forcing after 1750 has increased by +0. 046 W m -2 compared to AR6 due to dataset updates and by account of the fact that the post-1750 period was less volcanically active on average than the early Holocene which is now used in the ERF calculation. 605 https://doi. org/10. 5194/essd-2023-166 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4.
Additionally to IPCC reports, the World Meteorological Organisation (WMO) has developed a set of indicators for global climate monitoring (Trewin et al. , 2020)  and annually publishes 'state of the climate' reports with a global surface temperature estimate for the year of publication. For example, their 2022 "State of the Global Climate Report" gave an estimated global surface temperature for the 2013-2022 decade of 1. 14 [1. 02-1. 27] °C above the 1850-1900 average(WMO, 2023). Although https://doi. org/10. 5194/essd-2023-166 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License. the latest WMO report has worked to increase its consistency with AR6, its global surface temperature number is not directly comparable to the longer term averages given in AR6. WMO employ different datasets and incorporate reanalyses in their assessment of observed global temperature change, which AR6 did not: due to the lack of reanalysis data before 1980.
The Bulletin of the American Meteorological Society (BAMS) State of the Climate report is not directly comparable as it gives anomalies against a 1991-2020 mean and does not integrate across different datasets. There are choices around the methods used to aggregate surface temperatures into a global average, how to correct for systematic errors in measurements, methods of infilling missing data, and whether surface measurements or atmospheric temperatures just above the surface are used. These choices, and others, affect temperature change estimates and contribute to uncertainty (IPCC AR6 WGI Chapter 2, Cross Chapter Box 2. 1, Gulev et al. , 2021). Surface temperature information on land and sea is available with low latency through WMO distribution channels, with monthly station data from a substantial number of stations reported within a few days of the end of the month.
These are consolidated into global data sets by a number of institutions, making it feasible to report GMST updates within a few weeks of the end of the period of interest. The number of reporting locations with near-real time data available for reporting for the most recent periods is typically less than that available for historical data, as not all observation sites report recent data reliably, but this lower observation density only slightly increases the uncertainty in estimates of recent annual GMST compared with the past 20-30 years Trewin et al. , (2020). The GMST assessment in AR6 was based on four datasets: HadCRUT5 (Morice et al. , 2021) , Berkeley Earth(Rohde and    Hausfather, 2020), NOAAGlobalTemp -Interim (Vose et al. , 2021)  andKadow et al. (2020). (A fifth data set, China-MST (Sun et al.
, 2021)  was used for the land assessment only). The four GMST data sets were chosen by virtue of being quasiglobally complete, having data back to 1850, using the most recent generation of SST analyses, and using analysed (rather than climatological) values over sea ice. The first two of these are routinely updated operationally, with data for each year becoming available in the first few weeks of the following year. NOAAGlobalTemp -Interim was not updated operationally at the time AR6 was published, but has become NOAA's main operational GMST dataset (under the name NOAAGlobalTemp 5. 1) as of January 2023. All three data sets are updated and published monthly. Kadow et al. is updated on an ad hoc basis by the authors. To date, all four data sets remain supported with only minor version changes (if any) since AR6, but it is likely that more substantive version changes will occur to one or more over time, lessening direct comparability with AR6.
https://doi. org/10. 5194/essd-2023-166 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License. assessment of ocean heat content change for the 0-2000 m layer was based on global annual mean time series from five ocean heat content datasets: IAP (Cheng et al. , 2017) ;Domingues et al. , (2008); EN4(Good et al. , 2013);Ishii et al. , (2017); NCEI(Levitus et al. , 2012). Four of these datasets routinely provide updated OHC time series for the BAMS State of the Climate report, and all are used for the GCOS Earth heat inventory(von Schuckmann et al. , 2020; 2023a)  and the annual WMO global state of the climate. The uncertainty assessment for the 0-2000 m layer used the ensemble method described by Palmer et al. (2021) that separately accounts for parametric and structural uncertainty.
The >2000 m OHC change and associated uncertainty was assessed based on trend analysis of the available hydrographic data following Purkey and Johnson (2010). All five of the datasets used for the 0-2000 m OHC assessment are now updated at least annually and should in principle support an AR6 assessment time series update within the first few months of each year. There is potential to increase the observational ensemble used in the assessment by supplementing this set with additional data products that are also available annually for future updates.
Chen et al. , 2021). As inGillett et al. (2021), internal variability was estimated from intra-ensemble anomalies. Whereas theGillett et al. (2021)  results assessed by Eyring et al. (2021)  were based on HadCRUT4, this dataset is no longer being updated, and therefore we use the non-infilled version of HadCRUT5 here (Morice et al. , 2021). As shown byGillett et al. (2021), using HadCRUT5 in place of HadCRUT4 results in a 7% increase in the best-estimate of anthropogenic warming for 2010-2019. Gillett et al. (2021)  regressed 34 5-yr means of GMST over the period 1850-2019 onto simulated GMST over the same period.
2 from Seneviratne et al. , 2021. Like the analysis of global mean temperature, the choice of data sets is based on a compromise on the length of the data record, the data availability, near-real time updates and long-term support. As the indicator (in its current form) averages over all available land grid points, the spatial coverage should be high to obtain a meaningful average, which further limits the choice of datasets. The HadEX3 dataset (Dunn et al. , 2020) , which is used for Figure11. 2 in Seneviratne et al. (2021) , is static and does not cover years after 2018. We therefore additionally include the Berkeley Earth Surface Temperature dataset (Rohde et al. 2013) , and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5; Hersbach et al. , 2020). Berkeley Earth data currently enable an analysis of annual indices up to 2021 while ERA5 is updated daily with a latency of about 5 days (and the final release occurs after 2-3 months).
2008). A reforecast set consists of ensemble forecasts of 11 members (10 perturbed 1 control) issued for the same calendar day of the year as the operational forecast over each of the past 20 years. ERA5 reanalysis provides the initial conditions for the reforecasts. This reforecast set with 220 integrations (20 years 3 11 members) allows for evaluation of the model climatology of operational forecasts. We retrieve forecasts and the corresponding reforecasts of 2-m temperature (T2m), zonal and meridional components of 100-m wind speed, and geopotential at 500 hPa issued during boreal winter (DJF) on a global grid between December 2016 and February 2020. The retrieved spatial resolution is 0. 98 and the temporal resolution is 6 h (instantaneous values at 0000, 0600, 1200, and 1800 UTC). The data are retrieved from the Meteorological Archival and Retrieval System (MARS) of the ECMWF.
The 100-m wind speed (U100) is computed as the square root of the sum of squares of zonal and meridional components. The geopotential height (Z500) is computed by dividing the geopotential by Earth's gravitational acceleration g (59. 806 m s 22 ). As the prediction model is undergoing periodic improvements, the dataset used in this study consists of forecasts and reforecasts from several versions (CY43R1, CY43R3, CY45R1, and CY46R1)  (Vitart et al. 2019). Nevertheless, the differences in model formulation and hence the statistics between different versions are marginal (refer to appendix A in  Goutham et al. 2022). We focus on boreal winter in this study as this season experiences high variability in wind energy production in addition to increased energy demand mainly for space heating. Furthermore, predictions are more skillful in winter compared with other seasons due to stronger boundary conditions (e.