Editing IPCC:AR6/WGI/Chapter-Atlas (section)

=== Atlas.3.1 Global Atmosphere and Land Surface ===

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The principal atmospheric quantities of interest for understanding how climate change may impact human and ecological systems, as well as being key global indicators of change, are surface air temperature and precipitation. They are therefore a significant focus of the regional climate assessments in the following regional sections of the chapter ( [[#Atlas.4|Atlas.4]] to [[#Atlas.11|Atlas.11]] ) and of the Interactive Atlas. Changes in these variables over land during the recent past (1961–2015) are shown in Figure Atlas.11 using results from two global datasets (assessed in Chapter 2) to illustrate both where there is robust information on observed trends and observational uncertainty.

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'''Figure Atlas.11''' '''|''' '''Observed linear trends of signals in annual meansurface air temperature (a, b) and precipitation (e, f) in the Berkeley Earth, CRU TS and GPCC datasets (see Atlas.1 for dataset details).''' Trends are calculated for the common 1961–2015 period and are expressed as °C per decade for temperature and relative change (with respect to the climatological mean) per decade for precipitation. Crosses indicate regions where trends are not significant (at a 0.1 significance level) and the black lines mark out the reference regions defined in Atlas.1. Panels '''(c)''' and '''(d)''' display the period in which the signals of temperature change in data aggregated over the reference regions emerged from the noise of annual variability in the respective aggregated data. Emergence time is calculated for (c) Berkeley Earth (as used in (a)) and CRUTEM5. Regions in the CRUTEM5 map are shaded grey when data are available over less than 50% of the land area of the region. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

For temperature, a clear signal of warming is seen over most land areas with an amplification at high latitudes, though all continents apart from Africa also have regions where trends are not significant. Significant changes in annual mean precipitation are seen over much more limited areas though with consistent increasing trends over some northern high-latitude regions and decreasing trends over smaller regions in tropical Africa, the Americas and South West Asia. The information conveyed in Figure Atlas.11 on both consensus in the signal of change and on observational uncertainty is used in this chapter as a line of evidence to assess historical observed trends.

As an alternative way of viewing and summarizing information in the observational data, the panels (c) and (d) in Figure Atlas.11 show the time at which any significant temperature trends from the Berkeley Earth and CRUTEM5 datasets, averaged over the reference regions, emerged from interannual variability – with a signal-to-noise ratio greater than two ( [[#Hawkins--2020|Hawkins et al., 2020]] ). In the former, a regionally averaged warming signal has emerged over all of the land reference regions. In the latter, emergence times are only calculated for those regions which have data available in more than 50% of the land area (unlike Berkeley Earth, CRUTEM does not include spatial interpolation, see [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.3|Section 2.3.1.1.3]] ) and these are similar for all but one of the regions indicating that observational uncertainty does not change the main conclusion of widespread emergence of surface temperature signals over land regions.

As described earlier, information on projected future changes is required both at different time periods in the future under a range of emissions scenarios but also for different global warming levels. Figure Atlas.12 shows the global surface air temperature (GSAT) change projection calculated from the CMIP6 ensemble mean for the middle of the century under the SSP1-2.6 and SSP3-7.0 emissions scenarios compared to the end-of-century warming under SSP3-7.0 and for a global warming level of 2°C. The patterns of changes are similar to the observed warming and there is a high level of consistency with CMIP5 in terms of both patterns and magnitude of change (Interactive Atlas). However, for the long-term future, warming in the CMIP6 ensemble is generally higher, reflecting the increase in the top end of the range of climate sensitivities amongst the CMIP6 GCMs (Figure Atlas.1 3).

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'''Figure Atlas.12''' '''|''' '''Global temperature changes projected for mid-century under SSP1-2.6 (a) and SSP3-7.0 (c) compared with a 2°C global warming level (b) and the end of the century under SSP3-7.0 (d) from the CMIP6 ensemble.''' Note that the future period warmings are calculated against a baseline period of 1995–2014 whereas the global mean warming level is defined with respect to the baseline period of 1851–1900 used to define global warming levels. The other three SSP-based maps would show greater warmings with respect to this earlier baseline. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

Figure Atlas.12 demonstrates how temperature is projected to increase for all regions, and at a greater rate than the global average over many land regions, and with significant amplification in the Arctic. It also shows the higher mid-century warming and significantly higher end-of-century warming under the high-emissions SSP3-7.0 scenario compared to the low-emissions SSP1-2.6 scenario. Conversely, comparing the projected 2°C global warming level change with that projected additional warming compared to the recent past under the SSP1-2.6 scenario, demonstrates the much smaller additional warming projected under this low-emissions scenario. Finally, the maps display the CMIP6 ensemble mean projection, but it is important to explore the full range of outcomes from the ensemble, for example when undertaking a comprehensive risk assessment in which temperature is an important hazard. This can be explored regionally in the Interactive Atlas ( [[#Atlas.2|Atlas.2]] ) by viewing the time series of changes for all of the models within the ensemble over the AR6 WGI reference regions (Figure Atlas.2).

Changes in annual mean precipitation present a more complex picture with regions of decrease as well as increase, and areas where there is model disagreement on the sign of the change, even when the signal is strong in the long-term future period as shown in Cross-Chapter Box Atlas.1, Figure 1. However, as with the temperature changes, there is a high level of consistency in the patterns and magnitude of the precipitation changes, with changes in some areas being larger in the long-term future period. Considering changes over land, Cross-Chapter Box Atlas.1, Figure 1 also shows that at lower warming levels there are many regions, especially in the Southern Hemisphere, where there is no robust signal of change from the models.

In addition to displaying results from global model ensembles as maps of projected changes and their robustness or as time series of the projected temporal evolution of the median and range of a climate statistic, it is often useful to generate area-averaged summaries of these statistics under different future emissions scenarios or at specific global warming levels. This is demonstrated in Figure Atlas.1 3 and forms the basis of a common set of analyses, which are presented for the reference regions in the regional assessments in [[#Atlas.4|Atlas.4]] to [[#Atlas.11|Atlas.11]] . It shows the range of projected changes compared to the 1850–1900 and recent past 1995–2014 baseline periods for the CMIP5 and CMIP6 ensembles. The first four panels show: annual mean changes in temperature globally and over land only for various global warming levels and emissions scenarios and time periods (left pair), and then again globally and for global land, changes in precipitation and temperature at the same global warming levels (right pair). The second four panels provide the same temperature and precipitation information globally and for global land only in the December–February and July–August seasons. These results demonstrate the consensus between the two ensembles for increased warming over land areas and increases in global precipitation at all warming levels, and that global land precipitation increases more. They also show the increased precipitation response in December–January–February (DJF), reflecting the large precipitation increases in the Northern Hemisphere higher latitudes in winter. Finally, they demonstrate the greater warming projected by the CMIP6 ensemble, as an average over the ensemble and the upper end of the range. See [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] for an in-depth assessment of these results.

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'''Figure Atlas.13''' '''|''' '''Changes in annual mean surface air temperature and precipitation averaged over the global land–sea (left) and global land areas (right) in each horizontal pair of panels.''' The top-left two panels show the median (dots) and 10th–90th percentile range across each model ensemble for temperature change, for two datasets (CMIP5 and CMIP6) and two scenarios (SSP1-2.6/RCP2.6 and SSP5-8.5/RCP8.5). The first 12 bars represent the projected changes over three time periods (near-term 2021–2040, mid-term 2041–2060 and long-term 2081–2100) compared to the baseline period of 1995–2014, and the remaining four bars represent the additional warming projected relative to the same baseline to reach four global warming levels (GWLs; 1.5°C, 2°C, 3°C and 4°C). The top-right two panels show scatter diagrams of temperature against precipitation changes, displaying the median (dots) and 10th–90th percentile ranges for the same four GWLs, again representing the additional changes for the global temperature to reach the respective GWL from the baseline period of 1995–2014. In all panels the dark (light) grey lines or dots represent the CMIP6 (CMIP5) simulated changes in temperature and precipitation between the 1850–1900 baseline used for calculating GWLs and the recent-past baseline of 1995–2014 used to calculate the changes in the bar diagrams and scatter plots. Changes are absolute for temperature and relative for precipitation. The script used to generate this figure is available online ( [[#Iturbide--2021|Iturbide et al., 2021]] ) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

Global warming leads to systematic changes in regional climate variability via various mechanisms such as thermodynamic responses via altered lapse rates ( [[#Kröner--2017|Kröner et al., 2017]] ; [[#Brogli--2019|Brogli et al., 2019]] ) and land–atmosphere feedbacks ( [[#Boé--2014|Boé and Terray, 2014]] ). These can modify temporal and spatial variability of temperature and precipitation, including an altered seasonal and diurnal cycle and return frequency of extremes. Regional influences from and feedbacks with sea surface, clouds, radiation and other processes also modulate the regional response to enhanced warming, both locally and, via teleconnections, remotely.

Given their potential to influence extremes in temperature, precipitation and other climatic impact-drivers and hazards, and thus risks to human and ecological systems, it is important to understand these links for developing adaptations in response to clear anthropogenic influences on individual hazards. This will also support the related fields of disaster risk reduction and global sustainable development efforts ( [[#Steptoe--2018|Steptoe et al., 2018]] ). They demonstrated that 15 regional hazards shared connections via the El Niño–Southern Oscillation (ENSO), with the Indian Ocean Dipole, North Atlantic Oscillation and the Southern Annular Mode (see Annex IV) being secondary sources of significant regional interconnectivity (Figure Atlas.1 4). Understanding these connections and quantifying the concurrence of resulting hazards can support adaptation planning as well as multi-hazard resilience and disaster risk reduction goals.

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'''Figure Atlas.14''' '''|''' '''Influence of major modes of variability (see Annex IV) on regional extreme events relevant to assessing multi-hazard resilience.''' Ribbon colours define the driver from which they originate and their width is proportional to the correlation. Crossed lines represent where there is conflicting evidence for a correlation or where the driver is not directly related to the hazard; dots represent drivers that have both a positive and negative correlation with the hazard. Figure is copied from [[#Steptoe--2018|Steptoe et al. (2018)]] /CCBY4.0.

The main modes of variability influencing global and regional climate are comprehensively described in Annex IV. In the context of the assessment in the Atlas chapter, they are important because of their influence on the variability of temperature (Part A) and precipitation (Part B) in regions around the world. This is quantified in Table Atlas.1, which lists the fraction of interannual variance in seasonal mean temperature and precipitation explained by variability in these modes. The table provides information on the influence of the teleconnections for selected seasons for the interannual to decadal modes and at an annual scale for the multi-decadal modes. The columns related to the interannual to decadal modes focus on the seasons where these connections are strongest but each mode of variability will often have influences in other seasons (for more details see Annex IV). The table shows that for many regions, seasonal temperature and precipitation is substantially modulated by these modes of variability – all regions feel some influence, and variability in ocean basins often has influence in multiple remote regions.

'''Table Atlas.1''' '''|''' '''Regional mapping of the teleconnections associated with the main modes of variability (Annex IV).''' Fraction of surface air temperature and precipitation variance explained at interannual time scale by each mode of variability (columns) for each AR6 region (rows) based on the coefficient of determination R <sup>2</sup> . Units are in percent and non-significant values based on t-statistics at the 95% level of confidence are indicated by a white cell with a diagonal line. Grey cells represent regions where there is insufficient data to calculate any teleconnection. HadCRUT (HAD), GISTEMP (GIS), Berkeley Earth (BE), and CRU-TS (CRU) observed datasets are used to assess the strength of the teleconnection for surface air temperature, and GPCC and CRU-TS are used for precipitation. The colour scale given on label bars shown at the bottom quantifies the values of the explained variance and also stands for the sign of the teleconnection for the positive phase of the mode. All data are linearly detrended prior to the computation of the regression. Note that results are sensitive to the choice of the detrending function (linear, loess filter, 3-order polynomial function) but by a few percent at most, which is well below the range of the observational uncertainty assessed here through the use of several observational products. NAM: Northern Annular Mode; SAM: Southern Annular Mode; ENSO: El Niño–Southern Oscillation; IOB: Indian Ocean basin; IOD: Indian Ocean Dipole; AZM: Atlantic Zonal Mode; AMM: Atlantic Meridional Mode; PDV: Pacific Decadal Variability; AMV: Atlantic Multi-decadal Variability; DJF: December–January–February; MAM: March–April–May; SON: September–October–November; JJA: June–July–August.

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