Fig. 5.1.
Annual mean (Jan–Dec) Arctic (red lines) and global (blue lines) surface air temperature anomalies (°C) for (a) land and ocean areas, (b) land-only, and (c) ocean-only for 1900–2022. Spatial domains are listed in each panel. (Source: NASA GISTEMP v4.)
Fig. 5.2.
Near-surface (925-hPa) air temperature anomaly maps (°C) for each season during 2022: (a) winter (Jan–Mar), (b) spring (Apr–Jun), (c) summer (Jul–Sep), and (d) autumn (Oct–Dec). Temperature anomalies are shown relative to the 1991–2020 means. (Source: ERA5 reanalysis.)
Fig. 5.3.
Sea-level pressure (hPa) anomaly maps for each season during 2022: (a) winter (Jan–Mar), (b) spring (Apr–Jun), (c) summer (Jul–Sep), and (d) autumn (Oct–Dec). Anomalies are shown relative to the 1991–2020 means. (Source: ERA5 reanalysis.)
Fig. 5.4.
Seasonal departures of 2022 precipitation (cm) from the 1991–2020 climatological means for the Arctic seasons: (a) winter (Jan–Mar), (b) spring (Apr–Jun), (c) summer (Jul–Sep), and (d) autumn (Oct–Dec). Blue shades denote above-normal precipitation; red shades denote below-normal precipitation. (Source: ERA5 reanalysis.)
Fig. 5.5.
Time series of Arctic (60°N–90°N) precipitation, expressed as percent departures from the corresponding 1991–2020 averages (%), for (a) the calendar years 1950–2022 and for each three-month Arctic season: (b) winter (Jan–Mar), (c) spring (Apr–Jun), (d) summer (Jul–Sep), and (e) autumn (Oct–Dec). Results are from ERA5 (green lines; “×” denotes value based in part on the ERA5 preliminary product for December 2022) and GPCC 1.0° data (black lines; “o” and “+” denote values based on GPCC monitoring and first-guess products, respectively). GPCC values are for land only, and ERA5 values are for land and ocean. Linear trends and are shown in lower right of each panel. All trends are significant at p <0.001.
Fig. 5.6.
Precipitation trends (cm decade−1) over the period 1950–2022 for the Arctic seasons: (a) winter (Jan–Mar), (b) spring (Apr–Jun), (c) summer (Jul–Sep), and (d) autumn (Oct–Dec). Green shades denote trend increases and brown shades denote trend decreases. Stippling denotes trend significance at the 0.05 level. (Source: ERA5.)
Fig. 5.7.
Trends of daily extreme precipitation indices (% decade−1) over the period 1950–2021. Plots are shown for yearly maximum one-day total precipitation (Rx1; upper left), yearly maximum five-day amount (Rx5; upper right), yearly maximum number of consecutive wet days (CWD; lower left), and yearly maximum number of consecutive dry days (CDD; lower right). Green shades denote trends toward wetter extremes; brown shades denote trends towards drier extremes. Stippling denotes trend significance at the 0.05 level. (Source: ERA5.)
Fig. SB5.1.
The historical temperature ranking (T2m) of the 2022 mean air temperature compared to the 1950–2022 period. Note how many regions experienced air temperature rankings among the five highest temperatures on record, with extremely warm regions in the Barents Sea, central Greenland, and parts of Siberia.
Fig. SB5.2.
An ice arch in the Nares Strait between Canada and Greenland which typically appears in the winter such as in 2021 (left) but was absent in 2022 (right). Credit: European Union, Copernicus Sentinel-3 imagery. (Source: https://www.copernicus.eu/en/media/image-day-gallery/absence-ice-arch-nares-strait-2022-winter.)
Fig. SB5.3.
A summary of reported extreme event categories in the arctic in 2022. Cyclones and wind events may overlap. The total number of recorded events for 2022 was 56, and the summary is based on collected events from meteorological services connected to the Arctic, except from Russia. (Sources: National meteorological services associated with the Arctic.)
Fig. 5.8.
(a) Mean sea-surface temperature (SST; °C) in Aug 2022. Black contours indicate the 10°C SST isotherm. (b) SST anomalies (°C) in Aug 2022 relative to the Aug 1991–2020 mean. (c) Difference between Aug 2022 SSTs and Aug 2021 SSTs (negative values indicate where 2022 SSTs were lower). White shading in all panels is the Aug 2022 mean sea-ice extent. Black lines in (b) and (c) indicate the median ice edge for Aug 1991–2020. The regions marked by blue boundaries and the white dashed lines indicating 65°N in (b) and (c) relate to data presented in Fig. 5.10. Sea-ice concentration data are the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, version 4 (https://nsidc.org/data/g02202) and Near-Real-Time NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, version 2 (https://nsidc.org/data/g10016; Peng et al. 2013; Meier et al. 2021a,b), where a threshold of 15% concentration is used to calculate sea-ice extent.
Fig. 5.9.
Sea-surface temperature (SST) anomalies (°C) for (a) Jun 2022, (b) Jul 2022, (c) Aug 2022, and (d) Sep 2022 relative to the 1991–2020 mean for the respective month. The sea-ice concentration for the corresponding month is also shown. The evolution of sea-ice concentration over the months of Jun to Aug illustrates why it is not appropriate to evaluate long-term SST trends in Jun and Jul over most of the Arctic marginal seas, which still have significant sea-ice cover in those months. While sea-ice extent is lowest in Sep, SSTs cool in the latter part of the month (see text). The black dashed circle indicates the latitudinal bound of the map images shown in Figs. 5.8 and 5.10. See Fig. 5.8 caption for sea-ice dataset information.
Fig. 5.10.
(a) Linear sea-surface temperature (SST) trend (°C yr−1) for Aug of each year from 1982 to 2022. The trend is only shown for values that are statistically significant at the 95% confidence interval; the region is shaded gray otherwise. White shading is the Aug 2022 mean sea-ice extent, and the black line indicates the median ice edge for Aug 1991–2020. (b),(c),(d) Area-averaged SST anomalies (°C) for Aug of each year (1982–2022) relative to the 1991–2020 Aug mean for (b) the entire Arctic Ocean north of 65°N, indicated by the dashed white circle in (a), (c) the Chukchi Sea, and (d) the northern and southern Barents Sea indicated by smaller blue boxes (intersecting with land boundaries) in (a). The dotted lines show the linear SST anomaly trends over the period shown and trends in °C yr−1 (with 95% confidence intervals) are indicated on the plots. See Fig. 5.8 caption for sea ice dataset information.
Fig. 5.11.
(a) Monthly sea-ice extent anomalies (%, solid lines) and linear trend lines (dashed lines) for Mar (black) and Sep (red) from 1979 to 2022. The anomalies are relative to the 1991–2020 average for each month. (b) Mar 2022 and (c) Sep 2022 monthly average sea-ice extent; the median extent for 1991–2020 is shown by the magenta contour.
Fig. 5.12.
Sea-ice age coverage map for the week before minimum total extent (when age values are incremented to one year older) in (a) 1985 and (b) 2022; (c) extent of multiyear ice (black) and ice >4 years old (red) within the Arctic Ocean (inset) for the week of the minimum total extent.
Fig. 5.13.
(a) Oct–Apr monthly average sea-ice thickness, calculated over an inner-Arctic Ocean domain (inset of Fig. 5.12c), from ICESat-2 (circles) and CryoSat-2/SMOS (triangles) for 2018/19 (blue), 2019/20 (green), 2020/21 (lilac), and 2021/22 (black); (b) average Apr 2022 sea-ice thickness map from CryoSat-2/SMOS; (c) CryoSat-2/SMOS thickness anomaly map (relative to the 2010–21 average).
Fig. 5.14.
Annual sea-ice volume loss (orange) and gain (blue) between annual maximum and minimum from the CryoSat-2/SMOS Sea Ice Thickness Version 205 product (https://earth.esa.int/eogateway/catalog/smos-cryosat-l4-sea-ice-thickness, accessed 5 Mar 2023). Volume is not estimated during summer, May–Sep. The volume gain represents the change in volume from the first autumn observation in Oct to the annual maximum observed volume, Apr of the following year. The volume loss is the difference between the maximum and Oct values.
Fig. 5.15.
Total mass change (Gt) of the GrIS from 2002 through mid-Nov 2022 determined from GRACE (2002–17) and GRACE-FO (2018–present; Tapley et al. 2019). Monthly estimates are shown as black circles, and 2-sigma uncertainties are provided with (light green) and without (dark green) errors due to leakage of external signals to the trend (i.e., mass changes near Greenland but not associated with the GrIS).
Fig. 5.16.
(a) Net ablation for 2022 (m, top number) measured by PROMICE weather transects and referenced to the 1991–2020 period (%, bottom number). Circles are scaled in size to net ablation and scaled in color to the anomaly. White circles indicate anomaly values within methodological and measurement uncertainty. Stations are: Thule (THU), Upernavik (UPE), Kangerlussuaq (KAN), Nuuk (NUK), Qassimuit (QAS), Tasiliiq (TAS), Scoresby Sund (SCO), and Kronprins Christians Land (KPC). The regions North (NO), Northeast (NE), Northwest (NW), Central East (CE), Central West (CW), Southeast (SE), and Southwest (SW) are referenced in Fig. 5.18. (b) Number of melt days expressed as an anomaly with respect to the 1991–2020 reference period, from daily SSMIS 37 GHz, horizontally polarized passive microwave radiometer satellite data (Mote 2007). (c) Surface melt extent as a percentage of the ice sheet area during 2022 (solid orange) derived from SSMIS.
Fig. 5.17.
(a) Albedo anomaly for Jun–Aug 2022 measured from Sentinel-3 data, relative to a 2017–2021 reference period (Wehrlé et al. 2021). (b) Time series for average Greenland Ice Sheet Jun–Aug albedo from MODIS. (c) Bare ice area (km2) measured from Sentinel-3 observations, with 2022 in black (Wehrlé et al. 2021).
Fig. 5.18.
Solid ice discharge (Gt yr−1) based on ice velocity and thickness (Mankoff et al. 2020) by region of the Greenland Ice Sheet, as shown in Fig. 5.16a. Gray bars show uncertainty of ±10%.
Fig. 5.19.
Monthly snow-cover extent (SCE) anomalies for Arctic terrestrial land areas (>60°N) for (a) May and (b) Jun from 1967 to 2022. Anomalies are relative to the 1991–2020 average and standardized (each observation differenced from the mean and divided by the standard deviation, and thus unitless). Solid black and red lines depict 5-yr running means for North America and Eurasia, respectively. Filled circles highlight 2022 anomalies. (Source: Robinson et al. 2012).
Fig. 5.20.
Snow-cover duration (SCD) anomalies (% difference relative to climatological number of snow-free days for the 1998/99–2017/18 baseline) for the 2021/22 snow year: (a) snow onset period (Aug 2021–Jan 2022); and (b) snow melt period (Feb–Jul 2022). Purple (orange) indicates fewer (more) days than average. Snow water equivalent (SWE) anomalies (% difference from the 1991–2020 baseline) in 2022 for (c) Apr and (d) May. Purple (orange) indicates less (more) snow than average. Latitude 60°N marked by black dashed circle; land north of this defines the Arctic terrestrial area considered in this study. (Source: (a),(b) U.S. National Ice Center [2008]; (c),(d) four SWE products from Snow CCI [Luojus et al. 2022], MERRA2 [GMAO 2015], ERA5-Land [Muñoz Sabater 2019], and Crocus [Brun et al. 2013].)
Fig. 5.21.
Mean Apr snow mass anomalies for Arctic terrestrial areas calculated for North American (black) and Eurasian (red) sectors of the Arctic over 1981–2022. Anomalies are relative to the average for 1991–2020 and standardized (each observation differenced from the mean and divided by the standard deviation, and thus unitless). Filled circles highlight 2022 anomalies. Solid black and red lines depict 5-yr running means for North America and Eurasia, respectively, and the spread among the running means for individual datasets is shown in shading. (Source: four SWE products from Snow CCI [Luojus et al. 2022], MERRA2 [GMAO 2015], ERA5-Land [Muñoz Sabater 2019], and Crocus [Brun et al. 2013].)
Fig. 5.22.
Watersheds of the eight largest Arctic rivers featured in this analysis. Collectively, these rivers drain approximately 70% of the 16.8 million km2 pan-Arctic watershed (indicated by the red boundary line). The red dots show the location of the discharge monitoring stations.
Fig. 5.23.
Monthly discharge (km3) in (a) Eurasian and (b) North American rivers for 2021 (blue squares) and 2022 (red triangles) compared to monthly discharge throughout the 1991–2020 reference period (gray circles). The black bars indicate average monthly discharge during the reference period. Note the different magnitudes of discharge between the Eurasian and North American rivers (see y-axes).
Fig. 5.24.
Long-term trends in annual discharge (km3) for (a) Eurasian and (b) North American Arctic rivers. The North American time series gap from 1996 to 2001 is due to insufficient data availability during those years. Reported slopes (p <0.001 for both) are for 1976–2022.
Fig. 5.25.
Locations of the permafrost temperature monitoring sites (for which data are shown in Fig. 5.26), superimposed on average surface air temperature trends (°C decade−1) during 1981–2020 from ERA5 reanalysis (Hersbach et al. 2020; data available at https://cds.climate.copernicus.eu). See Table 5.2 for site names. Information about these sites is available at http://gtnpdatabase.org/, http://permafrost.gi.alaska.edu/sites_map, and https://www2.gwu.edu/∼calm/.
Fig. 5.26.
Time series of mean annual ground temperature (°C) at depths of 9 m–26 m below the surface at selected measurement sites that fall roughly into Adaptation Actions for a Changing Arctic Project priority regions (see Romanovsky et al. 2017): (a) cold continuous permafrost of northwestern North America and northeastern East Siberia (Beaufort-Chukchi region); (b) discontinuous permafrost in Alaska and northwestern Canada; (c) cold continuous permafrost of eastern and High Arctic Canada (Baffin Davis Strait); and (d) continuous to discontinuous permafrost in Scandinavia, Svalbard, and Russia/Siberia (Barents region). Temperatures are measured at or near the depth of zero annual amplitude where the seasonal variations of ground temperature are less than 0.1°C. Note differences in y-axis value ranges. Borehole locations are shown in Fig. 5.25 (data are updated from Smith et al. 2022b).
Fig. 5.27.
Average annual active layer thickness (ALT) anomalies (m) relative to the 2009–18 mean for six Arctic regions as observed by the Circumpolar Active Layer Monitoring program. Positive and negative anomaly values indicate thicker or thinner ALT, respectively, than the 10-yr reference mean. Only sites with >20 years of continuous thaw depth observations are included. The number of sites and reference period mean ALT are provided on each figure panel. Asterisks indicate a lower number of observations due to pandemic-related restrictions, with the number of sites reporting provided on graph. Canadian ALT is derived from thaw tubes that record the maximum thaw depth over the previous year. Since Canadian sites were not visited in 2020 and 2021, the maximum thaw depth recorded during the 2022 visit could have occurred any summer from 2019 through 2021, although the data point is plotted in 2021. Site-specific data and metadata are available at www2.gwu.edu/∼calm/.
Fig. 5.28.
Magnitude of Maximum Normalized Difference Vegetation Index (MaxNDVI) increases (“greening”) and decreases (“browning”) calculated as the change decade−1 via ordinary least squares regression for Arctic tundra (solid colors) and boreal forest north of 60° latitude (muted colors) during (a) 1982–2021 based on the AVHRR GIMMS 3-g+ dataset, and (b) 2000–22 based on the MODIS MCD13A1 dataset. The circumpolar treeline is indicated by a black line, and the 2022 minimum sea-ice extent is indicated by light shading in each panel.
Fig. 5.29.
Time series of Maximum Normalized Difference Vegetation Index (MaxNDVI) from the MODIS MCD13A1 (2000–22) dataset for the Eurasian Arctic (dark red), North American Arctic (blue), and the circumpolar Arctic (black), and from the long-term AVHRR GIMMS-3g+ dataset (1982–2021) for the circumpolar Arctic (gray).
Fig. 5.30.
Circumpolar Maximum Normalized Difference Vegetation Index (MaxNDVI) anomalies for the 2022 growing season relative to mean values (2000–22) for Arctic tundra (bright colors) and boreal forest north of 60° latitude (muted colors) from the MODIS MCD13A1 dataset. The circumpolar tree line is indicated by a black line, and the 2022 minimum sea-ice extent is indicated by light shading.
Fig. 5.31.
Average (a) chlorine monoxide (ClO) and (b) ozone concentrations (expressed as mixing ratio in ppbv and ppmv, respectively) measured by MLS at an altitude of ∼16 km for the area bounded by the Arctic stratospheric polar vortex. Data from 2010/11 (green), 2019/20 (blue), and 2021/22 (black) are compared with the average (solid white) and minimum/maximum range (gray shading) from 2004/05 to 2020/21, excluding the highlighted years. There is a gap in spring 2011 data due to an MLS instrument anomaly.
Fig. 5.32.
Minimum of the daily average total ozone column (Dobson units, DU) for Mar poleward of 63°N equivalent latitude (Butchart and Remsberg 1986). Open circles represent years in which the polar vortex was not well-defined in Mar, resulting in relatively high values owing to mixing with lower-latitude air masses and a lack of significant chemical ozone depletion. Red and blue lines indicate the average total ozone column for 1979–2021 and 2005–21, respectively. Ozone data for 1979–2019 are based on the combined NIWA-BS total column ozone database version 3.5.1 (Bodeker and Kremser 2021). Ozone data for 2020–22 are from OMI. Adapted from (Müller et al. 2008) and WMO (2022), and updated using ERA5 reanalysis data (Hersbach et al. 2020) to determine equivalent latitude.
Fig. 5.33.
Monthly mean anomaly maps of (a),(b) total ozone column (TOC; %) and (c),(d) noontime UV Index (UVI; %) for Mar and Apr 2022 relative to 2005–21 means. Stippling indicates pixels where anomalies exceed 2 st. dev. Gray-shaded areas centered at the North Pole indicate latitudes where no OMI data are available because of polar darkness. Locations of ground stations are indicated by blue crosses in every map, with labels added to the first map. Maps are based on the OMTO3 Level 3 total ozone product (Bhartia and Wellemeyer 2002). Site acronyms are provided in Table 5.3.
Fig. SB5.4
Alaska seabird die-offs, 1970 to present. Since 2015, mass die-offs have annually occurred in the northern Bering and southern Chukchi sea region. Species primarily affected include murres, puffins, auklets, shearwaters, fulmars, and kittiwakes.
