a) Zonal Total Cloud Cover trends in the 1984–2018 period
Hovmöller plots of the time evolution of the zonal, annual mean Total Cloud Cover (TCC) similar to the ones from Bender et al. 2011 but for the global ocean domain, are examined first. Figure 1 shows the zonal, annual mean Total Cloud Cover Hovmöller diagrams from ISCCP-H data for the period 1983–2018, for the Atlantic and the Pacific Ocean basins. The lines on the plot represent the linear fit to the 80% TCC isoline, chosen to approximate the borders of the two high-TCC midlatitude zones, and the linear fit to the 65% TCC isoline, chosen to approximate the borders of the two low-TCC subtropical zones. Solid lines indicate statistically significant linear trends. The main feature on the plot is the expansion, primarily poleward and to a lesser degree equatorward, of the low-TCC subtropical zones in both hemispheres, which results in a pronounced increase of the extent of those zones in both ocean basins over the 35-year period. On their poleward side the subtropical zone expansion coincides with a poleward shift in all basins of the high-TCC midlatitude zone, consistent with the results of Bender et al. (2011) for the shorter 1982–2008 period. In the Southern midlatitude zone, the poleward high-TCC shift occurs on both sides but is somewhat stronger on the equatorward side, resulting in only a small midlatitude zone narrowing, while in the Northern midlatitudes the poleward shift occurs only on the equatorward side resulting in a more significant narrowing of the zones. On the equatorward side of the two low-TCC subtropical zones, the simultaneous expansion of the Northern and Southern zones results in a significant contraction of the Inter Tropical Convergence Zone (ITCZ), which is more pronounced in the Atlantic Ocean basin. Note that the near merging of the 65% isolines of the Atlantic ITCZ in the later years does not signify a disappearance of the climate zone but rather a simultaneous reduction of the zone’s extent and its TCC (by about 10%). In the Pacific Ocean the ITCZ contraction is smaller and the TCC decrease is around 5%. This ITCZ contraction mirrors the findings of Wodzicki and Rapp (2016) for the Pacific Ocean based on precipitation trends from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Climatology Project (GPCP) datasets.
Figure 2 uses the same line definitions and color bar as Fig. 1 to show the zonal, annual mean TCC Hovmöller diagram for the CLARA-A3 dataset. The expansion of the subtropical low-TCC zone is similarly pronounced in the CLARA-A3 dataset, as is the resulting contraction of the ITCZ zone, but both show somewhat smaller magnitudes than in the ISCCP plot. The midlatitude high-TCC zones also shift poleward in both hemispheres and oceans in a similar manner to ISCCP, but the poleward part of the Southern midlatitude zone shifts equatorward in both oceans significantly contracting the zones, something not seen in ISCCP. Note that all three satellite product descriptions specifically indicate reduced accuracy in cloud detection in the polar regions (Heidinger et al. 2014, Karlsson et al. 2017, Rossow et al. 2022) and therefore the TCC trends are less reliable poleward of about 70 degrees in both Hemispheres. A Hovmöller plot of annual, zonal mean TCC for the PATMOS-x dataset (Fig. S1) shows features that are very similar to those found in the CLARA-A3 dataset.
A quantitative depiction of the cloud zone shifts in the ISCCP and CLARA-A3 datasets shown in Figs. 1 and 2 is presented in Table 1, which shows the slopes of the linear fits to the 80% and 65% TCC isolines in the two basins and datasets. Note that positive values indicate northward shifts while negative values indicate southward shifts, and bold numbers denote statistically significant shifts. The values show that the two datasets agree with the two-sided expansion of the subtropical zones, but ISCCP gives values that range from 1.59–3.74 degrees per decade for the poleward expansion and 0.45–1.76 degrees per decade for the equatorward one, while CLARA-A3 gives values of 0.64–1.75 degrees per decade for the poleward and 0.27–0.80 degrees per decade for the equatorward expansion. They also agree with similar values (~ 1-2.5 degrees per decade) for the poleward shift of the equatorward side of the midlatitude zones but disagree in the sign of the shift of the poleward sides of the midlatitude zones.
The values in Table 1 can be used to provide another perspective on the tropical expansion that has been observed in several observational studies over the past few decades (e.g. Seidel et al. 2008, Staten et al. 2020). Those studies used primarily dynamical and hydrologic indices to determine the poleward edge of the Hadley cells and defined the width of the tropics as the zone between the Northern and Southern Hemisphere Hadley cell edges. The present study uses the 65% TCC isoline to define the edges of the low-TCC subtropical zone and therefore can provide a measure of both the width of the global tropics, defined as the region between the poleward edges of the two low-TCC zones, but also the width of the ITCZ, defined as the region between the equatorward edges of the two low-TCC zones. For the width of the tropics, the values in Table 1 show a tropical expansion for ISCCP of 3.72 deg/decade in the Pacific and 5.78 deg/decade in the Atlantic, and for CLARA a tropical expansion of 1.35 deg/decade in the Pacific and 2.74 deg/decade in the Atlantic. These values fall on the high end or, in the case of ISCCP, higher than the 0.25-3 deg/decade range found in the dynamical studies (Staten et al. 2020). Note however that the present study examines only the two ocean basins rather than the global domain. For the contraction of the ITCZ, ISCCP shows values of 1.71 deg/decade in the Pacific and 2.21 deg/decade in the Atlantic while CLARA shows values of 0.67 deg/decade in the Pacific and 1 deg/decade in the Atlantic. For the Pacific ITCZ, the analysis of Wodzicki and Rapp (2016) finds a contraction of about 0.6 deg/decade for the 1980–2014 period.
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)
*Slopes of linear fits to the 80% (red) and 60% (blue) TCC isolines for ISCCP and CLARA-A3 and the Pacific and Atlantic Oceans, as depicted in Figs. 1 and 2. Positive/negative slopes indicate Northward/Southward direction, and bold numbers indicate linear fits significant at the 95% level.
The expansion of the subtropical zones alters the zonal cloud distribution by expanding the domain of the low-TCC regimes. However, Figs. 1 and 2 show that cloud cover trends also vary within each climate zone, so the latitude-resolved trends are examined next. Figure 3 shows the zonal, annual mean trend in TCC for the combined Atlantic and Pacific oceans over the 35-yr period (1984–2018) for ISCCP and CLARA-A3. In ISCCP, two regions of contrasting cloud cover trends emerge from the analysis. A low latitude region, between 50S and 50N, where TCC shows significant decreasing trends, and a high latitude region, poleward of 50N/S where TCC either increases in the southern region or shows near-zero trends in the northern one. In the low latitude region, the poleward expansion of the subtropical zone creates the TCC decreases in the 25o-40oN/S zone, a feature that is also found in the analysis of an empirically corrected version of the ISCCP-D dataset (Norris et al. 2016) for a shorter time period (1983–2009). A relatively weaker TCC decreasing trend coincides with the ITCZ, but stronger decreases are found to the north and south caused by both the equatorward expansion of the subtropical zones and by zonal TCC decreases near 5oS and 20oN. In the southern high latitude region TCC increasing trends are found, caused in part by the poleward expansion of the midlatitude zone, while in the northern high latitude region the lack of such expansion makes TCC trends negligible. Note however that large TCC satellite retrieval uncertainties exist in the polar regions and that there is low ocean coverage poleward of 70 degrees. Differences in Table 1 between ISCCP and CLARA in the magnitude and sometimes the sign of some TCC isoline trends also result in somewhat different latitudinal variations in zonal cloud cover trend between the two datasets. In the low latitude zone, both datasets show decreasing TCC trends with ISCCP showing trend values on average almost double those of CLARA-A3. In the Northern high latitude zone both datasets show near-zero TCC trends, but in the Southern high latitude zone their trends have opposite signs, upward for ISCCP, and downward for CLARA-A3.
b) Zonal Total Cloud Cover trends in the 2000–2018 period
For the period 2000–2018 when the ISCCP-H dataset is derived from a near steady number of satellites, a cloud property dataset from the MODIS instrument retrievals (Platnick et al. 2021) is also available. Therefore, for that period all of the MODIS, ISCCP-H, and CLARA-A3 datasets can be examined in conjunction with regard to their zonal TCC trends. Hovmöller diagrams of the time evolution of the zonal, annual mean TCC were derived first, and the plots (not shown here) revealed the same subtropical zone expansion and ITCZ narrowing in all three datasets, but with less significant linear trends due to the shorter length of the time series. The slopes of the different isolines for ISCCP, CLARA-A3, and MODIS are shown in Table S1. The sign of the trends remains the same as in the longer 35-year period, with the subtropical low-TCC zones expanding on both sides, the ITCZ contracting, and the midlatitude zones shifting poleward, but as expected the significance and magnitude of the trends is much lower compared to the longer period. The ISCCP analysis shows a tropical expansion of 2.07 deg/decade in the Pacific and 2.77 deg/decade in the Atlantic, CLARA-A3 shows an expansion of 1.03 deg/decade in the Pacific and 1 deg/decade in the Atlantic, while MODIS shows an expansion of 1.07 deg/decade in the Pacific and only 0.17 deg/decade in the Atlantic. For the ITCZ contraction, ISCCP gives values of 0.67 deg/decade for the Pacific and 1.16 deg/decade for the Pacific, CLARA-A3 gives values of 0.95 deg/decade in the Pacific 0.61 deg/decade in the Atlantic, while MODIS gives values of 1.3 deg/decade in the Pacific and 0.17 deg/decade in the Atlantic.
The zonal, annual mean TCC trends for ISCCP, CLARA, and MODIS for the 2000–2018 period are shown in Fig. 4. The MODIS-derived TCC trends are very similar between the Terra and Aqua instruments, so we show here only the trends from MODIS-Terra since they allow for a longer comparison period. The dominant feature is that all three datasets show a consistent pattern between the three zones, with upward or near-zero TCC trends in the high latitude zones and downward TCC trends in the low latitude zone. The ISCCP trend pattern for this 19-year period is similar to the longer 35-year period, with strong TCC decreases in the low latitude zone, increases in the Southern high latitudes and imperceptible changes in the Northern high latitudes. For this shorter period CLARA-A3 shows almost no trend in the Southern high latitudes and upward trends in the Northern high latitudes along with the consistent downward trends in the low latitude zone. The MODIS analysis shows upward TCC trends in both high latitude zones, and downward TCC trends in the low latitude zone that are somewhat stronger than CLARA-A3 in the Southern Hemisphere but significantly weaker than ISCCP at most low latitudes.
In addition to the passive retrievals of cloud properties analyzed so far, active retrievals from radar (CloudSat) and Lidar (CALIPSO) measurements are available for smaller intervals of the 2000–2018 time period and can be added as an independent cloud cover measurement to the cloud cover trend comparisons. Figure 5 shows the zonal, annual mean TCC trends from ISCCP, CLARA-A3, and MODIS together with those derived from the combined CloudSat/CALIPSO 2B-GEOPROF-LIDAR dataset (Mace et al. 2014) for their common time period 2007–2016. The plot shows that ISCCP, MODIS, and 2B-GEOPROF-LIDAR trends agree well in their overall structure, with weak TCC increases in the high latitude zones and strong decreases in the low latitude zone outside the equator, while CLARA-A3 shows similar increases in the high latitude zones but weaker decreases and even some increasing trends in the low latitude zone. The close correspondence between the passive (especially ISCCP and MODIS) and active (radar and lidar) TCC trend zonal patterns for this shorter period strengthens our confidence in the robustness of the trends derived from those instruments for the longer time periods.
Figure 6 provides a summary of the oceanic TCC annual trends for the three climatic zones and the global ocean, derived from the different datasets for their respective periods. For the low latitude zone, agreement is found in all datasets and periods that a significant and consistent (as indicated by the uncertainty bars) downward TCC trend is observed throughout this zone. The trend in ISCCP is nearly double that of CLARA-A3 and MODIS, and for both ISCCP and CLARA-A3 the trend is almost half as strong in the 19-year as in the 35-year record implying a weakening of the trend with time. In the Southern high latitudes, ISCCP shows a consistent upward TCC trend in both periods and MODIS shows also an upward trend of somewhat smaller magnitude for the shorter 19-year period. CLARA-A3 on the other hand shows a downward TCC trend in the longer period and a near-zero trend in the shorter period. In the Northern high latitudes, ISCCP shows near-zero trends in all periods while CLARA-A3 changes from a downward to an upward trend in the later period and MODIS shows a consistent upward trend in that period. The global ocean TCC trend is dominated by the more extensive low latitude zone and is negative for all datasets and periods. This global downward trend for the 35-year period is 0.72% per decade for ISCCP and 0.45% per decade for CLARA-A3, while for the more recent 19-year period it is weaker and ranges from 0.17% per decade for MODIS and CLARA-A3 to 0.39% per decade for ISCCP. For the negative global ocean trend, despite the mean value differences all three datasets show significant ranges of overlap.
c) Zonal cloud radiative effect trends in the satellite era
Two radiative flux datasets are used here to examine the trends in Shortwave Cloud Radiative Effect (SWCRE) during the satellite era. One is the ISCCP-FH dataset, which is a radiative flux dataset derived using as input the cloud and atmospheric property retrievals of the ISCCP-H dataset (Zhang and Rossow 2023). It covers the 1983–2017 period, and it provides SWCRE values and trends that are fully consistent with the ISCCP-H underlying cloud properties. The other is the CERES-EBAF dataset, which provides radiative flux measurements from the CERES instrument on Terra and Aqua and covers the period from 2000 to the present (Loeb et al. 2018). The SWCRE values are based on irradiances derived from a radiance to flux conversion facilitated by Angular Distribution Models (ADMs), which depend on scene identification provided by MODIS on the same platforms. The SWCRE trends are examined for three different periods: the 35-year period from 1983–2017 when ISCCP-FH is available, the 18-year period from 2000–2017 when both ISCCP-FH and CERES are available, and the 23-year period from 2000–2022 which is the full period covered by the CERES dataset.
The zonal SWCRE trends for the two datasets and the three periods are shown in Fig. 7. Since SWCRE is a negative quantity indicating cooling, positive SWCRE trends imply decreased cloud radiative cooling. Overall, a picture emerges that is consistent with the TCC trends presented in Figs. 3 and 4. In the high latitude zones all datasets and periods show negative or near-zero SWCRE trends implying increasing cloud radiative cooling or no radiative change, consistent with the upward or near-zero TCC trends shown earlier. In the low latitude zone the different datasets and periods show mostly positive SWCRE trends indicating less cloud radiative cooling consistent with the downward TCC trends documented earlier. For ISCCP-FH, at the high latitude zones and the poleward edges of the low latitude zone significant cloud SW cooling is found, while only in the 30oS-30oN region weak to moderate cloud radiative warming is present. This behavior is consistent in both the short and long time periods for ISCCP-FH. The SWCRE tends of ISCCP-FH and CERES are overall qualitatively similar but with notable quantitative differences. In the high latitude zones, the CERES dataset shows mostly negative but much weaker SWCRE trends, while in the low latitude zone it shows more consistent and larger positive SWCRE trends than ISCCP-FH. When the full period for CERES is plotted with the addition of years 2018–2022, the SWCRE positive trend in the low latitude zone becomes noticeably stronger and more significant implying weaker SW cloud radiative cooling in that later period, which is consistent with the recent increase in the Earth’s Energy Imbalance found by Loeb et al. (2021) and attributed by them to increased SW absorption.
The low latitude SWCRE warming is weak in the ISCCP-FH dataset (Fig. 7) despite the large TCC downward trend in the same region (Fig. 3 and Fig. 4). The reason is that in the ISCCP-H dataset there is an overall upward trend of cloud optical thickness (TAU) in both the longer and shorter periods, as illustrated in Figure S2. The figure also shows similar trends in optical thickness from MODIS for the shorter time period. The positive TAU trends are larger in the high latitude zones, for both ISCCP-H and MODIS, where they account for a larger SWCRE cooling trend in ISCCP-FH, but are also significant in the low latitude zone where they counteract the radiative effect of the strong TCC decreases thus producing only a weak radiative warming in that region. The ISCCP-H cloud optical thickness trend is overestimated by about a third because of the residual visible calibration trend (Rossow and Ferrier 2015), which produces larger changes for larger TAU values than for smaller values consistent with the comparison to MODIS values in Fig. S2. Therefore, the ISCCP-FH SWCRE trend is an overestimate of the high latitude cloud radiative cooling and an underestimate the low latitude cloud radiative warming.
Figure 8 summarizes the SWCRE trends of ISCCP-FH and CERES-EBAF in the three latitude zones, for the ISCCP-FH full 35-year period (1983–2017), for their common 18-year period (2000–2017), and for the CERES-EBAF full 23-year period (2000–2022). In the two high latitude zones, all datasets and periods show SW cloud radiative cooling trends which are larger in ISCCP-FH than in CERES, particularly in the Southern Hemisphere. In the low latitude zone, the CERES dataset shows significant SW cloud radiative warming trends (i.e. weaker cooling) while ISCCP-FH shows very weak radiative cooling trends (i.e. stronger cooling). In the global oceans mean result, the CERES dataset shows a SW cloud radiative warming trend of 0.12 W/m2/decade coming from the dominance of the more extensive low latitude zone, while the ISCCP-FH dataset shows a SW cloud radiative cooling trend of 0.25–0.35 W/m2/decade due to the strong cooling trends of the high latitude zones. It is important to note that the full 22-year period of CERES-EBAF shows a doubling of the global SW cloud radiative warming trend (0.24 w/m2/decade), coming primarily from the doubling of the SWCRE warming in the low latitude zone, a result that further emphasizes the increase over the past few years of the absorbed SW radiation and the Earth’s Energy Imbalance first demonstrated in Loeb et al. (2021).