Emergent Constraints on CMIP6 Climate Warming Projections: Contrasting Cloud- and Surface Temperature-Based Constraints

The latest Sixth Coupled Model Intercomparison Project (CMIP6) multi-model ensemble shows a broader range of projected warming than the previous-generation CMIP5 ensemble. We show that the projected warming is well-correlated with tropical and subtropical low-level cloud properties. These physically-meaningful relations enable us to use observed cloud properties to constrain future climate warming. We develop multivariate-linear-regression models with metrics selected from a set of potential constraints based on a step-wise selection approach. The resulting linear regression model using two low-cloud metrics shows better cross-validated results than regression models which use single metrics as constraints. Application of a regression model using the low-cloud metrics to climate projections results in similar estimates of the mean, but substantially-narrower ranges, of projected 21st century warming when compared with unconstrained simulations. The resulting projected global-mean warming in 2081-2100 relative to 1995-2014 is 2.84-5.12 K (5-95% range) for Shared Socioeconomic Pathway (SSP) 5-8.5, compared with a range of 2.34-5.81 K for unconstrained projections, and 0.60-1.70 K for SSP1-2.6, compared to an unconstrained range of 0.38-2.04 K. We provide evidence for a higher lower-bound of the projected warming range than that obtained from constrained projections based on the past global-mean temperature trend. Consideration of the impact of the sea surface temperature pattern effect on the recent observed warming trend, which is not well-captured in the CMIP6 ensemble, indicates that the relatively-low projected warming resulting from the global-mean temperature trend constraint may not be reliable and provides further justification for the use of climatologically-based cloud metrics to constrain projections.

Cloud effects on surface radiation balance at Helheim and Jakobshavn Glaciers (Greenland) using ground-based observations

<p>The surface radiation budget is an essential component of the total energy exchange between the atmosphere and the Earth’s surface. Measurements of radiative fluxes near/on ice surfaces are sparse in the polar regions, including on the Greenland Ice Sheet (GrIS), and the effects of cloud on radiative fluxes are still poorly studied. In this work, we assess the impacts of cloud on radiative fluxes using two metrics: the longwave-equivalent cloudiness, derived from long-wave radiation measurements, and the cloud transmittance factor, obtained from short-wave radiation. The metrics are applied to radiation data from two automatic weather stations located over the bare ground near the ice front of Helheim (HG) and Jakobshavn Isbræ (JI) on the GrIS. Comparisons of meteorological parameters, surface radiation fluxes, and cloud metrics show significant differences between the two sites. The cloud transmittance factor is higher at HG than at JI, and the incoming short-wave radiation in the summer at HG is 50.0 W m−2 larger than at JI. Cloud metrics derived at the two sites reveal   a high dependency on the wind direction. The total cloud radiative effect (CREnet) generally increases during melt season at the two stations due to long-wave CRE enhancement by cloud fraction.  CREnet decreases from May to June and increases afterward, due to the strengthened short-wave CRE. The annually averaged CREnet were 3.0 ± 7.4 W m-2 and 1.9 ± 15.1 W m−2 at JI and HG.  CREnet estimated from AWS indicates that clouds cool the JI and HG during melt season at different rates.</p>

Meteorological Conditions and Cloud Effects on Surface Radiation Balance Near Helheim Glacier and Jakobshavn Isbræ (Greenland) Using Ground-Based Observations

The surface radiation budget is an essential component of the total energy exchange between the atmosphere and the Earth’s surface. Measurements of radiative fluxes near/on ice surfaces are sparse in the polar regions, including on the Greenland Ice Sheet (GrIS), and the effects of cloud on radiative fluxes are still poorly studied. In this work, we assess the impacts of cloud on radiative fluxes using two metrics: the longwave-equivalent cloudiness, derived from long-wave radiation measurements, and the cloud transmittance factor, obtained from short-wave radiation data. The metrics are applied to radiation data from two automatic weather stations located over the bare ground near the ice front of Helheim (HG, 66.3290°N, 38.1460°W) and Jakobshavn Isbræ(JI, 69.2220°N, 49.8150°W) on the GrIS. Comparisons of meteorological parameters, surface radiation fluxes, and cloud metrics show significant differences between the two sites. The cloud transmittance factor is higher at HG than at JI, and the incoming short-wave radiation in the summer at HG is about 50.0 W m−2 larger than at JI. Cloud metrics derived at the two sites reveal partly cloudy conditions were frequent (42 and 65% of the period at HG and JI) with a high dependency on the wind direction. The total cloud radiative effect (CREnet) generally increases during melt season at the two stations due to long-wave CRE enhancement by cloud fraction. CREnet decreases from May to June and increases afterward, due to the strengthened short-wave CRE. The annually averaged CREnet were 3.0 ± 7.4 W m−2 and 1.9±15.1 W m−2 at JI and HG. CREnet estimated from AWS indicates that clouds cool the JI and HG during melt season at different rates.

UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.