This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO₂ is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO₂ radiative forcing and stronger positive feedbacks.

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world's major source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world's main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global aerosol model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth (DAOD). We obtain a dataset that constrains the relative contribution of nine major source regions to size-resolved dust emission, atmospheric loading, DAOD, concentration, and deposition flux. We find that the 22–29 Tg (1 standard error range) global loading of dust with a geometric diameter up to 20 µm is partitioned as follows: North African source regions contribute ∼ 50 % (11–15 Tg), Asian source regions contribute ∼ 40 % (8–13 Tg), and North American and Southern Hemisphere regions contribute ∼ 10 % (1.8–3.2 Tg). These results suggest that current models on average overestimate the contribution of North African sources to atmospheric dust loading at ∼ 65 %, while underestimating the contribution of Asian dust at ∼ 30 %. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ∼ 10 Tg yr⁻¹, which is a factor of 2–3 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.

The 3D analytic signal amplitude of a total magnetic intensity (TMI) map, introduced by Roest et al. (1992), is widely used in magnetic interpretation as a means of positioning anomalies directly over their sources. This technique is most important at low magnetic latitudes, where reduction to the pole distorts anomalies to the point where they often become uninterpretable: the reduction operator does not converge if the magnetization and regional field are truly horizontal (Baranov, 1957). Methods have been devised to suppress the artifacts appearing in low-latitude reduction to the pole, but no method can reduce such data without distortion (e.g., Silva, 1986; Hansen and Pawlowski, 1989), which becomes severe for inclinations less than 20°. The amplitude of the analytic signal, denoted by |A|, has the added advantage of being independent of the orientation of magnetization of the source bodies. It reaches a maximum over magnetic contacts, and thus, in theory, can be used to trace the outline of magnetic bodies. In practice, especially in the case of aeromagnetic data at high instrument-source separation, |A| is high over magnetic bodies, but is not sufficient to resolve body edges. This appears to be true even with higher-order analytic signal derivatives (Debeglia and Corpel, 1997, their Figure 11).

A long Saharan dust event affected the western Mediterranean in the period 12–28 June 2002. Dust was present mainly between 1‐ and 5‐km height affecting most parts of the Iberian Peninsula and reaching western/central Europe. Intensive backscatter lidar observations over Barcelona (Spain) and Sun photometer data from two stations (El Arenosillo, Spain, and Avignon, France) are used to evaluate different configurations the Dust Regional Atmospheric Modeling (DREAM) system. DREAM currently operates dust forecasts over the Mediterranean region (http://www.bsc.es/projects/earthscience/DREAM/) considering four particle size bins while only the first two are relevant for long‐range transport analysis since their life time is larger than 12 hours. A more detailed bin method is implemented, and two different dust distributions at sources are compared to the operational version. Evaluations are performed at two wavelengths (532 and 1064 nm). The dust horizontal and vertical structure simulated by DREAM shows very good qualitative agreement when compared to SeaWIFS satellite images and lidar height‐time displays over Barcelona. When evaluating the modeled aerosol optical depth (AOD) against Sun photometer data, significant improvements are achieved with the use of the new detailed bin method. In general, the model underpredicts the AOD for increasing Ångström exponents because of the influence of anthropogenic pollution in the boundary layer. In fact, the modeled AOD is highly anticorrelated with the observed Ångström exponents. Avignon shows higher influence of small anthropogenic aerosols which explains the better results of the model at the wavelength of 1064 nm over this location. The uncertainties of backscatter lidar inversions (20–30%) are in the same order of magnitude as the differences between the model experiments. Better model results are obtained when comparing to lidar because most of the anthropogenic effect is removed.