Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from ∼1∘ (eddy-parametrized) to ∼0.25∘ (eddy-present). For one model, ocean resolution also reaches 1/12∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary.

Cloud climatology of the Indian Ocean based on ship observations

Surface cloud data based on synoptic observations made by Voluntary Observing Ships (VOS) during the period 1951-98 were used to prepare the seasonal and annual cloud climatology of the Indian Ocean. The analysis has been carried out by separating the long-term trends, decadal and inter-annual components from the monthly cloud anomaly time series at each 5° × 5° grids. Maximum zone of total and low cloud cover shifts from equator to northern parts of India during the monsoon season. During the monsoon season (June-September), maximum total cloud cover exceeding 70% and low cloud cover exceeding 50% are observed over north Bay of Bengal. Maximum standard deviation of total and low cloud cover is observed near the equator and in the southern hemisphere. Both total and low cloud cover over Arabian Sea and the equatorial Indian Ocean are observed to decrease during the ENSO events. However, cloud cover over Bay of Bengal is not modulated by the ENSO events. On inter-decadal scale, low cloud cover shifted from a "low regime" to a "high regime" after 1980 which may be associated with the corresponding inter-decadal changes of sea surface temperatures over north Indian Ocean observed during the late 1970s.

Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris

We have digitized three meteorological variables (temperature, direction of the movement of the clouds, and cloud cover) from copies of Louis Morin’s original measurements (Source: Institute of History / Oeschger Centre for Climate Change Research, University of Bern) and subjected them to quality analysis to make these data available to the scientific community. Our available data cover the period 1665–1709 (temperature beginning in 1676). We compare the early instrumental temperature dataset with statistical methods and proxy data to validate the measurements in terms of inhomogeneities and claim that they are, apart from small inhomogeneities, reliable. The Late Maunder Minimum (LMM) is characterized by cold winters and autumns, and moderate springs and summers, with respect to the reference period of 1961–1990. Winter months show a significant lower frequency of westerly direction of movement of the clouds. This reduction of advection from the ocean leads to a cooling in Paris in winter. The influence of the advection becomes apparent when comparing the last decade of the 17th century (cold) and the first decade of the 18th century (warm). A lower frequency of westerly direction of movement of the clouds can also be seen in summer, but the influence is stronger in winter than in summer. Consequently, the unusually cold winters in the LMM can be attributed to a lower frequency of westerly direction of movement of the clouds. An impact analysis reveals that the winter of 1708/09 was a devastating one with respect of consecutive ice days, although other winters are more pronounced (e.g., the winters of 1676/77, 1678/79, 1683/84, 1692/93, 1694/95 and 1696/97) in terms of mean temperature, ice 15 days, cold days or consecutive cold days. An investigation of the cloud cover data revealed a high discrepancy in the seasons, where the winter season (DJF) (−13.2 %) and the spring season (MAM) (−12.6 %) show a negative anomaly of the total cloud cover (TCC), whereas summer (JJA) (−0.5 %) shows a moderate anomaly of TCC with respect to the 30 year mean of the Meteobluedata (1985–2014).

Environmental drivers of autumn migration departure decisions in midcontinental mallards

Background The timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale. Methods We combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion. Results Departure was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%). Conclusions Our results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.

Activity of southern Indiar1 Ocean convergence zone as seen in satellite cloud data during pre-monsoon months

The weekly mean cloud cover data for the pre-monsoon months of April and May over the Indian Ocean between20°S to 20°N latitudes and 40°E to 100" E longitudes have been studied for three good moon- soon years (1977, 1983, 1988) and three drought years (1972,1979, 1987). It is shown that while the characteristics of weekly mean cloud cover data during pre-monsoon months are similar for all the good monsoon years, they varied from one drought year to another. The study reveals some of the interesting features of southwest monsoon. An overall negative relationship between southern Indian Ocean convergence zone (SIOCZ) and monsoon activity is indicated. While at intraseasonal scale this may only be a simultaneous association, the pre-monsoon activity of SIOCZ may possibly have long-range predictive potential to some extent, for Indian monsoon rainfall.

The Effect of Oceanic South Atlantic Convergence Zone Episodes on Regional SST Anomalies: the Roles of Heat Fluxes and Upper-Ocean Dynamics

The South Atlantic Convergence Zone (SACZ) is an atmospheric system occurring in austral summer on the South America continent and sometimes extending over the adjacent South Atlantic. It is characterized by a persistent and very large, northwest-southeast-oriented, cloud band. Its presence over the ocean causes sea surface cooling that some past studies indicated as being produced by a decrease of incoming solar heat flux induced by the extensive cloud cover. Here we investigate ocean-atmosphere interaction processes in the Southwestern Atlantic Ocean (SWA) during SACZ oceanic episodes, as well as the resulting modulations occurring in the oceanic mixed layer and their possible feedbacks on the marine atmospheric boundary layer. Our main interests and novel results are on verifying how the oceanic SACZ acts on dynamic and thermodynamic mechanisms and contributes to the sea surface thermal balance in that region. In our oceanic SACZ episodes simulations we confirm an ocean surface cooling. Model results indicate that surface atmospheric circulation and the presence of an extensive cloud cover band over the SWA promote sea surface cooling via a combined effect of dynamic and thermodynamic mechanisms, which are of the same order of magnitude. The sea surface temperature (SST) decreases in regions underneath oceanic SACZ positions, near Southeast Brazilian coast, in the South Brazil Bight (SBB) and offshore. This cooling is the result of a complex combination of factors caused by the decrease of solar shortwave radiation reaching the sea surface and the reduction of horizontal heat advection in the Brazil Current (BC) region. The weakened southward BC and adjacent offshore region heat advection seems to be associated with the surface atmospheric circulation caused by oceanic SACZ episodes, which rotate the surface wind and strengthen cyclonic oceanic mesoscale eddy. Another singular feature found in this study is the presence of an atmospheric cyclonic vortex Southwest of the SACZ (CVSS), both at the surface and aloft at 850 hPa near 24°S and 45°W. The CVSS induces an SST decrease southwestward from the SACZ position by inducing divergent Ekman transport and consequent offshore upwelling. This shows that the dynamical effects of atmospheric surface circulation associated with the oceanic SACZ are not restricted only to the region underneath the cloud band, but that they extend southwestward where the CVSS presence supports the oceanic SACZ convective activity and concomitantly modifies the ocean dynamics. Therefore, the changes produced in the oceanic dynamics by these SACZ events may be important to many areas of scientific and applied climate research. For example, episodes of oceanic SACZ may influence the pathways of pollutants as well as fish larvae dispersion in the region.

The Surface Longwave Cloud Radiative Effect derived from Space Lidar Observations

Clouds warm the surface in the longwave (LW) and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE is estimated using long-term observations from space-based radiometers (2000–2021) but has some bias over continents and icy surfaces. It is also estimated globally using the combination of radar, lidar and space-based radiometer over the 5–year period ending in 2011. To develop a more reliable long time series of surface LW CRE over continental and icy surfaces, we propose new estimates of the global surface LW CRE from space-based lidar observations. We show from 1D atmospheric column radiative transfer calculations, that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple relationships between surface LW CRE, and five cloud properties that are well observed by the CALIPSO space-based lidar: opaque cloud cover and altitude, and thin cloud cover, altitude, and emissivity. We use these relationships to retrieve the surface LW CRE at global scale over the 2008–2020 time period (27 Wm−2). We evaluate this new surface LW CRE product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages, as well as to ground-based observations at specific locations. Our estimate appears to be an improvement over others as it appropriately capture the surface LW CRE annual variability over bright polar surfaces and it provides a dataset of more than 13 years long.