Gaps in network infrastructure limit our understanding of biogenic methane emissions for the United States Biogeosciences DOI 10.5194/bg-19-2507-2022 12 August 2022 To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover. Read more
Currents generated by the sea breeze in the southern Caspian Sea Ocean Science DOI 10.5194/os-18-675-2022 10 August 2022 The smaller thermal heat capacity of land relative to the sea results in land–sea thermal gradients with a daily cycle, called sea breeze systems, with the same daily periodicity. Since tides in the Caspian, as the largest enclosed basin with a persistent sea breeze system through the year, are very weak we found that most of the higher-frequency variations in coastal currents are a response to the sea breeze system. Read more
Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1 Geoscientific Model Development DOI 10.5194/gmd-15-3831-2022 8 August 2022 In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models. Read more
Lévy noise versus Gaussian-noise-induced transitions in the Ghil–Sellers energy balance model Nonlinear Processes in Geophysics DOI 10.5194/npg-29-183-2022 5 August 2022 In most of the investigations on metastable systems, the stochastic forcing is modulated by Gaussian noise. Lévy noise laws, which describe jump processes, have recently received a lot of attention, but much less is known. We study stochastic versions of the Ghil–Sellers energy balance model, and we highlight the fundamental difference between how transitions are performed between the competing warm and snowball states, depending on whether Gaussian or Lévy noise acts as forcing. Read more
Assessing the consequences of including aerosol absorption in potential stratospheric aerosol injection climate intervention strategies Atmospheric Chemistry and Physics DOI 10.5194/acp-22-6135-2022 3 August 2022 Simulations are presented investigating the influence of moderately absorbing aerosol in the stratosphere to combat the impacts of climate change. A number of detrimental impacts are noted compared to sulfate aerosol, including (i) reduced cooling efficiency, (ii) increased deficits in global precipitation, (iii) delays in the recovery of the stratospheric ozone hole, and (iv) disruption of the stratospheric circulation and the wintertime storm tracks that impact European precipitation. Read more
The onset of the spring phytoplankton bloom in the coastal North Sea supports the Disturbance Recovery Hypothesis Biogeosciences DOI 10.5194/bg-19-2417-2022 1 August 2022 In oceanic waters, the accumulation of phytoplankton biomass in winter, when light still limits growth, is attributed to a decrease in grazing as the mixed layer deepens. However, in coastal areas, it is not clear whether winter biomass can accumulate without this deepening. Using 21 years of weekly data, we found that in the Scottish coastal North Sea, the seasonal increase in light availability triggers the accumulation of phytoplankton biomass in winter, when light limitation is strongest. Read more
Warming of 0.5 °C may cause double the economic loss and increase the population affected by floods in China Natural Hazards and Earth System Sciences DOI 10.5194/nhess-22-1577-2022 29 July 2022 The impact of extreme events is increasing with global warming. Based on future scenario data and an improved quantitative assessment model of natural-disaster risk, this study analyses the spatial and temporal patterns of floods in China at 1.5 °C and 2 °C of global warming, quantitatively assesses the socioeconomic risks posed by floods, and determines the integrated risk levels. Global warming of 1.5 °C can effectively reduce the population affected and the economic risks of floods. Read more
Projections of hydrofluorocarbon (HFC) emissions and the resulting global warming based on recent trends in observed abundances and current policies Atmospheric Chemistry and Physics DOI 10.5194/acp-22-6087-2022 27 July 2022 The emissions of hydrofluorocarbons (HFCs) have increased significantly in the past as a result of the phasing out of ozone-depleting substances. Observations indicate that HFCs are used much less in certain refrigeration applications than previously projected. Current policies are projected to reduce emissions and the surface temperature contribution of HFCs from 0.28–0.44 °C to 0.14–0.31 °C in 2100. The Kigali Amendment is projected to reduce the contributions further to 0.04 °C in 2100. Read more
Marine heatwaves in the Arabian Sea Ocean Science DOI 10.5194/os-18-639-2022 25 July 2022 Marine heatwaves refer to discrete, prolonged warm ocean conditions known to cause severe destruction in marine ecosystems. We find that coastal waters off the west coast of India have experienced a rapid multifold increase in heatwave days since the early 80s. This resulted in more frequent and longer marine heatwave events in the last decade. We show that the rapid warming in the Arabian Sea in the last decade is the primary cause of the observed enhanced heatwave events in this basin. Read more
Using neural networks to improve simulations in the gray zone Nonlinear Processes in Geophysics DOI 10.5194/npg-29-171-2022 22 July 2022 Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days. Read more