Machine-learned climate model corrections from a global storm-resolving model

Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via subgrid parameterizations, which contribute significantly to the uncertainty in GCM predictions. One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections at each simulation timestep, such that the climate model evolves more like a high-resolution global storm-resolving model (GSRM). We train neural networks to learn the state-dependent temperature, humidity, and radiative flux corrections needed to nudge a 200 km coarse-grid climate model to the evolution of a 3~km fine-grid GSRM. When these corrective ML models are coupled to a year-long coarse-grid climate simulation, the time-mean spatial pattern errors are reduced by 6-25% for land surface temperature and 9-25% for land surface precipitation with respect to a no-ML baseline simulation. The ML-corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the baseline simulation

Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation

Abstract Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting

Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle

Abstract One approach to improving the accuracy of a coarse‐grid global climate model is to add machine‐learned (ML) state‐dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine‐grid global storm‐resolving model (GSRM). Our past work demonstrating this approach was trained with short (40‐day) simulations of GFDL's X‐SHiELD GSRM with 3 km global horizontal grid spacing. Here, we extend this approach to span the full annual cycle by training and testing our ML using a new year‐long GSRM simulation. Our corrective ML models are trained by learning the state‐dependent tendencies of temperature and humidity and surface radiative fluxes needed to nudge a closely related 200 km grid coarse model, FV3GFS, to the GSRM evolution. Coarse‐grid simulations adding these learned ML corrections run stably for multiple years. Compared to a no‐ML baseline, the time‐mean spatial pattern errors with respect to the fine‐grid target are reduced by 6%–26% for land surface temperature and 9%–25% for land surface precipitation. The ML‐corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the no‐ML baseline simulation

Delayed Mortality and Attenuated Thrombocytopenia Associated with Severe Malaria in Urokinase- and Urokinase Receptor-Deficient Mice

We explored the role of urokinase and tissue-type plasminogen activators (uPA and tPA), as well as the uPA receptor (uPAR; CD87) in mouse severe malaria (SM), using genetically deficient (−/−) mice. The mortality resulting from Plasmodium berghei ANKA infection was delayed in uPA(−/−) and uPAR(−/−) mice but was similar to that of the wild type (+/+) in tPA(−/−) mice. Parasitemia levels were similar in uPA(−/−), uPAR(−/−), and +/+ mice. Production of tumor necrosis factor, as judged from the plasma level and the mRNA levels in brain and lung, was markedly increased by infection in both +/+ and uPAR(−/−) mice. Breakdown of the blood-brain barrier, as evidenced by the leakage of Evans Blue, was similar in +/+ and uPAR(−/−) mice. SM was associated with a profound thrombocytopenia, which was attenuated in uPA(−/−) and uPAR(−/−) mice. Administration of aprotinin, a plasmin antagonist, also delayed mortality and attenuated thrombocytopenia. Platelet trapping in cerebral venules or alveolar capillaries was evident in +/+ mice but absent in uPAR(−/−) mice. In contrast, macrophage sequestration in cerebral venules or alveolar capillaries was evident in both +/+ and uPAR(−/−) mice. Polymorphonuclear leukocyte sequestration in alveolar capillaries was similar in +/+ and uPAR(−/−) mice. These results demonstrate that the uPAR deficiency attenuates the severity of SM, probably by its important role in platelet kinetics and trapping. These results therefore suggest that platelet sequestration contributes to the pathogenesis of SM

NA62 electronics barracks access 2017

Access to the cavern was required during a break in the 2017 data taking. Pictures from 18-07-17, focusing on the electronics barracks and some safety equipment