Efficient dynamical downscaling of general circulation models using continuous data assimilation

Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one-month period in January, 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large and small scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral nudging and CDA describe better the small-scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small-scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large- and small-scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields

Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres

Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The large range of issues related to these interactions, and in particular to three-dimensional interactions, motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local and directional observables, to integrated energetic quantities. In the continuity of this community effort, we propose here an open-source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of path-tracing in complex data, typically high-resolution large-domain grounds and clouds. The main algorithmic advances embedded in the library are those related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null-collision (maximum cross-section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitivive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Two other examples are given as illustrations, that are respectively used to analyse the transmission of solar radiation under a cloud together with its sensitivity to an optical parameter, and to assess a parametrization of 3D radiative effects of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2

The efficient global primitive equation climate model SPEEDO V2.0

The efficient primitive-equation coupled atmosphere-ocean model SPEEDO V2.0 is presented. The model includes an interactive sea-ice and land component. SPEEDO is a global earth system model of intermediate complexity. It has a horizontal resolution of T30 (triangular truncation at wave number 30) and 8 vertical layers in the atmosphere, and a horizontal resolution of 2 degrees and 20 levels in the ocean. The parameterisations in SPEEDO are developed in such a way that it is a fast model suitable for large ensembles or long runs (of O(104) years) on a typical current workstation. The model has no flux correction. We compare the mean state and inter-annual variability of the model with observational fields of the atmosphere and ocean. In particular the atmospheric circulation, the midlatitude patterns of variability and teleconnections from the tropics are well simulated. To show the capabilities of the model, we performed a long control run and an ensemble experiment with enhanced greenhouse gases. The long control run shows that the model is stable. CO2 doubling and future climate change scenario experiments show a climate sensitivity of 1.84KW-1m2, which is within the range of state-of-the-art climate models. The spatial response patterns are comparable to state-of-the-art, higher resolution models. However, for very high greenhouse gas concentrations the parameterisations are not valid. We conclude that the model is suitable for past, current and future climate simulations and for exploring wide parameter ranges and mechanisms of variability. However, as with any model, users should be careful when using the model beyond the range of physical realism of the parameterisations and model setup

Aerosol activation and cloud processing in the global aerosol-climate model ECHAM5-HAM

A parameterization for cloud processing is presented that calculates activation of aerosol particles to cloud drops, cloud drop size, and pH-dependent aqueous phase sulfur chemistry. The parameterization is implemented in the global aerosol-climate model ECHAM5-HAM. The cloud processing parameterization uses updraft speed, temperature, and aerosol size and chemical parameters simulated by ECHAM5-HAM to estimate the maximum supersaturation at the cloud base, and subsequently the cloud drop number concentration (CDNC) due to activation. In-cloud sulfate production occurs through oxidation of dissolved SO2 by ozone and hydrogen peroxide. The model simulates realistic distributions for annually averaged CDNC although it is underestimated especially in remote marine regions. On average, CDNC is dominated by cloud droplets growing on particles from the accumulation mode, with smaller contributions from the Aitken and coarse modes. The simulations indicate that in-cloud sulfate production is a potentially important source of accumulation mode sized cloud condensation nuclei, due to chemical growth of activated Aitken particles and to enhanced coalescence of processed particles. The strength of this source depends on the distribution of produced sulfate over the activated modes. This distribution is affected by uncertainties in many parameters that play a direct role in particle activation, such as the updraft velocity, the aerosol chemical composition and the organic solubility, and the simulated CDNC is found to be relatively sensitive to these uncertainties

Gliese 581d is the first discovered terrestrial-mass exoplanet in the habitable zone

It has been suggested that the recently discovered exoplanet GJ581d might be able to support liquid water due to its relatively low mass and orbital distance. However, GJ581d receives 35% less stellar energy than Mars and is probably locked in tidal resonance, with extremely low insolation at the poles and possibly a permanent night side. Under such conditions, it is unknown whether any habitable climate on the planet would be able to withstand global glaciation and / or atmospheric collapse. Here we present three-dimensional climate simulations that demonstrate GJ581d will have a stable atmosphere and surface liquid water for a wide range of plausible cases, making it the first confirmed super-Earth (exoplanet of 2-10 Earth masses) in the habitable zone. We find that atmospheres with over 10 bar CO2 and varying amounts of background gas (e.g., N2) yield global mean temperatures above 0 degrees Celsius for both land and ocean-covered surfaces. Based on the emitted IR radiation calculated by the model, we propose observational tests that will allow these cases to be distinguished from other possible scenarios in the future.Comment: Accepted to the Astrophysical Journal Letters; 9 pages, 1 table, 4 figure

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.Comment: 32 pages, 3 figure

How organized is deep convection over Germany?

Deep moist convection shows a tendency to organize into mesoscale structures. To be able to understand the potential effect of convective organization on the climate, one needs first to characterize organization. In this study, we systematically characterize the organizational state of convection over Germany based on two years of cloud-top observations derived from the Meteosat Second Generation satellite and of precipitation cores detected by the German C-band radar network. The organizational state of convection is characterized by commonly employed organization indices, which are mostly based on the object numbers, sizes and nearest-neighbour distances. According to the organization index Iorg, cloud tops and precipitation cores are found to be in an organized state for 69% and 92% of the time, respectively. There is an increase in rainfall when the number of objects and their sizes increase, independently of the organizational state. Case-studies of specific days suggest that convectively organized states correspond to either local multi-cell clusters, with less numerous, larger objects close to each other, or to scattered clusters, with more numerous, smaller organized objects spread out over the domain. For those days, simulations are performed with the large-eddy model ICON with grid spacings of 625, 312 and 156?m. Although the model underestimates rainfall and shows a too large cold cloud coverage, the organizational state is reasonably well represented without significant differences between the grid spacings