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Daniel Klocke, Robert Pincus, and Johannes Quaas

Abstract

The distribution of model-based estimates of equilibrium climate sensitivity has not changed substantially in more than 30 years. Efforts to narrow this distribution by weighting projections according to measures of model fidelity have so far failed, largely because climate sensitivity is independent of current measures of skill in current ensembles of models. This work presents a cautionary example showing that measures of model fidelity that are effective at narrowing the distribution of future projections (because they are systematically related to climate sensitivity in an ensemble of models) may be poor measures of the likelihood that a model will provide an accurate estimate of climate sensitivity (and thus degrade distributions of projections if they are used as weights). Furthermore, it appears unlikely that statistical tests alone can identify robust measures of likelihood. The conclusions are drawn from two ensembles: one obtained by perturbing parameters in a single climate model and a second containing the majority of the world’s climate models. The simple ensemble reproduces many aspects of the multimodel ensemble, including the distributions of skill in reproducing the present-day climatology of clouds and radiation, the distribution of climate sensitivity, and the dependence of climate sensitivity on certain cloud regimes. Weighting by error measures targeted on those regimes permits the development of tighter relationships between climate sensitivity and model error and, hence, narrower distributions of climate sensitivity in the simple ensemble. These relationships, however, do not carry into the multimodel ensemble. This suggests that model weighting based on statistical relationships alone is unfounded and perhaps that climate model errors are still large enough that model weighting is not sensible.

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Fabian Senf, Matthias Brueck, and Daniel Klocke

Abstract

Over the tropical oceans, the large-scale, meridional circulation drives the accumulation of moist and warm air, leading to a relatively narrow, convectively active band. Therein, deep moist convection interacts with its heterogeneous environment—the intertropical convergence zone (ITCZ)—and organizes into multiscale structures that strongly impact Earth’s hydrological cycle and radiation budget. Understanding the spatial correlations and interactions among deep convective clouds is important, but challenging. These clouds are investigated in this study with the help of large-domain, storm-resolving simulations over the tropical Atlantic. Based on vertically integrated mass flux fields, deep convective updraft cells are identified with object-based techniques and analyzed with respect to their structural behavior and spatial arrangement. The pair-correlation method, which compares simulated pair numbers as a function of pair distance to an appropriately chosen reference, is applied and extended to allow for spatial statistics in a heterogeneous environment (i.e., the ITCZ). Based on pair-correlation analysis, the average probability is enhanced to find an updraft cell pair within 100 km compared to a random distribution. Additionally, the spatial arrangement of larger or stronger cells deviates more from randomness compared to smaller or weaker cells, which might be related to their stronger dynamical interaction mechanisms. Using simplified equilibrium statistics of interacting cells, several spatial characteristics of the storm-resolving simulations can be reproduced.

Open access
Fabian Senf, Daniel Klocke, and Matthias Brueck

Abstract

Deep moist convection is an inherently multiscale phenomenon with organization processes coupling convective elements to larger-scale structures. A realistic representation of the tropical dynamics demands a simulation framework that is capable of representing physical processes across a wide range of scales. Therefore, storm-resolving numerical simulations at 2.4 km have been performed covering the tropical Atlantic and neighboring parts for 2 months. The simulated cloud fields are combined with infrared geostationary satellite observations, and their realism is assessed with the help of object-based evaluation methods. It is shown that the simulations are able to develop a well-defined intertropical convergence zone. However, marine convective activity measured by the cold cloud coverage is considerably underestimated, especially for the winter season and the western Atlantic. The spatial coupling across the resolved scales leads to simulated cloud number size distributions that follow power laws similar to the observations, with slopes steeper in winter than summer and slopes steeper over ocean than over land. The simulated slopes are, however, too steep, indicating too many small and too few large tropical cloud cells. It is also discussed that the number of larger cells is less influenced by multiday variability of environmental conditions. Despite the identified deficits, the analyzed simulations highlight the great potential of this modeling framework for process-based studies of tropical deep convection.

Open access
Mirjana Sakradzija, Fabian Senf, Leonhard Scheck, Maike Ahlgrimm, and Daniel Klocke

Abstract

The local impact of stochastic shallow convection on clouds and precipitation is tested in a case study over the tropical Atlantic on 20 December 2013 using the Icosahedral Nonhydrostatic Model (ICON). ICON is used at a grid resolution of 2.5 km and is tested in several configurations that differ in their treatment of shallow convection. A stochastic shallow convection scheme is compared to the operational deterministic scheme and a case with no representation of shallow convection. The model is evaluated by comparing synthetically generated irradiance data for both visible and infrared wavelengths against actual satellite observations. The experimental approach is designed to distinguish the local effects of parameterized shallow convection (or lack thereof) within the trades versus the ITCZ. The stochastic cases prove to be superior in reproducing low-level cloud cover, deep convection, and its organization, as well as the distribution of precipitation in the tropical Atlantic ITCZ. In these cases, convective heating in the subcloud layer is substantial, and boundary layer depth is increased as a result of the heating, while evaporation is enhanced at the expense of sensible heat flux at the ocean’s surface. The stochastic case where subgrid shallow convection is deactivated below the resolved deep updrafts indicates that local boundary layer convection is crucial for a better representation of deep convection. Based on these results, our study points to a necessity to further develop parameterizations of shallow convection for use at the convection-permitting resolutions and to assuredly include them in weather and climate models even as their imperfect versions.

Restricted access
Claudia Christine Stephan, Cornelia Strube, Daniel Klocke, Manfred Ern, Lars Hoffmann, Peter Preusse, and Hauke Schmidt

Abstract

Large uncertainties remain with respect to the representation of atmospheric gravity waves (GWs) in general circulation models (GCMs) with coarse grids. Insufficient parameterizations result from a lack of observational constraints on the parameters used in GW parameterizations as well as from physical inconsistencies between parameterizations and reality. For instance, parameterizations make oversimplifying assumptions about the generation and propagation of GWs. Increasing computational capabilities now allow GCMs to run at grid spacings that are sufficiently fine to resolve a major fraction of the GW spectrum. This study presents the first intercomparison of resolved GW pseudomomentum fluxes (GWMFs) in global convection-permitting simulations and those derived from satellite observations. Six simulations of three different GCMs are analyzed over the period of one month of August to assess the sensitivity of GWMF to model formulation and horizontal grid spacing. The simulations reproduce detailed observed features of the global GWMF distribution, which can be attributed to realistic GWs from convection, orography, and storm tracks. Yet the GWMF magnitudes differ substantially between simulations. Differences in the strength of convection may help explain differences in the GWMF between simulations of the same model in the summer low latitudes where convection is the primary source. Across models, there is no evidence for a systematic change with resolution. Instead, GWMF is strongly affected by model formulation. The results imply that validating the realism of simulated GWs across the entire resolved spectrum will remain a difficult challenge not least because of a lack of appropriate observational data.

Open access
Wiebke Schubotz, Daniel Klocke, Ulrich Löhnert, Andreas Macke, Bjorn Stevens, and Allison Wing
Free access
Xubin Zeng, Daniel Klocke, Ben J. Shipway, Martin S. Singh, Irina Sandu, Walter Hannah, Peter Bogenschutz, Yunyan Zhang, Hugh Morrison, Michael Pritchard, and Catherine Rio
Open access
Frédéric Hourdin, Thorsten Mauritsen, Andrew Gettelman, Jean-Christophe Golaz, Venkatramani Balaji, Qingyun Duan, Doris Folini, Duoying Ji, Daniel Klocke, Yun Qian, Florian Rauser, Catherine Rio, Lorenzo Tomassini, Masahiro Watanabe, and Daniel Williamson

Abstract

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

Full access
Mark J Rodwell, Linus Magnusson, Peter Bauer, Peter Bechtold, Massimo Bonavita, Carla Cardinali, Michail Diamantakis, Paul Earnshaw, Antonio Garcia-Mendez, Lars Isaksen, Erland Källén, Daniel Klocke, Philippe Lopez, Tony McNally, Anders Persson, Fernando Prates, and Nils Wedi

Medium-range weather prediction has become more skillful over recent decades, but forecast centers still suffer from occasional very poor forecasts, which are often referred to as “dropouts” or “busts.” This study focuses on European Centre for Medium-Range Weather Forecasts (ECMWF) day-6 forecasts for Europe. Although busts are defined by gross scores, bust composites reveal a coherent “Rex type” blocking situation, with a high over northern Europe and a low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective available potential energy (CAPE) to its east. This flow type occurs in spring and is often associated with a Rossby wave train that has crossed the Pacific. A composite on this initial flow type displays enhanced day-6 random forecast errors and some-what enhanced ensemble forecast spread, indicating reduced inherent predictability.

Mesoscale convective systems, associated with the high levels of CAPE, act to slow the motion of the trough. Hence, convection errors play an active role in the busts. The subgrid-scale nature of convection highlights the importance of the representation of model uncertainty in probabilistic forecasts. The cloud and extreme conditions associated with mesoscale convective systems also reduce the availability and utility of observations provided to the data assimilation.

A question of relevance to the wider community is, do we have observations with sufficient accuracy to better constrain the important error structures in the initial conditions? Meanwhile, improvements to ensemble prediction systems should help us better predict the increase in forecast uncertainty.

Full access
Markus Gross, Hui Wan, Philip J. Rasch, Peter M. Caldwell, David L. Williamson, Daniel Klocke, Christiane Jablonowski, Diana R. Thatcher, Nigel Wood, Mike Cullen, Bob Beare, Martin Willett, Florian Lemarié, Eric Blayo, Sylvie Malardel, Piet Termonia, Almut Gassmann, Peter H. Lauritzen, Hans Johansen, Colin M. Zarzycki, Koichi Sakaguchi, and Ruby Leung

Abstract

Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.

Open access