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Yuntao Jian, Marco Y. T. Leung, Wen Zhou, Maoqiu Jian, and Song Yang

Abstract

In this study, the relationship between ENSO and winter synoptic temperature variability (STV) over the Asian–Pacific–American region is examined in 26 CMIP5/6 model outputs. Compared to observations, most models fail to simulate the correct ENSO–STV relationship in historical simulations. To investigate the possible bias in the ENSO–STV simulations, two possible processes for the connection between ENSO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. On the one hand, both HPS and LPS models can overall reproduce a reasonable relationship between STV and the mean-flow conditions supporting extratropical eddy development. On the other hand, only HPS models can well capture the relationship between ENSO and the development of extratropical eddies, while LPS models fail to simulate this feature, indicating that the bias in the simulated ENSO–STV relationship among CMIP5/6 models can be traced back to ENSO simulation. Furthermore, the bias of the ENSO simulation is characterized by an unreasonable SST pattern bias, with an excessive westward extension of warm SST anomalies over the western Pacific and weak warm SST anomalies over the equatorial central-eastern Pacific, resulting in the underestimation of the zonal SST anomaly gradient among models. Therefore, the ENSO pattern bias induces an unrealistic circulation and temperature gradient over the Asian–Pacific–American region, affecting the simulations of the ENSO–STV connection. In addition, the ENSO–STV relationship over the Asian–Pacific–American region is still robust in future projections based on HPS models, providing implications for the selection of future climate predictors.

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Michael Maier-Gerber, Andreas H. Fink, Michael Riemer, Elmar Schoemer, Christoph Fischer, and Benedikt Schulz

Abstract

While previous research on subseasonal tropical cyclone (TC) occurrence has mostly focused on either the validation of numerical weather prediction (NWP) models, or the development of statistical models trained on past data, the present study combines both approaches to a statistical–dynamical (hybrid) model for probabilistic forecasts in the North Atlantic basin. Although state-of-the-art NWP models have been shown to lack predictive skill with respect to subseasonal weekly TC occurrence, they may predict the environmental conditions sufficiently well to generate predictors for a statistical model. Therefore, an extensive predictor set was generated, including predictor groups representing the climatological seasonal cycle (CSC), oceanic, and tropical conditions, tropical wave modes, as well as extratropical influences, respectively. The developed hybrid forecast model is systematically validated for the Gulf of Mexico and central main development region (MDR) for lead times up to 5 weeks. Moreover, its performance is compared against a statistical approach trained on past data, as well as against different climatological and NWP benchmarks. For subseasonal lead times, the CSC models are found to outperform the NWP models, which quickly lose skill within the first two forecast weeks, even in case of recalibration. The statistical models trained on past data increase skill over the CSC models, whereas even greater improvements in skill are gained by the hybrid approach out to week 5. The vast majority of the additional subseasonal skill in the hybrid model, relative to the CSC model, could be attributed to the tropical (oceanic) conditions in the Gulf of Mexico (central MDR).

Open access
Jan Wandel, Julian F. Quinting, and Christian M. Grams

Abstract

Warm conveyor belts (WCBs) associated with extratropical cyclones transport air from the lower troposphere into the tropopause region and contribute to upper-level ridge building and the formation of blocking anticyclones. Recent studies indicate that this constitutes an important source and magnifier of forecast uncertainty and errors in numerical weather prediction (NWP) models. However, a systematic evaluation of the representation of WCBs in NWP models has yet to be determined. Here, we employ the logistic regression models developed in Part I to identify the inflow, ascent, and outflow stages of WCBs in the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal reforecasts for Northern Hemisphere winter in the period January 1997 to December 2017. We verify the representation of these WCB stages in terms of systematic occurrence frequency biases, forecast reliability, and forecast skill. Systematic WCB frequency biases emerge already at early lead times of around 3 days with an underestimation for the WCB outflow over the North Atlantic and eastern North Pacific of around 40% relative to climatology. Biases in the predictor variables of the logistic regression models can partially explain these biases in WCB inflow, ascent, or outflow. Despite an overconfidence in predicting high WCB probabilities, skillful WCB forecasts are on average possible up to a lead time of 8–10 days with more skill over the North Pacific compared to the North Atlantic region. Our results corroborate that the current limited forecast skill for the large-scale extratropical circulation on subseasonal time scales beyond 10 days might be tied to the representation of WCBs and associated upscale error growth.

Open access
George C. Craig, Andreas H. Fink, Corinna Hoose, Tijana Janjić, Peter Knippertz, Audine Laurian, Sebastian Lerch, Bernhard Mayer, Annette Miltenberger, Robert Redl, Michael Riemer, Kirsten I. Tempest, and Volkmar Wirth

Abstract

Prediction of weather is a main goal of atmospheric science. Its importance to society is growing continuously due to factors such as vulnerability to natural disasters, the move to renewable energy sources, and the risks of climate change. But prediction is also a major scientific challenge due to the inherently limited predictability of a chaotic atmosphere, and has led to a revolution in forecasting methods as we have moved to probabilistic prediction. These changes provide the motivation for Waves to Weather (W2W), a major national research program in Germany with three main university partners in Munich, Mainz, and Karlsruhe. We are currently in the second 4-yr phase of our planned duration of 12 years and employ 36 doctoral and postdoctoral scientists. In the context of this large program, we address what we have identified to be the most important and challenging scientific questions in predictability of weather, namely, upscale error growth, errors associated with cloud processes, and probabilistic prediction of high-impact weather. This paper presents some key results of the first phase of W2W and discusses how they have influenced our understanding of predictability. The key role of interdisciplinary research linking atmospheric scientists with experts in visualization, statistics, numerical analysis, and inverse methods will be highlighted. To ensure a lasting impact on research in our field in Germany and internationally, we have instituted innovative programs for training and support of early-career scientists, and to support education, equal opportunities, and outreach, which are also described here.

Open access
Volkmar Wirth and Christopher Polster

Abstract

The waveguidability of an upper-tropospheric zonal jet quantifies its propensity to duct Rossby waves in the zonal direction. This property has played a central role in previous attempts to explain large wave amplitudes and the subsequent occurrence of extreme weather. In these studies, waveguidability was diagnosed with the help of ray tracing arguments using the zonal average of the observed flow as the relevant background state. Here, it is argued that this method is problematic both conceptually and mathematically. The issue is investigated in the framework of the nondivergent barotropic model. This model allows the straightforward computation of an alternative “zonalized” background state, which is obtained through conservative symmetrization of potential vorticity contours and that is argued to be superior to the zonal average. Using an idealized prototypical flow configuration with large-amplitude eddies, it is shown that the two different choices for the background state yield very different results; in particular, the zonal-mean background state diagnoses a zonal waveguide, while the zonalized background state does not. This result suggests that the existence of a waveguide in the zonal-mean background state is a consequence of, rather than a precondition for, large wave amplitudes, and it would mean that the direction of causality is opposite to the usual argument. The analysis is applied to two heatwave episodes from summer 2003 and 2010, yielding essentially the same result. It is concluded that previous arguments about the role of waveguidability for extreme weather need to be carefully reevaluated to prevent misinterpretation in the future.

Open access
Eva-Maria Walz, Marlon Maranan, Roderick van der Linden, Andreas H. Fink, and Peter Knippertz

Abstract

Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best available high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multisatellite Retrievals for GPM (IMERG) “final run” in a ±15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as extended probabilistic climatology (EPC) and compute it on a 0.1° × 0.1° grid for 40°S–40°N and the period 2001–19. To reduce and standardize information, a mixed Bernoulli–Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive web tool to the scientific community.

Open access
Julian F. Quinting and Christian M. Grams

Abstract

The physical and dynamical processes associated with warm conveyor belts (WCBs) importantly affect midlatitude dynamics and are sources of forecast uncertainty. Moreover, WCBs modulate the large-scale extratropical circulation and can communicate and amplify forecast errors. Therefore, it is desirable to assess the representation of WCBs in numerical weather prediction (NWP) models in particular on the medium to subseasonal forecast range. Most often, WCBs are identified as coherent bundles of Lagrangian trajectories that ascend in a time interval of 2 days from the lower to the upper troposphere. Although this Lagrangian approach has advanced the understanding of the involved processes significantly, the calculation of trajectories is computationally expensive and requires NWP data at a high spatial [O(~1)], vertical [O(~10hPa)], and temporal resolution [O(~36h)]. In this study, we present a statistical framework that derives footprints of WCBs from coarser NWP data that are routinely available. To this end, gridpoint-specific multivariate logistic regression models are developed for the Northern Hemisphere using meteorological parameters from ERA-Interim data as predictors and binary footprints of WCB inflow, ascent, and outflow based on a Lagrangian dataset as predictands. Stepwise forward selection identifies the most important predictors for these three WCB stages. The logistic models are reliable in replicating the climatological frequency of WCBs as well as the footprints of WCBs at instantaneous time steps. The novel framework is a first step toward a systematic evaluation of WCB representation in large datasets such as subseasonal ensemble reforecasts or climate projections.

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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

Abstract

Precipitation forecasts are of large societal value in the tropics. Here, we compare 1–5-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16) over 30°S–30°N with an extended probabilistic climatology based on the Tropical Rainfall Measuring Mission 3 B42 gridded dataset. Both models predict rainfall occurrence better than the reference only over about half of all land points, with a better performance by MSC. After applying the postprocessing technique ensemble model output statistics, this fraction increases to 87% (ECMWF) and 82% (MSC). For rainfall amount there is skill in many tropical areas (about 60% of land points), which can be increased by postprocessing to 97% (ECMWF) and 88% (MSC). Forecasts for extremes (>20 mm) are only marginally worse than those of occurrence but do not improve as much through postprocessing, particularly over dry areas. Forecast performance is generally best over arid Australia and worst over oceanic deserts, the Andes and Himalayas, as well as over tropical Africa, where models misrepresent the high degree of convective organization, such that even postprocessed forecasts are hardly better than climatology. Skill of 5-day accumulated forecasts often exceeds that of shorter ranges, as timing errors matter less. An increase in resolution and major model update in 2010 has significantly improved ECMWF predictions. Especially over tropical Africa new techniques such as convection-permitting models or combined statistical-dynamical forecasts may be needed to generate skill beyond the climatological reference.

Open access
Kevin Bachmann, Christian Keil, George C. Craig, Martin Weissmann, and Christian A. Welzbacher

Abstract

We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

Free access
Mirjam Hirt, Stephan Rasp, Ulrich Blahak, and George C. Craig

Abstract

Kilometer-scale models allow for an explicit simulation of deep convective overturning but many subgrid processes that are crucial for convective initiation are still poorly represented. This leads to biases such as insufficient convection triggering and late peak of summertime convection. A physically based stochastic perturbation scheme (PSP) for subgrid processes has been proposed (Kober and Craig) that targets the coupling between subgrid turbulence and resolved convection. The first part of this study presents four modifications to this PSP scheme for subgrid turbulence: an autoregressive, continuously evolving random field; a limitation of the perturbations to the boundary layer that removes artificial convection at night; a mask that turns off perturbations in precipitating columns to retain coherent structures; and nondivergent wind perturbations that drastically increase the effectiveness of the vertical velocity perturbations. In a revised version, PSP2, the combined modifications retain the physically based coupling to the boundary layer scheme of the original scheme while removing undesirable side effects. This has the potential to improve predictions of convective initiation in kilometer-scale models while minimizing other biases. The second part of the study focuses on perturbations to account for convective initiation by subgrid orography. Here the mechanical lifting effect is modeled by introducing vertical and horizontal wind perturbations of an orographically induced gravity wave. The resulting perturbations lead to enhanced convective initiation over mountainous terrain. However, the total benefit of this scheme is unclear and we do not adopt the scheme in our revised configuration.

Free access