Simulation of the global ENSO–Tropical cyclone teleconnection by a high-resolution coupled general circulation model

This study assesses the influence of the El Niño–Southern Oscillation (ENSO) on global tropical cyclone activity using a 150-yr-long integration with a high-resolution coupled atmosphere–ocean general circulation model [High-Resolution Global Environmental Model (HiGEM); with N144 resolution: ~90 km in the atmosphere and ~40 km in the ocean]. Tropical cyclone activity is compared to an atmosphere-only simulation using the atmospheric component of HiGEM (HiGAM). Observations of tropical cyclones in the International Best Track Archive for Climate Stewardship (IBTrACS) and tropical cyclones identified in the Interim ECMWF Re-Analysis (ERA-Interim) are used to validate the models. Composite anomalies of tropical cyclone activity in El Niño and La Niña years are used. HiGEM is able to capture the shift in tropical cyclone locations to ENSO in the Pacific and Indian Oceans. However, HiGEM does not capture the expected ENSO–tropical cyclone teleconnection in the North Atlantic. HiGAM shows more skill in simulating the global ENSO–tropical cyclone teleconnection; however, variability in the Pacific is overpronounced. HiGAM is able to capture the ENSO–tropical cyclone teleconnection in the North Atlantic more accurately than HiGEM. An investigation into the large-scale environmental conditions, known to influence tropical cyclone activity, is used to further understand the response of tropical cyclone activity to ENSO in the North Atlantic and western North Pacific. The vertical wind shear response over the Caribbean is not captured in HiGEM compared to HiGAM and ERA-Interim. Biases in the mean ascent at 500 hPa in HiGEM remain in HiGAM over the western North Pacific; however, a more realistic low-level vorticity in HiGAM results in a more accurate ENSO–tropical cyclone teleconnection

Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)

The PRIMAVERA project aimed to develop a new generation of advanced and well-evaluated high-resolution global climate models. As part of PRIMAVERA, seven different climate models were run in both standard and higher-resolution configurations, with common initial conditions and forcings to form a multi-model ensemble. The ensemble simulations were run on high-performance computers across Europe and generated approximately 1.6 PiB (pebibytes) of output. To allow the data from all models to be analysed at this scale, PRIMAVERA scientists were encouraged to bring their analysis to the data. All data were transferred to a central analysis facility (CAF), in this case the JASMIN super-data-cluster, where it was catalogued and details made available to users using the web interface of the PRIMAVERA Data Management Tool (DMT). Users from across the project were able to query the available data using the DMT and then access it at the CAF. Here we describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this multi-model dataset. We believe that PRIMAVERA's experience using a CAF demonstrates how similar, multi-institute, big-data projects can efficiently share, organise and analyse large volumes of data.</p

How well are Tropical Cyclones represented in reanalysis data sets?

Tropical cyclones (TCs) are identified and tracked in six recent reanalysis data sets and compared with those from the IBTrACS best track archive. Results indicate that nearly every cyclone present in IBTrACS over the period 1979-2012 can be found in all six reanalyses using a tracking and matching approach. However, TC intensities are significantly under-represented in the reanalyses compared to the observations. Applying a typical objective TC identification scheme, it is found that the largest uncertainties in TC identification occur for the weaker storms; this is exacerbated by uncertainties in the observations for weak storms and lack of consistency in operational procedures. For example, it is unclear whether certain types of storms, such as tropical depressions, subtropical cyclones and monsoon depressions, are included in the best track data for all reporting agencies. There are definite improvements in how well TCs are represented in more recent, higher resolution reanalyses; in particular MERRA2 is comparable with the NCEP-CFSR and JRA55 reanalyses, which perform significantly better than the older MERRA reanalysis

Extratropical transition of tropical cyclones in a multiresolution ensemble of atmosphere-only and fully coupled global Climate Models

Tropical cyclones undergo extratropical transition (ET) in every ocean basin. Projected changes in ET frequency under climate change are uncertain and differ between basins, so multimodel studies are required to establish confidence. We used a feature-tracking algorithm to identify tropical cyclones and performed cyclone phase-space analysis to identify ET in an ensemble of atmosphere-only and fully coupled global model simulations, run at various resolutions under historical (1950–2014) and future (2015–50) forcing. Historical simulations were evaluated against five reanalyses for 1979–2018. Considering ET globally, ensemble-mean biases in track and genesis densities are reduced in the North Atlantic and western North Pacific when horizontal resolution is increased from ∼100 to ∼25 km. At high resolution, multi-reanalysis-mean climatological ET frequencies across most ocean basins as well as basins’ seasonal cycles are reproduced better than in low-resolution models. Skill in simulating historical ET interannual variability in the North Atlantic and western North Pacific is ∼0.3, which is lower than for all tropical cyclones. Models project an increase in ET frequency in the North Atlantic and a decrease in the western North Pacific. We explain these opposing responses by secular change in ET seasonality and an increase in lower-tropospheric, pre-ET warm-core strength, both of which are largely unique to the North Atlantic. Multimodel consensus about climate change responses is clearer for frequency metrics than for intensity metrics. These results help clarify the role of model resolution in simulating ET and help quantify uncertainty surrounding ET in a warming climate.All authors received financial support from the PRIMAVERA project (European Commission Horizon2020 Grant Agreement 641727) with data access via JASMIN (https://jasmin.ac.uk) supported by IS-ENES3 (Grant Agreement 824084). AJB also received support from National Environmental Research Council (NERC) national capability grant for the North Atlantic Climate System: Integrated study (ACSIS) program (Grants NE/N018001/1, NE/N018044/1, NE/N018028/1, and NE/N018052/1). KL received funding from the German Federal Ministry of Education and Research (BMBF) through JPI Climate/JPI Oceans NextG-Climate Science-ROADMAP (FKZ: 01LP2002A). The authors are grateful to the editor and to three anonymous reviewers, whose recommendations improved this paper. AJB, PLV, RJH, and MJR conceived the study. Simulations were performed by MJR, ET, KL, CDR, and LT. Output data were managed by JS. MJR performed the cyclone tracking. BV computed the Eady growth rate. AJB undertook cyclone phase-space analysis and all other data analyses, figure preparation, and wrote the manuscript. All authors provided input in interpreting results and approved the final manuscript. The authors declare no competing interests.Peer Reviewed"Article signat per 13 autors/es: Alexander J. Baker, Malcolm J. Roberts, Pier Luigi Vidale, Kevin I. Hodges, Jon Seddon, Benoît Vannière, Rein J. Haarsma, Reinhard Schiemann, Dimitris Kapetanakis, Etienne Tourigny, Katja Lohmann, Christopher D. Roberts, and Laurent Terray"Postprint (published version

Adaptive constraints for feature tracking

In this paper extensions to an existing tracking algorithm are described. These extensions implement adaptive tracking constraints in the form of regional upper-bound displacements and an adaptive track smoothness constraint. Together, these constraints make the tracking algorithm more flexible than the original algorithm (which used fixed tracking parameters) and provide greater confidence in the tracking results. The result of applying the new algorithm to high-resolution ECMWF reanalysis data is shown as an example of its effectiveness

Impact of increased horizontal resolution in coupled and atmosphere-only models of the HadGEM1 family upon the climate patterns of South America

This study investigates the impact of increased horizontal resolution in coupled and atmosphere-only global climate models on the simulation of climate patterns in South America (SA). We analyze simulations of the HadGEM1 model family with three different horizontal resolutions in the atmosphere—N96 (~135 km at 50°N), N144 (~90 km) and N216 (~60 km)—and two different resolutions in the ocean—1° and 1/3°. In general, the coupled simulation with the highest resolution (60 km in the atmosphere and 1/3° in the ocean) has smaller systematic errors in seasonal mean precipitation, temperature and circulation over SA than the atmosphere-only model at all resolutions. The models, both coupled and atmosphere-only, properly simulate spatial patterns of the seasonal shift of the Intertropical Convergence Zone (ITCZ), the formation and positioning of the South Atlantic Convergence Zone (SACZ), and the subtropical Atlantic and Pacific highs. However, the models overestimate rainfall, especially in the ITCZ and over the western border of high-elevation areas such as southern Chile. The coupling, combined with higher resolution, result in a more realistic spatial pattern of rain, particularly over the Atlantic ITCZ and the continental branch of the SACZ. All models correctly simulate the phase and amplitude of the annual cycle of precipitation and air temperature over most of South America. The overall results show that despite some problems, increasing the resolution in the HadGEM1 model family results in a more realistic representation of climate patterns over South America and the adjacent oceans

Contribution of tropical cyclones to atmospheric moisture transport and rainfall over East Asia

The coastal region of East Asia (EA) is one of the regions with the most frequent impacts from tropical cyclones (TCs). In this study, rainfall and moisture transports related to TCs are measured over the EA, and the contribution of TCs to the regional water budget is compared with other contributors, especially the mean circulation of the EA summer monsoon (EASM). Based on ERA-Interim re-analysis (1979–2012), the trajectories of TCs are identified using an objective feature tracking method. Over 60% of TCs occur from July to October (JASO). During JASO, TC rainfall contributes 10-30% the of monthly total rainfall over the coastal region of EA; this contribution is highest over the south/southeast coast of China in September. TCs make a larger contribution to daily extreme rainfall (above the 95th percentile): 50-60% over the EA coast and as high as 70% over Taiwan island. Compared with the mean EASM, TCs transport less moisture over the EA. However, as the peak of the mean seasonal cycle of TCs lags two months behind that of the EASM, the moisture transported by TCs is an important source for the water budget over the EA region when the EASM withdraws. This moisture transport is largely performed by westward-moving TCs. These results improve our understanding of the water cycle of EA and provide a useful test bed for evaluating and improving seasonal forecasts and coupled climate models

A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part I: interannual variability

Interannual rainfall variability in China affects agriculture, infrastructure and water resource management. To improve its understanding and prediction, many studies have associated precipitation variability with particular causes for specific seasons and regions. Here, a consistent and objective method, Empirical Orthogonal Teleconnection (EOT) analysis, is applied to 1951–2007 high-resolution precipitation observations over China in all seasons. Instead of maximizing the explained space–time variance, the method identifies regions in China that best explain the temporal variability in domain-averaged rainfall. The EOT method is validated by the reproduction of known relationships to the El Niño Southern Oscillation (ENSO): high positive correlations with ENSO are found in eastern China in winter, along the Yangtze River in summer, and in southeast China during spring. New findings include that wintertime rainfall variability along the southeast coast is associated with anomalous convection over the tropical eastern Atlantic and communicated to China through a zonal wavenumber-three Rossby wave. Furthermore, spring rainfall variability in the Yangtze valley is related to upper-tropospheric midlatitude perturbations that are part of a Rossby wave pattern with its origin in the North Atlantic. A circumglobal wave pattern in the northern hemisphere is also associated with autumn precipitation variability in eastern areas. The analysis is objective, comprehensive, and produces timeseries that are tied to specific locations in China. This facilitates the interpretation of associated dynamical processes, is useful for understanding the regional hydrological cycle, and allows the results to serve as a benchmark for assessing general circulation models

Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ~200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical Orthogonal Teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms

Modelling European winter wind storm losses in current and future climate

Severe wind storms are one of the major natural hazards in the extratropics and inflict substantial economic damages and even casualties. Insured storm-related losses depend on (i) the frequency, nature and dynamics of storms, (ii) the vulnerability of the values at risk, (iii) the geographical distribution of these values, and (iv) the particular conditions of the risk transfer. It is thus of great importance to assess the impact of climate change on future storm losses. To this end, the current study employs—to our knowledge for the first time—a coupled approach, using output from high-resolution regional climate model scenarios for the European sector to drive an operational insurance loss model. An ensemble of coupled climate-damage scenarios is used to provide an estimate of the inherent uncertainties. Output of two state-of-the-art global climate models (HadAM3, ECHAM5) is used for present (1961-1990) and future climates (2071-2100, SRES A2 scenario). These serve as boundary data for two nested regional climate models with a sophisticated gust parametrizations (CLM, CHRM). For validation and calibration purposes, an additional simulation is undertaken with the CHRM driven by the ERA40 reanalysis. The operational insurance model (Swiss Re) uses a European-wide damage function, an average vulnerability curve for all risk types, and contains the actual value distribution of a complete European market portfolio. The coupling between climate and damage models is based on daily maxima of 10m gust winds, and the strategy adopted consists of three main steps: (i) development and application of a pragmatic selection criterion to retrieve significant storm events, (ii) generation of a probabilistic event set using a Monte-Carlo approach in the hazard module of the insurance model, and (iii) calibration of the simulated annual expected losses with a historic loss data base. The climate models considered agree regarding an increase in the intensity of extreme storms in a band across central Europe (stretching from southern UK and northern France to Denmark, northern Germany into eastern Europe). This effect increases with event strength, and rare storms show the largest climate change sensitivity, but are also beset with the largest uncertainties. Wind gusts decrease over northern Scandinavia and Southern Europe. Highest intra-ensemble variability is simulated for Ireland, the UK, the Mediterranean, and parts of Eastern Europe. The resulting changes on European-wide losses over the 110-year period are positive for all layers and all model runs considered and amount to 44% (annual expected loss), 23% (10years loss),50% (30years loss), and 104% (100years loss). There is a disproportionate increase in losses for rare high-impact events. The changes result from increases in both severity and frequency of wind gusts. Considerable geographical variability of the expected losses exists, with Denmark and Germany experiencing the largest loss increases (116% and 114%, respectively). All countries considered except for Ireland (−22%) experience some loss increases. Some ramifications of these results for the socio-economic sector are discussed, and future avenues for research are highlighted. The technique introduced in this study and its application to realistic market portfolios offer exciting prospects for future research on the impact of climate change that is relevant for policy makers, scientists and economist