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Aaron J. Hill, Christopher C. Weiss, and David C. Dowell

Abstract

Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification of the Origins of Rotation in Tornadoes Experiment–Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the Southeast United States. StickNet observations are assimilated with an experimental version of the High-Resolution Rapid Refresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

Open access
Torbjørn Selseng, Marit Klemetsen, and Tone Rusdal

Abstract

In recent decades there has been a surge in the scholarship on climate change adaptation (CCA) terminology, and diverging interpretations of the term have emerged. Given the crucial role of local governments in building societywide adaptive capacity, understanding how municipalities understand and interpret CCA is important. In this study, we analyze 12 large-scale questionnaires from 2007 to 2020 distributed to all Norwegian municipalities. Using a combination of directed and conventional content analysis of the questions and answers, we summarize and map the progress of adaptation work over the 14 years and assess the consistency and the scope of the surveys in light of the current research on climate adaptation. We find diverging views on what adaptation entails, both from the researchers, in the phrasing of questions, and from the respondents. The empirical evidence suggests an overall imbalanced interpretation of CCA, in terms of the risks and consequences we may face, the climate to which adapting is needed, and adequate adaptation strategies. We go on to discuss the implications of these findings, highlighting the need for a shared and well-communicated framework for local CCA and a closer monitoring of the actual efforts of the municipalities. If instead left unchecked, this confusion might lead to unsustainable maladaptation at the local government level throughout Norway and beyond.

Open access
Guo-Yuan Lien, Chung-Han Lin, Zih-Mao Huang, Wen-Hsin Teng, Jen-Her Chen, Ching-Chieh Lin, Hsu-Hui Ho, Jyun-Ying Huang, Jing-Shan Hong, Chia-Ping Cheng, and Ching-Yuang Huang

Abstract

The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched in June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from the Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semioperational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the ensemble forecast sensitivity to observation impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

Open access
Xiaohe An, Bo Wu, Tianjun Zhou, and Bo Liu

Abstract

The interdecadal Pacific oscillation (IPO) and Atlantic multidecadal oscillation (AMO), two leading modes of decadal climate variability, are not independent. It was proposed that ENSO-like sea surface temperature (SST) variations play a central role in the Pacific responses to the AMO forcing. However, observational analyses indicate that the AMO-related SST anomalies in the tropical Pacific are far weaker than those in the extratropical North Pacific. Here, we show that SST in the North Pacific is tied to the AMO forcing by convective heating associated with precipitation over the tropical Pacific, instead of by SST there, based on an ensemble of pacemaker experiments with North Atlantic SST restored to the observation in a coupled general circulation model. The AMO modulates precipitation over the equatorial and tropical southwestern Pacific through exciting an anomalous zonal circulation and an interhemispheric asymmetry of net moist static energy input into the atmosphere. The convective heating associated with the precipitation anomalies drives SST variations in the North Pacific through a teleconnection, but it remarkably weakens the ENSO-like SST anomalies through a thermocline damping effect. This study has implications that the IPO is a combined mode generated by both AMO forcing and local air–sea interactions, but the IPO-related global warming acceleration/slowdown is independent of the AMO.

Open access
Jingqiu Yang, Haishan Chen, Yidi Song, Siguang Zhu, Botao Zhou, and Jie Zhang

Abstract

The Eurasian continent experienced significant warming during the past decades. West Asia is located in an arid/semiarid zone, and its warming amplification has drawn considerable attention. However, the climatic effect of such a warming is not clear yet. In this study, we explored the possible impacts of recent land surface warming over West Asia on the atmospheric general circulation and climate. Results show that abnormal spring land surface warming over West Asia tends to increase precipitation over North China and decrease (increase) precipitation (air temperature) over Northeast China in early summer (June). It is noted that the precipitation anomalies are much stronger over the eastern region of North/Northeast China. Further analysis suggests that abnormal spring land surface warming can trigger eastward-propagating disturbances via diabatic heating, which intensifies the atmospheric circumglobal teleconnection (CGT) pattern, causing anomalous circulation and climate in early summer over northern China. Sensitivity experiments demonstrate that abnormal spring land surface warming can increase the atmospheric baroclinic instability and trigger Rossby waves that propagate along the westerly jet stream (WJS), resulting in the formation of CGT. Due to persistent land surface thermal forcing and the interaction between the basic flow (especially the WJS) and CGT, the CGT tends to be intensified. The anomalous wave center over East Asia in early summer is responsible for the precipitation increases (decreases) over North (Northeast) China and the evident warming in Northeast China. Our results suggest that the spring land surface thermal anomalies over West Asia can be a potential signal for short-term prediction of early summer climate over northern China.

Open access
Tobias Goecke and Ekaterina Machulskaya

Abstract

We present a detailed evaluation of the turbulence forecast product eddy dissipation parameter (EDP) used at the Deutscher Wetterdienst (DWD). It is based on the turbulence parameterization scheme TURBDIFF, which is operational within the Icosahedral Nonhydrostatic (ICON) numerical weather prediction model used operationally by DWD. For aviation purposes, the procedure provides the cubic root of the eddy dissipation rate ε 1/3 as an overall turbulence index. This quantity is a widely used measure for turbulence intensity as experienced by aircraft. The scheme includes additional sources of turbulent kinetic energy with particular relevance to aviation, which are briefly introduced. These sources describe turbulence generation by the subgrid-scale action of wake eddies, mountain waves, and convection, as well as horizontal shear as found close to fronts or the jet stream. Furthermore, we introduce a postprocessing calibration to an empirical EDR distribution, and we demonstrate the potential as well as limitations of the final EDP-based turbulence forecast by considering several case studies of typical turbulence events. Finally, we reveal the forecasting capability of this product by verifying the model results against one year of aircraft in situ EDR measurements from commercial aircraft. We find that the forecasted EDP performs favorably when compared to the Ellrod index. In particular, the turbulence signal from deep convection, which is accounted for in the EDP product, is advantageous when spatial nonlocality is allowed in the verification procedure.

Open access
Alexander J. Ross, Ryan C. Grow, Lauren D. Hayhurst, Haley A. MacLeod, Graydon I. McKee, Kyle W. Stratton, Marissa E. Wegher, and Michael D. Rennie

Abstract

Groundhog Day is a widespread North American ritual that marks the onset of spring, with festivities centered around animals that humans believe have abilities to make seasonal predictions. Yet, the collective success of groundhog Marmota monax prognosticators has never been rigorously tested. Here, we propose the local climate-predicted phenology of early blooming spring plants (Carolina spring beauty, or Claytonia caroliniana, which overlaps in native range with groundhogs) as a novel and relevant descriptor of spring onset that can be applied comparatively across a broad geographical range. Of 530 unique groundhog-year predictions across 33 different locations, spring onset was correctly predicted by groundhogs exactly 50% of the time. While no singular groundhog predicted the timing of spring with any statistical significance, there were a handful of groundhogs with notable records of both successful and unsuccessful predictions: Essex Ed (Essex, Connecticut), Stonewall Jackson (Wantage, New Jersey), and Chuckles (Manchester, Connecticut) correctly predicted spring onset over 70% of the time. By contrast, Buckeye Chuck (Marion, Ohio), Dunkirk Dave (Dunkirk, New York), and Holland Huckleberry (Holland, Ohio) made incorrect predictions over 70% of the time. The two most widely recognized and long-tenured groundhogs in their respective countries—Wiarton Willie (Canada) and Punxsutawney Phil (United States)—had success rates of 54% and 52%, respectively, despite over 150 collective guesses. Using a novel phenological indicator of spring, this study determined, without a shadow of a doubt, that groundhog prognosticating abilities for the arrival of spring are no better than chance.

Open access
Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Mark A. Collier, Russell Fiedler, Thomas S. Moore, Christopher C. Chapman, Bernadette M. Sloyan, and Richard J. Matear

Abstract

We detail the system design, model configuration, and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1). CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multiyear climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to the present. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are subsampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross-domain covariances between ocean, atmosphere, sea ice, and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean, and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions, potentially enabling individual forecasts for all members each month over the 60-yr period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

Open access
Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Dougal T. Squire, Mark A. Collier, Christopher C. Chapman, Russell Fiedler, Dylan Harries, Thomas S. Moore, Doug Richardson, James S. Risbey, Benjamin J. E. Schroeter, Serena Schroeter, Bernadette M. Sloyan, Carly Tozer, Ian G. Watterson, Amanda Black, Courtney Quinn, and Richard J. Matear

Abstract

The CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatiotemporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere, and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large-scale ocean structure, transports, and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration, and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive, and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multiyear to decadal time scales.

Open access
T. C. Johns, E. W. Blockley, and J. K. Ridley

Abstract

We present a coupled retrospective forecast (hindcast) study using the Met Office Global Coupled Model, version 2 (GC2), in which we identify and mitigate causes of initialization shock that lead to rapid error growth in sea ice forecasts. Sea ice state variables and volume budget terms as a function of forecast lead time are evaluated relative to analyses from an uncoupled Met Office ocean–sea ice analysis system [Forecast Ocean Assimilation Model, version 13 (FOAMv13)]. Two sources of initialization shock are highlighted and addressed, both of which are related to effective differences in physics between the analysis system and coupled forecast model. The primary shock to sea ice state variables arises from the use of a salinity-independent freezing temperature for seawater in GC2 as opposed to a salinity-dependent formulation in FOAMv13. A secondary effect arises from differences in the sea ice roughness and hence air–ice drag in the GC2 forecast model compared to the FOAMv13 analysis system. Generalizing from the findings of this study, we suggest that using nonnative analyses as initial conditions for coupled numerical weather prediction (NWP) studies will likely make them prone to initialization shock in some model components, to the detriment of forecast skill. To reduce the undesirable impacts of initialization shock on short-range forecast skill noted in this study we would therefore recommend the use of initial conditions (analyses) physically consistent with the native model components of the coupled forecast model, a native coupled analysis likely being the optimal initialization method.

Open access