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M. J. Rodwell, D. S. Richardson, D. B. Parsons, and H. Wernli

Abstract

While chaos ensures that probabilistic weather forecasts cannot always be “sharp,” it is important for users and developers that they are reliable. For example, they should not be overconfident or underconfident. The “spread–error” relationship is often used as a first-order assessment of the reliability of ensemble weather forecasts. This states that the ensemble standard deviation (a measure of forecast uncertainty) should match the root-mean-square error on the ensemble mean (when averaged over a sufficient number of forecast start dates). It is shown here that this relationship is now largely satisfied at the European Centre for Medium-Range Weather Forecasts (ECMWF) for ensemble forecasts of the midlatitude, midtropospheric flow out to lead times of at least 10 days when averaged over all flow situations throughout the year. This study proposes a practical framework for continued improvement in the reliability (and skill) of such forecasts. This involves the diagnosis of flow-dependent deficiencies in short-range (∼12 h) reliability for a range of synoptic-scale flow types and the prioritization of modeling research to address these deficiencies. The approach is demonstrated for a previously identified flow type, a trough over the Rockies with warm, moist air ahead. The mesoscale convective systems that can ensue are difficult to predict and, by perturbing the jet stream, are thought to lead to deterministic forecast “busts” for Europe several days later. The results here suggest that jet stream spread is insufficient during this flow type, and thus unreliable. This is likely to mean that the uncertain forecasts for Europe may, nevertheless, still be overconfident.

Open access
L. Magnusson, J.-R. Bidlot, M. Bonavita, A. R. Brown, P. A. Browne, G. De Chiara, M. Dahoui, S. T. K. Lang, T. McNally, K. S. Mogensen, F. Pappenberger, F. Prates, F. Rabier, D. S. Richardson, F. Vitart, and S. Malardel

Abstract

Tropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.

Open access

The Arm Program's Water Vapor Intensive Observation Periods

Overview, Initial Accomplishments, and Future Challenges

H. E. Revercomb, D. D. Turner, D. C. Tobin, R. O. Knuteson, W. F. Feltz, J. Barnard, J. Bösenberg, S. Clough, D. Cook, R. Ferrare, J. Goldsmith, S. Gutman, R. Halthore, B. Lesht, J. Liljegren, H. Linné, J. Michalsky, V. Morris, W. Porch, S. Richardson, B. Schmid, M. Splitt, T. Van Hove, E. Westwater, and D. Whiteman

A series of water vapor intensive observation periods (WVIOPs) were conducted at the Atmospheric Radiation Measurement (ARM) site in Oklahoma between 1996 and 2000. The goals of these WVIOPs are to characterize the accuracy of the operational water vapor observations and to develop techniques to improve the accuracy of these measurements.

The initial focus of these experiments was on the lower atmosphere, for which the goal is an absolute accuracy of better than 2% in total column water vapor, corresponding to ~1 W m−2 of infrared radiation at the surface. To complement the operational water vapor instruments during the WVIOPs, additional instrumentation including a scanning Raman lidar, microwave radiometers, chilled-mirror hygrometers, a differential absorption lidar, and ground-based solar radiometers were deployed at the ARM site. The unique datasets from the 1996, 1997, and 1999 experiments have led to many results, including the discovery and characterization of a large (> 25%) sonde-to-sonde variability in the water vapor profiles from Vaisala RS-80H radiosondes that acts like a height-independent calibration factor error. However, the microwave observations provide a stable reference that can be used to remove a large part of the sonde-to-sonde calibration variability. In situ capacitive water vapor sensors demonstrated agreement within 2% of chilled-mirror hygrometers at the surface and on an instrumented tower. Water vapor profiles retrieved from two Raman lidars, which have both been calibrated to the ARM microwave radiometer, showed agreement to within 5% for all altitudes below 8 km during two WVIOPs. The mean agreement of the total precipitable water vapor from different techniques has converged significantly from early analysis that originally showed differences up to 15%. Retrievals of total precipitable water vapor (PWV) from the ARM microwave radiometer are now found to be only 3% moister than PWV derived from new GPS results, and about 2% drier than the mean of radiosonde data after a recently defined sonde dry-bias correction is applied. Raman lidar profiles calibrated using tower-mounted chilled-mirror hygrometers confirm the expected sensitivity of microwave radiometer data to water vapor changes, but it is drier than the microwave radiometer (MWR) by 0.95 mm for all PWV amounts. However, observations from different collocated microwave radiometers have shown larger differences than expected and attempts to resolve the remaining inconsistencies (in both calibration and forward modeling) are continuing.

The paper concludes by outlining the objectives of the recent 2000 WVIOP and the ARM–First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX), the latter of which switched the focus to characterizing upper-tropospheric humidity measurements.

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David A. Lavers, N. Bruce Ingleby, Aneesh C. Subramanian, David S. Richardson, F. Martin Ralph, James D. Doyle, Carolyn A. Reynolds, Ryan D. Torn, Mark J. Rodwell, Vijay Tallapragada, and Florian Pappenberger

Abstract

A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.

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J. Verlinde, J. Y. Harrington, G. M. McFarquhar, V. T. Yannuzzi, A. Avramov, S. Greenberg, N. Johnson, G. Zhang, M. R. Poellot, J. H. Mather, D. D. Turner, E. W. Eloranta, B. D. Zak, A. J. Prenni, J. S. Daniel, G. L. Kok, D. C. Tobin, R. Holz, K. Sassen, D. Spangenberg, P. Minnis, T. P. Tooman, M. D. Ivey, S. J. Richardson, C. P. Bahrmann, M. Shupe, P. J. DeMott, A. J. Heymsfield, and R. Schofield

The Mixed-Phase Arctic Cloud Experiment (M-PACE) was conducted from 27 September through 22 October 2004 over the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) on the North Slope of Alaska. The primary objectives were to collect a dataset suitable to study interactions between microphysics, dynamics, and radiative transfer in mixed-phase Arctic clouds, and to develop/evaluate cloud property retrievals from surface-and satellite-based remote sensing instruments. Observations taken during the 1977/98 Surface Heat and Energy Budget of the Arctic (SHEBA) experiment revealed that Arctic clouds frequently consist of one (or more) liquid layers precipitating ice. M-PACE sought to investigate the physical processes of these clouds by utilizing two aircraft (an in situ aircraft to characterize the microphysical properties of the clouds and a remote sensing aircraft to constraint the upwelling radiation) over the ACRF site on the North Slope of Alaska. The measurements successfully documented the microphysical structure of Arctic mixed-phase clouds, with multiple in situ profiles collected in both single- and multilayer clouds over two ground-based remote sensing sites. Liquid was found in clouds with cloud-top temperatures as cold as −30°C, with the coldest cloud-top temperature warmer than −40°C sampled by the aircraft. Remote sensing instruments suggest that ice was present in low concentrations, mostly concentrated in precipitation shafts, although there are indications of light ice precipitation present below the optically thick single-layer clouds. The prevalence of liquid down to these low temperatures potentially could be explained by the relatively low measured ice nuclei concentrations.

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L. Liu, D. Shawki, A. Voulgarakis, M. Kasoar, B. H. Samset, G. Myhre, P. M. Forster, Ø. Hodnebrog, J. Sillmann, S. G. Aalbergsjø, O. Boucher, G. Faluvegi, T. Iversen, A. Kirkevåg, J.-F. Lamarque, D. Olivié, T. Richardson, D. Shindell, and T. Takemura

Abstract

Atmospheric aerosols such as sulfate and black carbon (BC) generate inhomogeneous radiative forcing and can affect precipitation in distinct ways compared to greenhouse gases (GHGs). Their regional effects on the atmospheric energy budget and circulation can be important for understanding and predicting global and regional precipitation changes, which act on top of the background GHG-induced hydrological changes. Under the framework of the Precipitation Driver Response Model Intercomparison Project (PDRMIP), multiple models were used for the first time to simulate the influence of regional (Asian and European) sulfate and BC forcing on global and regional precipitation. The results show that, as in the case of global aerosol forcing, the global fast precipitation response to regional aerosol forcing scales with global atmospheric absorption, and the slow precipitation response scales with global surface temperature response. Asian sulfate aerosols appear to be a stronger driver of global temperature and precipitation change compared to European aerosols, but when the responses are normalized by unit radiative forcing or by aerosol burden change, the picture reverses, with European aerosols being more efficient in driving global change. The global apparent hydrological sensitivities of these regional forcing experiments are again consistent with those for corresponding global aerosol forcings found in the literature. However, the regional responses and regional apparent hydrological sensitivities do not align with the corresponding global values. Through a holistic approach involving analysis of the energy budget combined with exploring changes in atmospheric dynamics, we provide a framework for explaining the global and regional precipitation responses to regional aerosol forcing.

Open access
G. Myhre, P. M. Forster, B. H. Samset, Ø. Hodnebrog, J. Sillmann, S. G. Aalbergsjø, T. Andrews, O. Boucher, G. Faluvegi, D. Fläschner, T. Iversen, M. Kasoar, V. Kharin, A. Kirkevåg, J.-F. Lamarque, D. Olivié, T. B. Richardson, D. Shindell, K. P. Shine, C. W. Stjern, T. Takemura, A. Voulgarakis, and F. Zwiers

Abstract

As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of intermodel differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere, leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and better quantifications are needed to improve precipitation predictions. Here, the authors introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve understanding of the causes of the present diversity in future climate projections.

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Sara H. Knox, Robert B. Jackson, Benjamin Poulter, Gavin McNicol, Etienne Fluet-Chouinard, Zhen Zhang, Gustaf Hugelius, Philippe Bousquet, Josep G. Canadell, Marielle Saunois, Dario Papale, Housen Chu, Trevor F. Keenan, Dennis Baldocchi, Margaret S. Torn, Ivan Mammarella, Carlo Trotta, Mika Aurela, Gil Bohrer, David I. Campbell, Alessandro Cescatti, Samuel Chamberlain, Jiquan Chen, Weinan Chen, Sigrid Dengel, Ankur R. Desai, Eugenie Euskirchen, Thomas Friborg, Daniele Gasbarra, Ignacio Goded, Mathias Goeckede, Martin Heimann, Manuel Helbig, Takashi Hirano, David Y. Hollinger, Hiroki Iwata, Minseok Kang, Janina Klatt, Ken W. Krauss, Lars Kutzbach, Annalea Lohila, Bhaskar Mitra, Timothy H. Morin, Mats B. Nilsson, Shuli Niu, Asko Noormets, Walter C. Oechel, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Karina V. R. Schäfer, Hans Peter Schmid, Narasinha Shurpali, Oliver Sonnentag, Angela C. I. Tang, Masahito Ueyama, Rodrigo Vargas, Timo Vesala, Eric J. Ward, Lisamarie Windham-Myers, Georg Wohlfahrt, and Donatella Zona

Abstract

This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH4) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH4 flux measurements globally, initial results comparing CH4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH4 fluxes across sites ranged from −0.2 ± 0.02 g C m–2 yr–1 for an upland forest site to 114.9 ± 13.4 g C m–2 yr–1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m–2 yr–1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH4 flux across wetland sites globally. Water table position was positively correlated with annual CH4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH4 estimates due to gap-filling and random errors were on average ±1.6 g C m–2 yr–1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH4 flux database, the controls on ecosystem CH4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH4 emissions.

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M. Ades, R. Adler, Rob Allan, R. P. Allan, J. Anderson, Anthony Argüez, C. Arosio, J. A. Augustine, C. Azorin-Molina, J. Barichivich, J. Barnes, H. E. Beck, Andreas Becker, Nicolas Bellouin, Angela Benedetti, David I. Berry, Stephen Blenkinsop, Olivier. Bock, Michael G. Bosilovich, Olivier. Boucher, S. A. Buehler, Laura. Carrea, Hanne H. Christiansen, F. Chouza, John R. Christy, E.-S. Chung, Melanie Coldewey-Egbers, Gil P. Compo, Owen R. Cooper, Curt Covey, A. Crotwell, Sean M. Davis, Elvira de Eyto, Richard A. M de Jeu, B.V. VanderSat, Curtis L. DeGasperi, Doug Degenstein, Larry Di Girolamo, Martin T. Dokulil, Markus G. Donat, Wouter A. Dorigo, Imke Durre, Geoff S. Dutton, G. Duveiller, James W. Elkins, Vitali E. Fioletov, Johannes Flemming, Michael J. Foster, Richard A. Frey, Stacey M. Frith, Lucien Froidevaux, J. Garforth, S. K. Gupta, Leopold Haimberger, Brad D. Hall, Ian Harris, Andrew K Heidinger, D. L. Hemming, Shu-peng (Ben) Ho, Daan Hubert, Dale F. Hurst, I. Hüser, Antje Inness, K. Isaksen, Viju John, Philip D. Jones, J. W. Kaiser, S. Kelly, S. Khaykin, R. Kidd, Hyungiun Kim, Z. Kipling, B. M. Kraemer, D. P. Kratz, R. S. La Fuente, Xin Lan, Kathleen O. Lantz, T. Leblanc, Bailing Li, Norman G Loeb, Craig S. Long, Diego Loyola, Wlodzimierz Marszelewski, B. Martens, Linda May, Michael Mayer, M. F. McCabe, Tim R. McVicar, Carl A. Mears, W. Paul Menzel, Christopher J. Merchant, Ben R. Miller, Diego G. Miralles, Stephen A. Montzka, Colin Morice, Jens Mühle, R. Myneni, Julien P. Nicolas, Jeannette Noetzli, Tim J. Osborn, T. Park, A. Pasik, Andrew M. Paterson, Mauri S. Pelto, S. Perkins-Kirkpatrick, G. Pétron, C. Phillips, Bernard Pinty, S. Po-Chedley, L. Polvani, W. Preimesberger, M. Pulkkanen, W. J. Randel, Samuel Rémy, L. Ricciardulli, A. D. Richardson, L. Rieger, David A. Robinson, Matthew Rodell, Karen H. Rosenlof, Chris Roth, A. Rozanov, James A. Rusak, O. Rusanovskaya, T. Rutishäuser, Ahira Sánchez-Lugo, P. Sawaengphokhai, T. Scanlon, Verena Schenzinger, S. Geoffey Schladow, R. W Schlegel, Eawag Schmid, Martin, H. B. Selkirk, S. Sharma, Lei Shi, S. V. Shimaraeva, E. A. Silow, Adrian J. Simmons, C. A. Smith, Sharon L Smith, B. J. Soden, Viktoria Sofieva, T. H. Sparks, Paul W. Stackhouse Jr., Wolfgang Steinbrecht, Dimitri A. Streletskiy, G. Taha, Hagen Telg, S. J. Thackeray, M. A. Timofeyev, Kleareti Tourpali, Mari R. Tye, Ronald J. van der A, Robin, VanderSat B.V. van der Schalie, Gerard van der SchrierW. Paul, Guido R. van der Werf, Piet Verburg, Jean-Paul Vernier, Holger Vömel, Russell S. Vose, Ray Wang, Shohei G. Watanabe, Mark Weber, Gesa A. Weyhenmeyer, David Wiese, Anne C. Wilber, Jeanette D. Wild, Takmeng Wong, R. Iestyn Woolway, Xungang Yin, Lin Zhao, Guanguo Zhao, Xinjia Zhou, Jerry R. Ziemke, and Markus Ziese
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