Articles | Volume 27, issue 4
https://doi.org/10.5194/npg-27-489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-27-489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Oct 2020
Research article |
| 28 Oct 2020
| 28 Oct 2020
A method for predicting the uncompleted climate transition process
Pengcheng Yan
Institute of Arid Meteorology, China Meteorological Administration, Lanzhou, China
China Meteorological Administration Training Center, Beijing, China
Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou, China
Key Laboratory of Arid Climatic Change and Reducing Disaster, China Meteorological Administration, Lanzhou, China
Guolin Feng
Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China
Wei Hou
CORRESPONDING AUTHOR
Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China
Ping Yang
China Meteorological Administration Training Center, Beijing, China
Related authors
No articles found.
Pengcheng Yan, Wei Hou, and Guolin Feng
Nonlin. Processes Geophys., 23, 115–126, https://doi.org/10.5194/npg-23-115-2016, https://doi.org/10.5194/npg-23-115-2016, 2016
Short summary
Short summary
In the previous work (Yan et al., 2015, NPG), we proposed a novel method to detect the transition process of climate change and exposed a new understanding of climate change. In this work, by using this method, we studied several climate changes of the sea surface temperature over the past century. The result shows that the system is bi-stable, and the persist time of transition process is shortened. Besides, a quantitative relation among the transition process parameters is obtained and verified.
P. C. Yan, G. L. Feng, and W. Hou
Nonlin. Processes Geophys., 22, 249–258, https://doi.org/10.5194/npg-22-249-2015, https://doi.org/10.5194/npg-22-249-2015, 2015
Short summary
Short summary
A novel method is created to detect the process of the abrupt change, which has not been mentioned yet in traditional research. By building an ideal time series with a transition process, the results show that the process could be detected clearly. When applied to a climate index, this method detects five processes, and all of them have reappeared via the “start-end states” phase diagram. Additionally, it is detectable that the persist time of the process is related to global warming.
Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset
Applying prior correlations for ensemble-based spatial localization
A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter
Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Inferring the instability of a dynamical system from the skill of data assimilation exercises
Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz 96 system
Improving the potential accuracy and usability of EURO-CORDEX estimates of future rainfall climate using frequentist model averaging
Ensemble Riemannian data assimilation over the Wasserstein space
An early warning sign of critical transition in the Antarctic ice sheet – a data-driven tool for a spatiotemporal tipping point
Behavior of the iterative ensemble-based variational method in nonlinear problems
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst
Correcting for model changes in statistical postprocessing – an approach based on response theory
Brief communication: Residence time of energy in the atmosphere
Seasonal statistical–dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing and its evaluation
Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems
Order of operation for multi-stage post-processing of ensemble wind forecast trajectories
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47, https://doi.org/10.5194/npg-30-37-2023, https://doi.org/10.5194/npg-30-37-2023, 2023
Short summary
Short summary
Older observations of the Earth system sometimes lack information about the time they were taken, posing problems for analyses of past climate. To begin to ameliorate this problem, we propose new methods of varying complexity, including methods to estimate the distribution of the offsets between true and reported observation times. The most successful method accounts for the nonlinearity in the system, but even the less expensive ones can improve data assimilation in the presence of time error.
Chu-Chun Chang and Eugenia Kalnay
Nonlin. Processes Geophys., 29, 317–327, https://doi.org/10.5194/npg-29-317-2022, https://doi.org/10.5194/npg-29-317-2022, 2022
Short summary
Short summary
This study introduces a new approach for enhancing the ensemble data assimilation (DA), a technique that combines observations and forecasts to improve numerical weather predictions. Our method uses the prescribed correlations to suppress spurious errors, improving the accuracy of DA. The experiments on the simplified atmosphere model show that our method has comparable performance to the traditional method but is superior in the early stage and is more computationally efficient.
Andrey A. Popov, Amit N. Subrahmanya, and Adrian Sandu
Nonlin. Processes Geophys., 29, 241–253, https://doi.org/10.5194/npg-29-241-2022, https://doi.org/10.5194/npg-29-241-2022, 2022
Short summary
Short summary
Numerical weather prediction requires the melding of both computational model and data obtained from sensors such as satellites. We focus on one algorithm to accomplish this. We aim to aid its use by additionally supplying it with data obtained from separate models that describe the average behavior of the computational model at any given time. We show that our approach outperforms the standard approaches to this problem.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022, https://doi.org/10.5194/npg-29-77-2022, 2022
Short summary
Short summary
The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021, https://doi.org/10.5194/npg-28-633-2021, 2021
Short summary
Short summary
Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys., 28, 565–583, https://doi.org/10.5194/npg-28-565-2021, https://doi.org/10.5194/npg-28-565-2021, 2021
Short summary
Short summary
In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.
Stephen Jewson, Giuliana Barbato, Paola Mercogliano, Jaroslav Mysiak, and Maximiliano Sassi
Nonlin. Processes Geophys., 28, 329–346, https://doi.org/10.5194/npg-28-329-2021, https://doi.org/10.5194/npg-28-329-2021, 2021
Short summary
Short summary
Climate model simulations are uncertain. In some cases this makes it difficult to know how to use them. Significance testing is often used to deal with this issue but has various shortcomings. We describe two alternative ways to manage uncertainty in climate model simulations that avoid these shortcomings. We test them on simulations of future rainfall over Europe and show they produce more accurate projections than either using unadjusted climate model output or statistical testing.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. van Leeuwen, Dongmian Zou, and Gilad Lerman
Nonlin. Processes Geophys., 28, 295–309, https://doi.org/10.5194/npg-28-295-2021, https://doi.org/10.5194/npg-28-295-2021, 2021
Short summary
Short summary
Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.
Abd AlRahman AlMomani and Erik Bollt
Nonlin. Processes Geophys., 28, 153–166, https://doi.org/10.5194/npg-28-153-2021, https://doi.org/10.5194/npg-28-153-2021, 2021
Short summary
Short summary
This paper introduces a tool for data-driven discovery of early warning signs of critical transitions in ice shelves from remote sensing data. Our directed spectral clustering method considers an asymmetric affinity matrix along with the associated directed graph Laplacian. We applied our approach to reprocessing the ice velocity data and remote sensing satellite images of the Larsen C ice shelf.
Shin'ya Nakano
Nonlin. Processes Geophys., 28, 93–109, https://doi.org/10.5194/npg-28-93-2021, https://doi.org/10.5194/npg-28-93-2021, 2021
Short summary
Short summary
The ensemble-based variational method is a method for solving nonlinear data assimilation problems by using an ensemble of multiple simulation results. Although this method is derived based on a linear approximation, highly uncertain problems, in which system nonlinearity is significant, can also be solved by applying this method iteratively. This paper reformulated this iterative algorithm to analyze its behavior in high-dimensional nonlinear problems and discuss the convergence.
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22, https://doi.org/10.5194/npg-28-1-2021, https://doi.org/10.5194/npg-28-1-2021, 2021
Short summary
Short summary
Numerical weather prediction involves numerically solving the mathematical equations, which describe the geophysical flow, by transforming them so that they can be computed. Through this transformation, it appears that the equations actually solved by the machine are then a modified version of the original equations, introducing an error that contributes to the model error. This work helps to characterize the covariance of the model error that is due to this modification of the equations.
Reinhold Hess
Nonlin. Processes Geophys., 27, 473–487, https://doi.org/10.5194/npg-27-473-2020, https://doi.org/10.5194/npg-27-473-2020, 2020
Short summary
Short summary
Forecasts of ensemble systems are statistically aligned to synoptic observations at DWD in order to provide support for warning decision management. Motivation and design consequences for extreme and rare meteorological events are presented. Especially for probabilities of severe wind gusts global logistic parameterisations are developed that generate robust statistical forecasts for extreme events, while local characteristics are preserved.
Jonathan Demaeyer and Stéphane Vannitsem
Nonlin. Processes Geophys., 27, 307–327, https://doi.org/10.5194/npg-27-307-2020, https://doi.org/10.5194/npg-27-307-2020, 2020
Short summary
Short summary
Postprocessing schemes used to correct weather forecasts are no longer efficient when the model generating the forecasts changes. An approach based on response theory to take the change into account without having to recompute the parameters based on past forecasts is presented. It is tested on an analytical model and a simple model of atmospheric variability. We show that this approach is effective and discuss its potential application for an operational environment.
Carlos Osácar, Manuel Membrado, and Amalio Fernández-Pacheco
Nonlin. Processes Geophys., 27, 235–237, https://doi.org/10.5194/npg-27-235-2020, https://doi.org/10.5194/npg-27-235-2020, 2020
Short summary
Short summary
We deduce that after a global thermal perturbation, the Earth's
atmosphere would need about a couple of months to come back to equilibrium.
André Düsterhus
Nonlin. Processes Geophys., 27, 121–131, https://doi.org/10.5194/npg-27-121-2020, https://doi.org/10.5194/npg-27-121-2020, 2020
Short summary
Short summary
Seasonal prediction of the of the North Atlantic Oscillation (NAO) has been improved in recent years by improving dynamical models and ensemble predictions. One step therein was the so-called sub-sampling, which combines statistical and dynamical predictions. This study generalises this approach and makes it much more accessible. Furthermore, it presents a new verification approach for such predictions.
Courtney Quinn, Terence J. O'Kane, and Vassili Kitsios
Nonlin. Processes Geophys., 27, 51–74, https://doi.org/10.5194/npg-27-51-2020, https://doi.org/10.5194/npg-27-51-2020, 2020
Short summary
Short summary
This study presents a novel method for reduced-rank data assimilation of multiscale highly nonlinear systems. Time-varying dynamical properties are used to determine the rank and projection of the system onto a reduced subspace. The variable reduced-rank method is shown to succeed over other fixed-rank methods. This work provides implications for performing strongly coupled data assimilation with a limited number of ensemble members on high-dimensional coupled climate models.
Nina Schuhen
Nonlin. Processes Geophys., 27, 35–49, https://doi.org/10.5194/npg-27-35-2020, https://doi.org/10.5194/npg-27-35-2020, 2020
Short summary
Short summary
We present a new way to adaptively improve weather forecasts by incorporating last-minute observation information. The recently measured error, together with a statistical model, gives us an indication of the expected future error of wind speed forecasts, which are then adjusted accordingly. This new technique can be especially beneficial for customers in the wind energy industry, where it is important to have reliable short-term forecasts, as well as providers of extreme weather warnings.
Cited articles
Alexander, R., Reinhard, C., and Andrey, G.: Multistability and critical thresholds of
the Greenland ice sheet, Nat. Clim. Change, 2, 429–432, 2012.
Amaya, D. J., Siler, N., Xie, S. P., and Arthur, J. M.: The interplay of internal and
forced modes of Hadley Cell expansion: lessons from the global warming hiatus, Clim. Dynam., 51,
305–319, https://doi.org/10.1007/s00382-017-3921-5, 2018.
Barnett, T. P., Pierce, D. W., Latif M., and Dietmar, D.: Interdecadal interactions
between the tropics and midlatitudes in the Pacific basin, Geophys. Res. Lett., 26, 615–618,
1999.
Benzi, R., Parisi, G., Sutera, A., and Vulpiani, A.: Stochastic resonance in climatic
change, Tellus, 34, 10–16, 1982.
Cabezas, H. and Fath, B. D.: Towards a theory of sustainable systems, Fluid Phase
Equilibria, 3, 194–197, https://doi.org/10.1016/S0378-3812(01)00677-X, 2002.
Charney, J. G. and Devore, J. G.: Multiple flow equilibria in the atmosphere and
blocking, J. Atmos. Sci., 36, 1205–1216, 1979.
Goldblatt, C., Lenton, T. M., and Watson, A. J.: Bistability of atmospheric oxygen and
the Great Oxidation, Nature, 443, 683–686, https://doi.org/10.1038/nature05169, 2006.
Goossens, C. and Berger, A.: Annual and Seasonal Climatic Variations over the Northern
Hemisphere and Europe during the Last Century, Ann. Geophys., 4, 385–400,
https://doi.org/10.1016/0040-1951(86)90317-3, 1986.
Huang, J. Y.: Meteorological Statistical Analysis and Prediction, China Meteorological
Press, Beijing, 28–30, 1990.
Kosaka, Y. and Xie, S. P.: Recent global-warming hiatus tied to equatorial Pacific
surface cooling, Nature, 501, 403–407, https://doi.org/10.1038/nature12534, 2013.
Lenton, T. M.: Arctic Climate Tipping Points, Ambio, 41, 10–22, 2012.
Li, J. P., Chou, J. F., and Shi, J. E.: Complete detection and types of abrupt climatic
change, J. Beijing Meteorol. College, 1, 7–12, 1996.
Livina, V. N., Ditlevsen, P. D., and Lenton, T. M.: An independent test of methods of
detecting system states and bifurcations in time-series data, Phys. A-Stat. Mech. Appl., 391,
485–496, 2012.
Lorenz, E. N.: Deterministic nonperiodoc flow, J. Atmos. Sci., 20, 130–141,
https://doi.org/10.1175/1520-0469(1963)020<0130:Dnf>2.0.Co;2, 1963.
Lorenz, E. N.: Nondeterministic theories of climatic change, Quaternary Res., 6,
495–506, https://doi.org/10.1016/0033-5894(76)90022-3, 1976.
Lu, C. H., Guan, Z. Y., Li, Y. H., and Bai, Y. Y.: Interdecadal linkages between
Pacific decadal oscillation and interhemispheric oscillation and their possible connections with
East Asian Monsoon, Chin. J. Geophys., 56, 1084–1094, https://doi.org/10.1002/cjg2.20012, 2013.
Mantua, N. J., Hare, S., Zhang, Y., John, W., and Robert, F.: A Pacific Interdecadal
Climate Oscillation with Impacts on Salmon Production PDO, B. Am. Meteorol. Soc., 78,
1069–1079, https://doi.org/10.1175/1520-0477(1997)078<1069:Apicow>2.0.Co;2, 1997.
May, R. M.: Simple mathematical models with very complicated dynamics, Nature, 261,
459–467, https://doi.org/10.1038/261459a0, 1976.
Mudelsee, M.: Ramp function regression: a tool for quantifying climate Transitions,
Comput. Geosci., 26, 293–307, https://doi.org/10.1016/s0098-3004(99)00141-7, 2000.
Newman, M., Alexander, M. A., Ault, T. R., and Cobb, K. M.: The Pacific Decadal
Oscillation, Revisited, J. Climate, 29, 4399–4427, https://doi.org/10.1175/Jcli-D-15-0508.1, 2016.
Nozaki K.: Abrupt change in primary productivity in a littoral zone of Lake Biwa with
the development of a filamentous green-algal community, Freshwater Biol., 46, 587–602, 2001.
Osterkamp, S., Kraft, D., and Schirmer, M.: Climate change and the ecology of the Weser
estuary region: Assessing the impact of an abrupt change in climate, Clim. Res., 18, 97–104,
2001.
Overpeck, J. T. and Cole, J. E.: Abrupt change in earth's climate system,
Annu. Rev. Environ. Resour., 31, 1–31, https://doi.org/10.1146/annurev.energy.30.050504.144308, 2006.
Pierini, S.: Stochastic tipping points in climate dynamics, Phys. Rev. E, 85, 027101,
https://doi.org/10.1103/PhysRevE.85.027101, 2012.
Sherman, D. G., Hart, R. G., and Easton, J. D.: Abrupt change in head position and
cerebral infarction, Stroke, 12, 2, https://doi.org/10.1161/01.Str.12.1.2, 1981.
Shi, W. J., Tao, F. L., Liu, J. Y., Xu, X. L., Kuang, W. H., Dong, J. W., and Shi,
X. L.: Has climate change driven spatio-temporal changes of cropland in northern China since the
1970s?, Clim. Change, 124, 163–177, https://doi.org/10.1007/s10584-014-1088-1, 2014.
Thom, R.: Stability Structural and Morphogenesis, Sichuan Education Press, Sichuan,
1972.
Trenberth, K. E. and Hurrell, J. W.: Decadal atmosphere-ocean variations in the
Pacific, Clim. Dynam., 9, 303–319, https://doi.org/10.1007/Bf00204745, 1994.
Wei, F. Y.: Modern Climatic Statistical Diagnosis and Forecasting Technology, China
Meteorological Press, Beijing, 1999.
Yang, P., Xiao, Z. N., Yang, J., andc Liu, H.: Characteristics of clustering extreme
drought events in China during 1961–2010, Acta Meteorol. Sin., 27, 186–198, 2013a.
Yang, P., Ren, G. Y., and Liu, W.: Spatial and temporal characteristics of Beijing
urban heat island intensity, J. Appl. Meteorol. Climatol., 52, 1803–1816, 2013b.
Yang, P., Ren, G. Y., and Yan, P. C.: Evidence for a strong association of
short-duration intense rainfall with urbanization in the Beijing urban area, J. Climate, 30,
5851–5870, 2017.
Yan, P. C., Feng, G. L., Hou, W., and Wu, H.: Statistical characteristics on decadal
abrupt change process of time sequence in 500 hPa temperature field, Chin. J. Atmos. Sci.,
38, 861–873, 2014.
Yan, P. C., Feng, G. L., and Hou, W.: A novel method for analyzing the process of
abrupt climate change, Nonlin. Processes Geophys., 22, 249–258, https://doi.org/10.5194/npg-22-249-2015,
2015.
Yan, P. C., Hou, W., and Feng, G.: Transition process of abrupt climate change based on
global sea surface temperature over the past century, Nonlin. Processes Geophys., 23, 115–126,
https://doi.org/10.5194/npg-23-115-2016, 2016.
Yang, X. Q., Zhu, Y. M., Xie, Q., and Ren, X. J.: Advances in studies of Pacific
Decadal Oscillation, Chin. J. Atmos. Sci., 28, 979–992, 2004.
Zhang, Y. J., Wallace, M., and Battisti, D. S.: Enso-like interdecadal variability:
1900–93, J. Climate, 10, 1004–1020,
https://doi.org/10.1175/1520-0442(1997)010<1004:Eliv>2.0.Co;2, 1997.
Short summary
A system transiting from one stable state to another has to experience a period. Can we predict the end moment (state) if the process has not been completed? This paper presents a method to solve this problem. This method is based on the quantitative relationship among the parameters, which is used to describe the transition process of the abrupt change. By using the historical data, we extract some parameters for predicting the uncompleted climate transition process.
A system transiting from one stable state to another has to experience a period. Can we predict...
